Studying conscious and unconscious vision with functional Magnetic Resonance Imaging : the BOLD promise

Waking up from a dreamless sleep, I open my eyes, recognize my wife’s face and am filled with joy. In this thesis, I used functional Magnetic Resonance Imaging (fMRI) to gain insights into the mechanisms involved in this seemingly simple daily occurrence, which poses at least three great challenges to neuroscience: how does conscious experience arise from the activity of the brain? How does the brain process visual input to the point of recognizing individual faces? How does the brain store semantic knowledge about people that we know? To start tackling the first question, I studied the neural correlates of unconscious processing of invisible faces. I was unable to image significant activations related to the processing of completely invisible faces, despite existing reports in the literature. I thus moved on to the next question and studied how recognition of a familiar person was achieved in the brain; I focused on finding invariant representations of person identity – representations that would be activated any time we think of a familiar person, read their name, see their picture, hear them talk, etc. There again, I could not find significant evidence for such representations with fMRI, even in regions where they had previously been found with single unit recordings in human patients (the Jennifer Aniston neurons). Faced with these null outcomes, the scope of my investigations eventually turned back towards the technique that I had been using, fMRI, and the recently praised analytical tools that I had been trusting, Multivariate Pattern Analysis. After a mostly disappointing attempt at replicating a strong single unit finding of a categorical response to animals in the right human amygdala with fMRI, I put fMRI decoding to an ultimate test with a unique dataset acquired in the macaque monkey. There I showed a dissociation between the ability of fMRI to pick up face viewpoint information and its inability to pick up face identity information, which I mostly traced back to the poor clustering of identity selective units. Though fMRI decoding is a powerful new analytical tool, it does not rid fMRI of its inherent limitations as a hemodynamics-based measure.

Magnetic Resonance Imaging of concrete

http://www-civ.eng.cam.ac.uk/cjb/papers/cp88.pdf

Functional magnetic resonance imaging of impaired sensory prediction in schizophrenia.

IMPORTANCE: Forward models predict the sensory consequences of planned actions and permit discrimination of self- and non-self-elicited sensation; their impairment in schizophrenia is implied by an abnormality in behavioral force-matching and the flawed agency judgments characteristic of positive symptoms, including auditory hallucinations and delusions of control. OBJECTIVE: To assess attenuation of sensory processing by self-action in individuals with schizophrenia and its relation to current symptom severity. DESIGN, SETTING, AND PARTICIPANTS: Functional magnetic resonance imaging data were acquired while medicated individuals with schizophrenia (n = 19) and matched controls (n = 19) performed a factorially designed sensorimotor task in which the occurrence and relative timing of action and sensation were manipulated. The study took place at the neuroimaging research unit at the Institute of Cognitive Neuroscience, University College London, and the Maudsley Hospital. RESULTS: In controls, a region of secondary somatosensory cortex exhibited attenuated activation when sensation and action were synchronous compared with when the former occurred after an unexpected delay or alone. By contrast, reduced attenuation was observed in the schizophrenia group, suggesting that these individuals were unable to predict the sensory consequences of their own actions. Furthermore, failure to attenuate secondary somatosensory cortex processing was predicted by current hallucinatory severity. CONCLUSIONS AND RELEVANCE: Although comparably reduced attenuation has been reported in the verbal domain, this work implies that a more general physiologic deficit underlies positive symptoms of schizophrenia.

A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented.

Activation in mesolimbic and visuospatial neural circuits elicited by smoking cues: evidence from functional magnetic resonance imaging.

OBJECTIVE: The authors sought to increase understanding of the brain mechanisms involved in cigarette addiction by identifying neural substrates modulated by visual smoking cues in nicotine-deprived smokers. METHOD: Event-related functional magnetic resonance imaging (fMRI) was used to detect brain activation after exposure to smoking-related images in a group of nicotine-deprived smokers and a nonsmoking comparison group. Subjects viewed a pseudo-random sequence of smoking images, neutral nonsmoking images, and rare targets (photographs of animals). Subjects pressed a button whenever a rare target appeared. RESULTS: In smokers, the fMRI signal was greater after exposure to smoking-related images than after exposure to neutral images in mesolimbic dopamine reward circuits known to be activated by addictive drugs (right posterior amygdala, posterior hippocampus, ventral tegmental area, and medial thalamus) as well as in areas related to visuospatial attention (bilateral prefrontal and parietal cortex and right fusiform gyrus). In nonsmokers, no significant differences in fMRI signal following exposure to smoking-related and neutral images were detected. In most regions studied, both subject groups showed greater activation following presentation of rare target images than after exposure to neutral images. CONCLUSIONS: In nicotine-deprived smokers, both reward and attention circuits were activated by exposure to smoking-related images. Smoking cues are processed like rare targets in that they activate attentional regions. These cues are also processed like addictive drugs in that they activate mesolimbic reward regions.

Prostate Cancer: The Role of Multiparametric Magnetic Resonance Imaging

Multiparametric Magnetic Resonance Imaging has been increasingly used for detection, localization and staging of prostate cancer over the last years. It combines high-resolution T2 Weighted-Imaging and at least two functional techniques, which include Dynamic Contrast–Enhanced Magnetic Resonance Imaging, Diffusion-Weighted Imaging, and Magnetic Resonance Imaging Spectroscopy. Although the combined use of a pelvic phased-array and an Endorectal Coil is considered the state-of-the-art for Magnetic Resonance Imaging evaluation of prostate cancer, Endorectal Coil is only absolute mandatory for Magnetic Resonance Imaging Spectroscopy at 1.5 T. Sensitivity and specificity levels in cancer detection and localization have been improving with functional technique implementation, compared to T2 Weighted-Imaging alone. It has been particularly useful to evaluate patients with abnormal PSA and negative biopsy. Moreover, the information added by the functional techniques may correlate to cancer aggressiveness and therefore be useful to select patients for focal radiotherapy, prostate sparing surgery, focal ablative therapy and active surveillance. However, more studies are needed to compare the functional techniques and understand the advantages and disadvantages of each one. This article reviews the basic principles of prostatic mp-Magnetic Resonance Imaging, emphasizing its role on detection, staging and active surveillance of prostate cancer.

High-resolution magnetic resonance imaging quantitatively detects individual pancreatic islets.

OBJECTIVE-We studied whether manganese-enhanced high-field magnetic resonance (MR) imaging (MEHFMRI) could quantitatively detect individual islets in situ and in vivo and evaluate changes in a model of experimental diabetes.RESEARCH DESIGN AND METHODS-Whole pancreata from untreated (n = 3), MnCl(2) and glucose-injected mice (n = 6), and mice injected with either streptozotocin (STZ; n = 4) or citrate buffer (n = 4) were imaged ex vivo for unambiguous evaluation of islets. Exteriorized pancreata of MnCl(2) and glucose-injected mice (n = 6) were imaged in vivo to directly visualize the gland and minimize movements. In all cases, MR images were acquired in a 14.1 Testa scanner and correlated with the corresponding (immuno)histological sections.RESULTS-In ex vivo experiments, MEHFMRI distinguished different pancreatic tissues and evaluated the relative abundance of islets in the pancreata of normoglycemic mice. MEHFMRI also detected a significant decrease in the numerical and volume density of islets in STZ-injected mice. However, in the latter measurements the loss of beta-cells was undervalued under the conditions tested. The experiments on the externalized pancreata confirmed that MEHFMRI could visualize native individual islets in living, anesthetized mice.CONCLUSIONS-Data show that MEHFMRI quantitatively visualizes individual islets in the intact mouse pancreas, both ex vivo and in vivo. Diabetes 60:2853-2860, 2011

An introduction to model-independent diffusion magnetic resonance imaging.

ABSTRACT: q-Space-based techniques such as diffusion spectrum imaging, q-ball imaging, and their variations have been used extensively in research for their desired capability to delineate complex neuronal architectures such as multiple fiber crossings in each of the image voxels. The purpose of this article was to provide an introduction to the q-space formalism and the principles of basic q-space techniques together with the discussion on the advantages as well as challenges in translating these techniques into the clinical environment. A review of the currently used q-space-based protocols in clinical research is also provided.

Oxygenation-sensitive cardiovascular magnetic resonance imaging (OS-CMR) : potential confounding factors in use of OS-CMR

La résonance magnétique cardiovasculaire sensible à l'oxygénation (OS-CMR) est devenue une modalité d'imagerie diagnostique pour la surveillance de changements dans l'oxygénation du myocarde. Cette technique offre un grand potentiel en tant qu'outil diagnostic primaire pour les maladies cardiovasculaires, en particulier la détection non-invasive d'ischémie. Par contre, il existe plusieurs facteurs potentiellement confondants de cette technique, quelques-uns d'ordre méthodologique comme les paramètres de séquençage et d'autres de nature physiologiques qui sont peut compris. En raison des effets causés par le contenu tissulaire d'eau, l'état d'hydratation peut avoir un impact sur l'intensité du signal. Ceci est un des aspects physiologiques en particulier dont nous voulions quantifier l'effet confondant par la manipulation de l'état d'hydratation chez des humains et l'observation des changements de l'intensité du signal dans des images OS-CMR. Méthodes: In vitro: Du sang artériel et veineux de huit porcs a été utilisé pour évaluer la dilution en série du sang et son effet correspondant sur l'intensité du signal de la séquence OS. In vivo: Vingt-deux volontaires en santé ont subi OS-CMR. Les concentrations d'hémoglobine (Hb) ont été mesurées au niveau de base et immédiatement après une l'infusion cristalloïde rapide de 1000 mL de solution Lactate Ringer's (LRS). Les images OS-CMR ont été prises dans une vue mid-ventriculaire court axe. L'intensité du signal myocardique a été mesurée durant une rétention respiratoire volontaire maximale, suite à une période d'hyperventilation de 60 secondes. Les changements dans l'intensité du signal entre le début et la fin de la rétention de la respiration ont été exprimés relativement au niveau de base (% de changement). Résultats: L'infusion a résulté en une diminution significative de l'Hb mesurée (142.5±3.3 vs. 128.8±3.3 g/L; p<0.001), alors que l'IS a augmenté de 3.2±1.2% entre les images du niveau de base en normo- et hypervolémie (p<0.05). L'IS d'hyperventilation ainsi que les changements d'IS induits par l'apnée ont été attenués après hémodilution (p<0.05). L'évaluation quantitative T2* a démontré une corrélation négative entre le temps de T2* et la concentration d'hémoglobine (r=-0.46, p<0.005). Conclusions: Il existe plusieurs éléments confondants de la technique OS-CMR qui requièrent de l'attention et de l'optimisation pour une future implémentation clinique à grande échelle. Le statut d'hydratation en particulier pourrait être un élément confondant dans l'imagerie OS-CMR. L'hypervolémie mène à une augmentation en IS au niveau de base et atténue la réponse IS durant des manoeuvres de respiration vasoactives. Cette atténuation de l'intensité du signal devrait être tenue en compte et corrigée dans l'évaluation clinique d'images OS-CMR.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.