PET Molecular Imaging of Hypoxia in Ischemic Stroke: An Update.

Hypoxia, a condition of insufficient oxygen availability to support metabolism, occurs when the vascular supply is interrupted, as in stroke. The identification of the hypoxic and viable tissue in stroke as compared with irreversible lesions (necrosis) has relevant implications for the treatment of ischemic stroke. Traditionally, imaging by positron emission tomography (PET), using 15O-based radiotracers, allowed the measurement of perfusion and oxygen extraction in stroke, providing important insights in its pathophysiology. However, these multitracer evaluations are of limited applicability in clinical settings. More recently, specific tracers have been developed, which accumulate with an inverse relationship to oxygen concentration and thus allow visualizing the hypoxic tissue non invasively. These belong to two main groups: nitroimidazoles, and among these the 18F-Fluoroimidazole (18F-FMISO) is the most widely used, and the copper-based tracers, represented mainly by Cu-ATSM. While these tracers have been at first developed and tested in order to image hypoxia in tumors, they have also shown promising results in stroke models and preliminary clinical studies in patients with cardiovascular disorders, allowing the detection of hypoxic tissue and the prediction of the extent of subsequent ischemia and clinical outcome. These tracers have therefore the potential to select an appropriate subgroup of patients who could benefit from a hypoxia-directed treatment and provide prognosis relevant imaging. The molecular imaging of hypoxia made important progress over the last decade and has a potential for integration into the diagnostic and therapeutic workup of patients with ischemic stroke.

Transfer of ultrasmall iron oxide nanoparticles from human brain-derived endothelial cells to human glioblastoma cells.

Nanoparticles (NPs) are being used or explored for the development of biomedical applications in diagnosis and therapy, including imaging and drug delivery. Therefore, reliable tools are needed to study the behavior of NPs in biological environment, in particular the transport of NPs across biological barriers, including the blood-brain tumor barrier (BBTB), a challenging question. Previous studies have addressed the translocation of NPs of various compositions across cell layers, mostly using only one type of cells. Using a coculture model of the human BBTB, consisting in human cerebral endothelial cells preloaded with ultrasmall superparamagnetic iron oxide nanoparticles (USPIO NPs) and unloaded human glioblastoma cells grown on each side of newly developed ultrathin permeable silicon nitride supports as a model of the human BBTB, we demonstrate for the first time the transfer of USPIO NPs from human brain-derived endothelial cells to glioblastoma cells. The reduced thickness of the permeable mechanical support compares better than commercially available polymeric supports to the thickness of the basement membrane of the cerebral vascular system. These results are the first report supporting the possibility that USPIO NPs could be directly transferred from endothelial cells to glioblastoma cells across a BBTB. Thus, the use of such ultrathin porous supports provides a new in vitro approach to study the delivery of nanotherapeutics to brain cancers. Our results also suggest a novel possibility for nanoparticles to deliver therapeutics to the brain using endothelial to neural cells transfer.

Effects of MRI scan acceleration on brain volume measurement consistency.

PURPOSE: To evaluate the effects of recent advances in magnetic resonance imaging (MRI) radiofrequency (RF) coil and parallel imaging technology on brain volume measurement consistency. MATERIALS AND METHODS: In all, 103 whole-brain MRI volumes were acquired at a clinical 3T MRI, equipped with a 12- and 32-channel head coil, using the T1-weighted protocol as employed in the Alzheimer's Disease Neuroimaging Initiative study with parallel imaging accelerations ranging from 1 to 5. An experienced reader performed qualitative ratings of the images. For quantitative analysis, differences in composite width (CW, a measure of image similarity) and boundary shift integral (BSI, a measure of whole-brain atrophy) were calculated. RESULTS: Intra- and intersession comparisons of CW and BSI measures from scans with equal acceleration demonstrated excellent scan-rescan accuracy, even at the highest acceleration applied. Pairs-of-scans acquired with different accelerations exhibited poor scan-rescan consistency only when differences in the acceleration factor were maximized. A change in the coil hardware between compared scans was found to bias the BSI measure. CONCLUSION: The most important findings are that the accelerated acquisitions appear to be compatible with the assessment of high-quality quantitative information and that for highest scan-rescan accuracy in serial scans the acquisition protocol should be kept as consistent as possible over time. J. Magn. Reson. Imaging 2012;36:1234-1240. ©2012 Wiley Periodicals, Inc.

Neurophysiological origin of human brain asymmetry for speech and language.

The physiological basis of human cerebral asymmetry for language remains mysterious. We have used simultaneous physiological and anatomical measurements to investigate the issue. Concentrating on neural oscillatory activity in speech-specific frequency bands and exploring interactions between gestural (motor) and auditory-evoked activity, we find, in the absence of language-related processing, that left auditory, somatosensory, articulatory motor, and inferior parietal cortices show specific, lateralized, speech-related physiological properties. With the addition of ecologically valid audiovisual stimulation, activity in auditory cortex synchronizes with left-dominant input from the motor cortex at frequencies corresponding to syllabic, but not phonemic, speech rhythms. Our results support theories of language lateralization that posit a major role for intrinsic, hardwired perceptuomotor processing in syllabic parsing and are compatible both with the evolutionary view that speech arose from a combination of syllable-sized vocalizations and meaningful hand gestures and with developmental observations suggesting phonemic analysis is a developmentally acquired process.

The geometric structure of the brain fiber pathways.

The structure of the brain as a product of morphogenesis is difficult to reconcile with the observed complexity of cerebral connectivity. We therefore analyzed relationships of adjacency and crossing between cerebral fiber pathways in four nonhuman primate species and in humans by using diffusion magnetic resonance imaging. The cerebral fiber pathways formed a rectilinear three-dimensional grid continuous with the three principal axes of development. Cortico-cortical pathways formed parallel sheets of interwoven paths in the longitudinal and medio-lateral axes, in which major pathways were local condensations. Cross-species homology was strong and showed emergence of complex gyral connectivity by continuous elaboration of this grid structure. This architecture naturally supports functional spatio-temporal coherence, developmental path-finding, and incremental rewiring with correlated adaptation of structure and function in cerebral plasticity and evolution.

Anatomo-functional magnetic resonance imaging of the spinal cord and its application to the characterization of spinal lesions in cats

Les lésions de la moelle épinière ont un impact significatif sur la qualité de la vie car elles peuvent induire des déficits moteurs (paralysie) et sensoriels. Ces déficits évoluent dans le temps à mesure que le système nerveux central se réorganise, en impliquant des mécanismes physiologiques et neurochimiques encore mal connus. L'ampleur de ces déficits ainsi que le processus de réhabilitation dépendent fortement des voies anatomiques qui ont été altérées dans la moelle épinière. Il est donc crucial de pouvoir attester l'intégrité de la matière blanche après une lésion spinale et évaluer quantitativement l'état fonctionnel des neurones spinaux. Un grand intérêt de l'imagerie par résonance magnétique (IRM) est qu'elle permet d'imager de façon non invasive les propriétés fonctionnelles et anatomiques du système nerveux central. Le premier objectif de ce projet de thèse a été de développer l'IRM de diffusion afin d'évaluer l'intégrité des axones de la matière blanche après une lésion médullaire. Le deuxième objectif a été d'évaluer dans quelle mesure l'IRM fonctionnelle permet de mesurer l'activité des neurones de la moelle épinière. Bien que largement appliquées au cerveau, l'IRM de diffusion et l'IRM fonctionnelle de la moelle épinière sont plus problématiques. Les difficultés associées à l'IRM de la moelle épinière relèvent de sa fine géométrie (environ 1 cm de diamètre chez l'humain), de la présence de mouvements d'origine physiologique (cardiaques et respiratoires) et de la présence d'artefacts de susceptibilité magnétique induits par les inhomogénéités de champ, notamment au niveau des disques intervertébraux et des poumons. L'objectif principal de cette thèse a donc été de développer des méthodes permettant de contourner ces difficultés. Ce développement a notamment reposé sur l'optimisation des paramètres d'acquisition d'images anatomiques, d'images pondérées en diffusion et de données fonctionnelles chez le chat et chez l'humain sur un IRM à 3 Tesla. En outre, diverses stratégies ont été étudiées afin de corriger les distorsions d'images induites par les artefacts de susceptibilité magnétique, et une étude a été menée sur la sensibilité et la spécificité de l'IRM fonctionnelle de la moelle épinière. Les résultats de ces études démontrent la faisabilité d'acquérir des images pondérées en diffusion de haute qualité, et d'évaluer l'intégrité de voies spinales spécifiques après lésion complète et partielle. De plus, l'activité des neurones spinaux a pu être détectée par IRM fonctionnelle chez des chats anesthésiés. Bien qu'encourageants, ces résultats mettent en lumière la nécessité de développer davantage ces nouvelles techniques. L'existence d'un outil de neuroimagerie fiable et robuste, capable de confirmer les paramètres cliniques, permettrait d'améliorer le diagnostic et le pronostic chez les patients atteints de lésions médullaires. Un des enjeux majeurs serait de suivre et de valider l'effet de diverses stratégies thérapeutiques. De telles outils représentent un espoir immense pour nombre de personnes souffrant de traumatismes et de maladies neurodégénératives telles que les lésions de la moelle épinière, les tumeurs spinales, la sclérose en plaques et la sclérose latérale amyotrophique.

Increased expression of "peripheral-type" benzodiazepine receptors in human temporal lobe epilepsy: implications for PET imaging of hippocampal sclerosis.

Increased binding sites for "peripheral-type" benzodiazepine receptor (PTBR) ligands have been described in a wide range of neurological disorders including both human and experimental epilepsy. This study was undertaken to assess PTBR expression in relation to the presence of hippocampal sclerosis in human temporal lobe epilepsy (TLE). For this purpose, hippocampal CA1 subfields were dissected from surgical samples from patients with therapy-refractive TLE with (n = 5) or without (n = 2) hippocampal sclerosis and from age-matched nonepileptic postmortem controls (n = 5). PTBR expression was assessed by immunohistochemistry and reverse-transcription polymerase chain reaction. Receptor sites were evaluated using an in vitro binding assay and the selective PTBR ligand [3H]PK11195. Epileptic patients with hippocampal sclerosis showed increases in PTBR binding sites, immunoreactivity, and mRNA expression compared to both nonsclerotic TLE patients and postmortem nonepileptic controls. Induction of PTBR expression and binding sites were directly correlated with the presence of hippocampal sclerosis and the accompanying reactive gliosis.

Nature of crossmodal plasticity in the blind brain and interplay with sight restoration

Thèse réalisée en cotutelle avec l'Université catholique de Louvain.

Comparative costs and outcomes of traumatic brain injury from biking accidents with or without helmet use

Contexte: Évaluer les déterminants de maladies évitables et leurs coûts est nécessaire dans le contexte d’assurance maladie universelle. Le moment d’évaluer les impacts des traumatismes crâniocérébraux (TCC) survenus lors d’accidents de vélo est idéal vu la popularité récente du cyclisme au Québec. Objectifs: Comparer les caractéristiques démographiques et médicales, ainsi que les coûts sociétaux qu’engendrent les TCC de cyclistes portant ou non un casque. Méthodologie: Étude rétrospective de 128 cyclistes avec TCC admis à l’Hôpital Général de Montréal entre 2007 et 2011. Les variables indépendantes sont sociodémographiques, cliniques et le port du casque. Les variables dépendantes sont la durée de séjour, l’échelle GOS-E, l’échelle ISS, l’orientation au congé, les décès et les coûts à la société. Résultats: Le groupe portant un casque était plus vieux, plus éduqué, retraité et marié; au niveau médical, ils avaient des TCCs moins sévères à l’imagerie, des hospitalisations aux soins intensifs plus courtes et moins de neurochirurgies. Les coûts médians à la société pour les TCC isolés de cyclistes avec casque étaient significativement moindres. Conclusion: Dans cette étude, le port du casque semblait prévenir certaines complications des TCC et permettait de faire économiser de l’argent à l’état. Le port de casque est recommandé.

Where there's smoke, there's fire : the brain reactivity of chronic smokers to anti-smoking stimuli

Contexte: Plusieurs études ont démontré que les indices environnementaux associés à la cigarette peuvent provoquer des envies de consommer (« cravings ») chez les fumeurs, ce qui nuit aux efforts d’abandon de la substance et favorise le maintien du tabagisme. Un bon nombre d’études en imagerie cérébrale ont examiné les bases neurophysiologiques de cette caractéristique clinique. Le tabagisme se caractérise aussi par l’incapacité des représentations négatives de la consommation (méfaits médicaux et sociaux) d’influencer la consommation des fumeurs. Étonnamment toutefois, très peu de travaux de recherche se sont intéressés à examiner les bases neurophysiologiques de cette insouciance envers les méfaits de la cigarette chez les fumeurs. En utilisant l'imagerie cérébrale fonctionnelle, l'objectif de cette étude était: d’examiner la réponse neurophysiologique des fumeurs chroniques à des images qui illustrent les effets négatifs de la cigarette (campagne anti-tabac); d’examiner le caractère affectif de cette réactivité utilisant des conditions contrôles (c.-à-d., images aversives non-liées au tabac et appétitives liées au tabac); d'examiner la connectivité fonctionnelle durant cette tâche entre les systèmes affectifs et exécutifs (une interaction qui peut favoriser ou entraver l'impact des évènements aversifs). Méthodes: 30 fumeurs chroniques ont passé une session de neuroimagerie durant laquelle ils devaient regarder des images appétitives et aversives de cigarettes, des images aversives non-reliées au tabac et des images neutres. Résultats: Les images aversives liés au tabagisme suscitent une plus grande activation dans le cortex médial préfrontal, l'amygdale, le gyrus frontal inférieur et le cortex orbitofrontal latéral en comparaison avec les images neutres, mais une moins grande activation dans des structures médiaux / sous-corticales comparé aux images aversives non-reliés et images appétitives reliées aux tabac. L’activité du système exécutif présente une connectivité fonctionnelle négative avec le système affectif lorsque les images aversives sont liées au tabac, mais pas quand elles ne le sont pas. Conclusions: Le modèle d'activation du cerveau observé suggère qu’il y a un biais dans la réactivité des fumeurs chroniques lorsqu’ils observent des représentations négatives de la consommation du tabac. L’activité du système exécutif cérébral semble promouvoir chez les fumeurs une baisse d’activité dans des régions impliquées dans la genèse d’une réponse physiologique affective; il s’agit d’un mécanisme qui permettrait de réduire l’impact persuasif de ces représentations des méfaits de la cigarette sur la consommation des fumeurs.

Dopamine D1 and D2 Receptor Gene Expression and cGMP, IP3 and Ca2+ Regulation in the Brain Regions of Hypoxic Neonatal Rats: Role of Glucose, Oxygen and Epinephrine Supplementation

Department of Biotechnology, Cochin University of Science and Technology

“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.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.