The Acute STroke Registry and Analysis of Lausanne (ASTRAL): design and baseline analysis of an ischemic stroke registry including acute multimodal imaging.

BACKGROUND AND PURPOSE: Stroke registries are valuable tools for obtaining information about stroke epidemiology and management. The Acute STroke Registry and Analysis of Lausanne (ASTRAL) prospectively collects epidemiological, clinical, laboratory and multimodal brain imaging data of acute ischemic stroke patients in the Centre Hospitalier Universitaire Vaudois (CHUV). Here, we provide design and methods used to create ASTRAL and present baseline data of our patients (2003 to 2008). METHODS: All consecutive patients admitted to CHUV between January 1, 2003 and December 31, 2008 with acute ischemic stroke within 24 hours of symptom onset were included in ASTRAL. Patients arriving beyond 24 hours, with transient ischemic attack, intracerebral hemorrhage, subarachnoidal hemorrhage, or cerebral sinus venous thrombosis, were excluded. Recurrent ischemic strokes were registered as new events. RESULTS: Between 2003 and 2008, 1633 patients and 1742 events were registered in ASTRAL. There was a preponderance of males, even in the elderly. Cardioembolic stroke was the most frequent type of stroke. Most strokes were of minor severity (National Institute of Health Stroke Scale [NIHSS] score ≤ 4 in 40.8% of patients). Cardioembolic stroke and dissections presented with the most severe clinical picture. There was a significant number of patients with unknown onset stroke, including wake-up stroke (n=568, 33.1%). Median time from last-well time to hospital arrival was 142 minutes for known onset and 759 minutes for unknown-onset stroke. The rate of intravenous or intraarterial thrombolysis between 2003 and 2008 increased from 10.8% to 20.8% in patients admitted within 24 hours of last-well time. Acute brain imaging was performed in 1695 patients (97.3%) within 24 hours. In 1358 patients (78%) who underwent acute computed tomography angiography, 717 patients (52.8%) had significant abnormalities. Of the 1068 supratentorial stroke patients who underwent acute perfusion computed tomography (61.3%), focal hypoperfusion was demonstrated in 786 patients (73.6%). CONCLUSIONS: This hospital-based prospective registry of consecutive acute ischemic strokes incorporates demographic, clinical, metabolic, acute perfusion, and arterial imaging. It is characterized by a high proportion of minor and unknown-onset strokes, short onset-to-admission time for known-onset patients, rapidly increasing thrombolysis rates, and significant vascular and perfusion imaging abnormalities in the majority of patients.

The yield of high-density electric source imaging in focal epilepsy

Background: EEG is the cornerstone of epilepsy diagnostics and mandatory to determine the underlying epilepsy syndrome (e.g. focal vs idiopathic generalized). However, its potential as imaging tool is still underrecognized. In the present study, we aim to determine the prerequisites of maximal benefit of electric source imaging (ESI) to localize the irritative zone in patients with focal epilepsy. Methods: 150 patients suffering from focal epilepsy and with minimum 1 year post-operative follow-up were studied prospectively by reviewers blinded to the underlying diagnosis and outcome. We evaluated the influence of two important factors on sensitivity and specificity of ESI: the number of electrodes (low resolution, LR-ESI: \30 vs. high resolution, HR-ESI: 128-256 electrodes), and the use of individual MRI (i-MRI) vs. template MRI (t-MRI) as head model.Results: ESI had a sensitivity of 85% and a specificity of 87% when HR-ESI with i-MRI was used. Using LR-ESI, sensitivity decreased to 68%, or even 57% when only t-MRI was available. The sensitivity of HR-ESI/i-MRI compared favorably with those of MRI (76%), PET (69%) and ictal/interictal SPECT (64%).Interpretation: This study on a large patient group shows excellent sensitivity and specificity of ESI if 128 EEG channels or more are used for ESI and if the results are co-registered to the patient's individual MRI. Localization precision is as high as or even higher than established brain imaging techniques, providing excellent costeffectiveness in epilepsy evaluation. HR-ESI appears to be a valuable additional imaging tool, given that larger electrode arrays are easily and rapidly applied with modern EEG equipment and that structural MRI is nearly always available for these patients.

Impact of brain aging and neurodegeneration on cognition: evidence from MRI.

PURPOSE OF REVIEW: We present an overview of recent concepts in mechanisms underlying cognitive decline associated with brain aging and neurodegeneration from the perspective of MRI. RECENT FINDINGS: Recent findings challenge the established link between neuroimaging biomarkers of neurodegeneration and age-related or disease-related cognitive decline. Amyloid burden, white matter hyperintensities and local patterns of brain atrophy seem to have differential impact on cognition, particularly on episodic and working memory - the most vulnerable domains in 'normal aging' and Alzheimer's disease. Studies suggesting that imaging biomarkers of neurodegeneration are independent of amyloid-β give rise to new hypothesis regarding the pathological cascade in Alzheimer's disease. Findings in patients with autosomal-dominant Alzheimer's disease confirm the notion of differential temporal trajectory of amyloid deposition and brain atrophy to add another layer of complexity on the basic mechanisms of cognitive aging and neurodegeneration. Finally, the concept of cognitive reserve in 'supernormal aging' is questioned by evidence for the preservation of neurochemical, structural and functional brain integrity in old age rather than recruitment of 'reserves' for maintaining cognitive abilities. SUMMARY: Recent advances in clinical neuroscience, brain imaging and genetics challenge pathophysiological hypothesis of neurodegeneration and cognitive aging dominating the field in the last decade and call for reconsidering the choice of therapeutic window for early intervention.

Trilateral retinoblastoma: neuroimaging characteristics and value of routine brain screening on admission.

Trilateral retinoblastoma (TRb) is a rare disease associating intraocular retinoblastoma with intracranial primitive neuroectodermal tumor. Treatment is difficult and prognosis is poor. This multicenter study evaluates clinical findings and MR imaging characteristics of associated intracranial tumors in Rb patients. Clinical data of 17 patients (16 TRb and 1 quadrilateral Rb patients) included time intervals between Rb and TRb diagnosis and presence of baseline brain-imaging (BBI). Two reviewers reviewed all images individually and one reviewer per center evaluated their images. Consensus was reached during a joint scoring session. Studies were reviewed for tumor location, size and imaging characteristics (signal intensity (SI) on T1- and T2-weighted images, enhancement pattern and cystic appearance). Of 18 intracranial tumors, 78 % were located in the pineal gland and 22 % suprasellar. All tumors showed well-defined borders with mostly heterogenous enhancement (72 %) and isointense SI on T1- (78 %) and T2-weighted images (72 %) compared to gray matter. The majority of pineal TRbs showed a cystic component (57 %). TRb detected synchronously with the intraocular tumors on BBI (n = 7) were significantly smaller (P = 0.02), and mainly asymptomatic than TRb detected later on (n = 10). Overall, 5-year-survival of TRb patients detected on BBI was 67 % (95 % CI 29-100 %) compared to 11 % (95 % CI 0-32 %) for the group with delayed diagnosis. TRb mainly develops in the pineal gland and frequently presents with a cystic appearance that could be misinterpreted as benign pineal cysts. Routine BBI in all newly diagnosed Rb patients can detect TRb at a subclinical stage.

Multimodal MRI analysis of structural and functional networks in the human brain : application to two clinical studies : driving abilities under THC intoxication and early white matter changes in FXTAS

Les approches multimodales dans l'imagerie cérébrale non invasive sont de plus en plus considérées comme un outil indispensable pour la compréhension des différents aspects de la structure et de la fonction cérébrale. Grâce aux progrès des techniques d'acquisition des images de Resonance Magnetique et aux nouveaux outils pour le traitement des données, il est désormais possible de mesurer plusieurs paramètres sensibles aux différentes caractéristiques des tissues cérébraux. Ces progrès permettent, par exemple, d'étudier les substrats anatomiques qui sont à la base des processus cognitifs ou de discerner au niveau purement structurel les phénomènes dégénératifs et développementaux. Cette thèse met en évidence l'importance de l'utilisation d'une approche multimodale pour étudier les différents aspects de la dynamique cérébrale grâce à l'application de cette approche à deux études cliniques: l'évaluation structurelle et fonctionnelle des effets aigus du cannabis fumé chez des consommateurs réguliers et occasionnels, et l'évaluation de l'intégrité de la substance grise et blanche chez des jeunes porteurs de la prémutations du gène FMR1 à risque de développer le FXTAS (Fragile-X Tremor Ataxia Syndrome). Nous avons montré que chez les fumeurs occasionnels de cannabis, même à faible concentration du principal composant psychoactif (THC) dans le sang, la performance lors d'une tâche visuo-motrice est fortement diminuée, et qu'il y a des changements dans l'activité des trois réseaux cérébraux impliqués dans les processus cognitifs: le réseau de saillance, le réseau du contrôle exécutif, et le réseau actif par défaut (Default Mode). Les sujets ne sont pas en mesure de saisir les saillances dans l'environnement et de focaliser leur attention sur la tâche. L'augmentation de la réponse hémodynamique dans le cortex cingulaire antérieur suggère une augmentation de l'activité introspective. Une investigation des ef¬fets au niveau cérébral d'une exposition prolongée au cannabis, montre des changements persistants de la substance grise dans les régions associées à la mémoire et au traitement des émotions. Le niveau d'atrophie dans ces structures corrèle avec la consommation de cannabis au cours des trois mois précédant l'étude. Dans la deuxième étude, nous démontrons des altérations structurelles des décennies avant l'apparition du syndrome FXTAS chez des sujets jeunes, asymptomatiques, et porteurs de la prémutation du gène FMR1. Les modifications trouvées peuvent être liées à deux mécanismes différents. Les altérations dans le réseau moteur du cervelet et dans la fimbria de l'hippocampe, suggèrent un effet développemental de la prémutation. Elles incluent aussi une atrophie de la substance grise du lobule VI du cervelet et l'altération des propriétés tissulaires de la substance blanche des projections afférentes correspondantes aux pédoncules cérébelleux moyens. Les lésions diffuses de la substance blanche cérébrale peu¬vent être un marquer précoce du développement de la maladie, car elles sont liées à un phénomène dégénératif qui précède l'apparition des symptômes du FXTAS. - Multimodal brain imaging is becoming a leading tool for understanding different aspects of brain structure and function. Thanks to the advances in Magnetic Resonance imaging (MRI) acquisition schemes and data processing techniques, it is now possible to measure different parameters sensitive to different tissue characteristics. This allows for example to investigate anatomical substrates underlying cognitive processing, or to disentangle, at a pure structural level degeneration and developmental processes. This thesis highlights the importance of using a multimodal approach for investigating different aspects of brain dynamics by applying this approach to two clinical studies: functional and structural assessment of the acute effects of cannabis smoking in regular and occasional users, and grey and white matter assessment in young FMR1 premutation carriers at risk of developing FXTAS. We demonstrate that in occasional smokers cannabis smoking, even at low concentration of the main psychoactive component (THC) in the blood, strongly decrease subjects' performance on a visuo-motor tracking task, and globally alters the activity of the three brain networks involved in cognitive processing: the Salience, the Control Executive, and the Default Mode networks. Subjects are unable to capture saliences in the environment and to orient attention to the task; the increase in Hemodynamic Response in the Anterior Cingulate Cortex suggests an increase in self-oriented mental activity. A further investigation on long term exposure to cannabis, shows a persistent grey matter modification in brain regions associated with memory and affective processing. The degree of atrophy in these structures also correlates with the estimation of drug use in the three months prior the participation to the study. In the second study we demonstrate structural changes in young asymptomatic premutation carriers decades before the onset of FXTAS that might be related to two different mechanisms. Alteration of the cerebellar motor network and of the hippocampal fimbria/ fornix, may reflect a potential neurodevelopmental effect of the premutation. These include grey matter atrophy in lobule VI and modification of white matter tissue property in the corresponding afferent projections through the Middle Cerebellar Peduncles. Diffuse hemispheric white matter lesions that seem to appear closer to the onset of FXTAS and be related to a neurodegenerative phenomenon may mark the imminent onset of FXTAS.

Structural connectomics in brain diseases.

Imaging the connectome in vivo has become feasible through the integration of several rapidly developing fields of science and engineering, namely magnetic resonance imaging and in particular diffusion MRI on one side, image processing and network theory on the other side. This framework brings in vivo brain imaging closer to the real topology of the brain, contributing to narrow the existing gap between our understanding of brain structural organization on one side and of human behavior and cognition on the other side. Given the seminal technical progresses achieved in the last few years, it may be ready to tackle even greater challenges, namely exploring disease mechanisms. In this review we analyze the current situation from the technical and biological perspectives. First, we critically review the technical solutions proposed in the literature to perform clinical studies. We analyze for each step (i.e. MRI acquisition, network building and network statistical analysis) the advantages and potential limitations. In the second part we review the current literature available on a selected subset of diseases, namely, dementia, schizophrenia, multiple sclerosis and others, and try to extract for each disease the common findings and main differences between reports.

Diagnosing dementia and normal aging: clinical relevance of brain ratios and cognitive performance in a Brazilian sample

The main objective of the present study was to evaluate the diagnostic value (clinical application) of brain measures and cognitive function. Alzheimer and multiinfarct patients (N = 30) and normal subjects over the age of 50 (N = 40) were submitted to a medical, neurological and cognitive investigation. The cognitive tests applied were Mini-Mental, word span, digit span, logical memory, spatial recognition span, Boston naming test, praxis, and calculation tests. The brain ratios calculated were the ventricle-brain, bifrontal, bicaudate, third ventricle, and suprasellar cistern measures. These data were obtained from a brain computer tomography scan, and the cutoff values from receiver operating characteristic curves. We analyzed the diagnostic parameters provided by these ratios and compared them to those obtained by cognitive evaluation. The sensitivity and specificity of cognitive tests were higher than brain measures, although dementia patients presented higher ratios, showing poorer cognitive performances than normal individuals. Normal controls over the age of 70 presented higher measures than younger groups, but similar cognitive performance. We found diffuse losses of tissue from the central nervous system related to distribution of cerebrospinal fluid in dementia patients. The likelihood of case identification by functional impairment was higher than when changes of the structure of the central nervous system were used. Cognitive evaluation still seems to be the best method to screen individuals from the community, especially for developing countries, where the cost of brain imaging precludes its use for screening and initial assessment of dementia.

Optical coherence tomography imaging of the basal ganglia: feasibility and brief review

Optical coherence tomography (OCT) is a promising medical imaging technique that uses light to capture real-time cross-sectional images from biological tissues in micrometer resolution. Commercially available optical coherence tomography systems are employed in diverse applications, including art conservation and diagnostic medicine, notably in cardiology and ophthalmology. Application of this technology in the brain may enable distinction between white matter and gray matter, and obtainment of detailed images from within the encephalon. We present, herein, the in vivo implementation of OCT imaging in the rat brain striatum. For this, two male 60-day-old rats (Rattus norvegicus, Albinus variation, Wistar) were stereotactically implanted with guide cannulas into the striatum to guide a 2.7-French diameter high-definition OCT imaging catheter (Dragonfly™, St. Jude Medical, USA). Obtained images were compared with corresponding histologically stained sections to collect imaging samples. A brief analysis of OCT technology and its current applications is also reported, as well as intra-cerebral OCT feasibility on brain mapping during neurosurgical procedures.

Umami: a delicious flavor formed by convergence of taste and olfactory pathways in the human brain

Umami taste is produced by glutamate acting on a fifth taste system. However, glutamate presented alone as a taste stimulus is not highly pleasant, and does not act synergistically with other tastes (sweet, salt, bitter and sour). We show here that when glutamate is given in combination with a consonant, savory, odour (vegetable), the resulting flavor can be much more pleasant. Moreover, we showed using functional brain imaging with fMRI that the glutamate taste and savory odour combination produced much greater activation of the medial orbitofrontal cortex and pregenual cingulate cortex than the sum of the activations by the taste and olfactory components presented separately. Supralinear effects were much less (and significantly less) evident for sodium chloride and vegetable odour. Further, activations in these brain regions were correlated with the pleasantness and fullness of the flavor, and with the consonance of the taste and olfactory components. Supralinear effects of glutamate taste and savory odour were not found in the insular primary taste cortex. We thus propose that glutamate acts by the nonlinear effects it can produce when combined with a consonant odour in multimodal cortical taste-olfactory convergence regions. We propose the concept that umami can be thought of as a rich and delicious flavor that is produced by a combination of glutamate taste and a consonant savory odour. Glutamate is thus a flavor enhancer because of the way that it can combine supralinearly with consonant odours in cortical areas where the taste and olfactory pathways converge far beyond the receptors.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

Brain activity and medical diagnosis: an EEG study

Abstract Background Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis Results The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making. Conclusions PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.

Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.