Nosologic imaging of the brain: segmentation and classification using MRI and MRSI

A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumors. First, a registered brain atlas and a subject‐specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high‐resolution nosologic images, representing tumor heterogeneity and class probabilities, which may help clinicians in decision making. Copyright © 2008 John Wiley & Sons, Ltd.     

Choice of the feference for EEG mapping in the newborn: An initial comparison of common nose reference,average and source derivation

Summary The instantaneous amplitude of different EEG patterns found in a 36 week newborn baby were mapped by isopotential display, using 3 different references. We found that in certain cases, the nose reference recalculated average and source derivations give the same results. If the different EEG activities on the skull have too an high amplitude, and are not in phase opposition, the average reference differs from the zero potential of the common reference. In this case, the nose may be a better reference. However, it is possible that under other conditions high amplitude activity contaminates the nose, rendering the other references as better choices. From these preliminary results, we recommend the simultaneous display of maps obtained with the different references, and the comparison of the maps with the tracings for topographic studies in babies. It is absolutely necessary to record at least two polygraphic derivations to be able to recognize the sleep stages in which the analyses were performed. The eyes movement lead lets us to see if the EEG activity contaminates the face with multiple displays. It is then possible to decide which reference system is most appropriate.This study was supported by a grant from the University of Rouen, and realized with the help of the Rouen University Hospital. The authors wish to thank the Neonatal care unit (Professor C. Fessard) for the children, C. Berland for the recordings, G. Sainton and E. Dreano for their technical assistance, and J. Sanctot for her secretarial capabilities.     

Automatic reference selection for quantitative EEG interpretation: Identification of diffuse/localised activity and the active earlobe reference,iterative detection of the distribution of EEG rhythms

EEG (Electroencephalograph) interpretation is important for the diagnosis of neurological disorders. The proper adjustment of the montage can highlight the EEG rhythm of interest and avoid false interpretation. The aim of this study was to develop an automatic reference selection method to identify a suitable reference. The results may contribute to the accurate inspection of the distribution of EEG rhythms for quantitative EEG interpretation. The method includes two pre-judgements and one iterative detection module. The diffuse case is initially identified by pre-judgement 1 when intermittent rhythmic waveforms occur over large areas along the scalp. The earlobe reference or averaged reference is adopted for the diffuse case due to the effect of the earlobe reference depending on pre-judgement 2. An iterative detection algorithm is developed for the localised case when the signal is distributed in a small area of the brain. The suitable averaged reference is finally determined based on the detected focal and distributed electrodes. The presented technique was applied to the pathological EEG recordings of nine patients. One example of the diffuse case is introduced by illustrating the results of the pre-judgements. The diffusely intermittent rhythmic slow wave is identified. The effect of active earlobe reference is analysed. Two examples of the localised case are presented, indicating the results of the iterative detection module. The focal and distributed electrodes are detected automatically during the repeating algorithm. The identification of diffuse and localised activity was satisfactory compared with the visual inspection. The EEG rhythm of interest can be highlighted using a suitable selected reference. The implementation of an automatic reference selection method is helpful to detect the distribution of an EEG rhythm, which can improve the accuracy of EEG interpretation during both visual inspection and automatic interpretation.     

Automated tissue segmentation and blind recovery of (1)H MRS imaging spectral patterns of normal and diseased human brain

Constrained non-negative matrix factorization (cNMF) with iterative data selection is described and demonstrated as a data analysis method for fast and automatic recovery of biochemically meaningful and diagnostically specific spectral patterns of the human brain from (1)H MRS imaging ((1)H MRSI) data. To achieve this goal, cNMF decomposes in vivo multidimensional (1)H MRSI data into two non-negative matrices representing (a) the underlying tissue-specific spectral patterns and (b) the spatial distribution of the corresponding metabolite concentrations. Central to the proposed approach is automatic iterative data selection which uses prior knowledge about the spatial distribution of the spectra to remove voxels that are due to artifacts and undesired metabolites/tissues such as the strong lipid and water components. The automatic recovery of diagnostic spectral patterns is demonstrated for long-TE (1)H MRSI data on normal human brain, multiple sclerosis, and serial brain tumor. The results show the ability of cNMF with iterative data selection to automatically and simultaneously recover tissue-specific spectral patterns and achieve segmentation of normal and diseased human brain tissue, concomitant with simplification of information content. These features of cNMF, which permit rapid recovery, reduction and interpretation of the complex diagnostic information content of large multi-dimensional spectroscopic imaging data sets, have the potential to enhance the clinical utility of in vivo(1)H MRSI.     

Metabolic activity diffusion imaging (MADI): II. Noninvasive,high-resolution human brain mapping of sodium pump flux and cell metrics

We introduce a new ¹H₂O magnetic resonance approach: metabolic activity diffusion imaging (MADI). Numerical diffusion-weighted imaging decay simulations characterized by the mean cellular water efflux (unidirectional) rate constant (k_io), mean cell volume (V), and cell number density ( ρ ) are produced from Monte Carlo random walks in virtual stochastically sized/shaped cell ensembles. Because of active steady-state trans-membrane water cycling (AWC), k_io reflects the cytolemmal Na⁺, K⁺ATPase (NKA) homeostatic cellular metabolic rate (^cMR_NKA). A digital 3D “library” contains thousands of simulated single diffusion-encoded (SDE) decays. Library entries match well with disparate, animal, and human experimental SDE decays. The V and ρ values are consistent with estimates from pertinent in vitro cytometric and ex vivo histopathological literature: in vivo V and ρ values were previously unavailable. The library allows noniterative pixel-by-pixel experimental SDE decay library matchings that can be used to advantage. They yield proof-of-concept MADI parametric mappings of the awake, resting human brain. These reflect the tissue morphology seen in conventional MRI. While V is larger in gray matter (GM) than in white matter (WM), the reverse is true for ρ . Many brain structures have k_io values too large for current, invasive methods. For example, the median WM k_io is 22s⁻¹; likely reflecting mostly exchange within myelin. The k_io•V product map displays brain tissue ^cMR_NKA variation. The GM activity correlates, quantitatively and qualitatively, with the analogous resting-state brain ¹⁸FDG-PET tissue glucose consumption rate (^tMR_glucose) map; but noninvasively, with higher spatial resolution, and no pharmacokinetic requirement. The cortex, thalamus, putamen, and caudate exhibit elevated metabolic activity. MADI accuracy and precision are assessed. The results are contextualized with literature overall homeostatic brain glucose consumption and ATP production/consumption measures. The MADI/PET results suggest different GM and WM metabolic pathways. Preliminary human prostate results are also presented.     

Astrocytes and human cognition: Modeling information integration and modulation of neuronal activity

Recent research focusing on the participation of astrocytes in glutamatergic tripartite synapses has revealed mechanisms that support cognitive functions common to human and other mammalian species, such as learning, perception, conscious integration, memory formation/retrieval and the control of voluntary behavior. Astrocytes can modulate neuronal activity by means of release of glutamate, d-serine, adenosine triphosphate and other signaling molecules, contributing to sustain, reinforce or depress pre- and post-synaptic membranes. We review molecular mechanisms present in tripartite synapses and model the cognitive role of astrocytes. Single protoplasmic astrocytes operate as a “Local Hub”, integrating information patterns from neuronal and glial populations. Two mechanisms, here modeled as the “domino” and “carousel” effects, contribute to the formation of intercellular calcium waves. As waves propagate through gap junctions and reach other types of astrocytes (interlaminar, polarized, fibrous and varicose projection), the active astroglial network functions as a “Master Hub” that integrates results of distributed processing from several brain areas and supports conscious states. Response of this network would define the effect exerted on neuronal plasticity (membrane potentiation or depression), behavior and psychosomatic processes. Theoretical results of our modeling can contribute to the development of new experimental research programs to test cognitive functions of astrocytes.