Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Estimation of the postmortem interval by means of ¹H MRS of decomposing brain tissue: influence of ambient temperature

Standard methods for the estimation of the postmortem interval (PMI, time since death), based on the cooling of the corpse, are limited to about 48 h after death. As an alternative, noninvasive postmortem observation of alterations of brain metabolites by means of (1)H MRS has been suggested for an estimation of the PMI at room temperature, so far without including the effect of other ambient temperatures. In order to study the temperature effect, localized (1)H MRS was used to follow brain decomposition in a sheep brain model at four different temperatures between 4 and 26°C with repeated measurements up to 2100 h postmortem. The simultaneous determination of 25 different biochemical compounds at each measurement allowed the time courses of concentration changes to be followed. A sudden and almost simultaneous change of the concentrations of seven compounds was observed after a time span that decreased exponentially from 700 h at 4°C to 30 h at 26°C ambient temperature. As this represents, most probably, the onset of highly variable bacterial decomposition, and thus defines the upper limit for a reliable PMI estimation, data were analyzed only up to this start of bacterial decomposition. As 13 compounds showed unequivocal, reproducible concentration changes during this period while eight showed a linear increase with a slope that was unambiguously related to ambient temperature. Therefore, a single analytical function with PMI and temperature as variables can describe the time courses of metabolite concentrations. Using the inverse of this function, metabolite concentrations determined from a single MR spectrum can be used, together with known ambient temperatures, to calculate the PMI of a corpse. It is concluded that the effect of ambient temperature can be reliably included in the PMI determination by (1)H MRS.

Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study)

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents.

Quantification of network perfusion in ASL cerebral blood flow data with seed based and ICA approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.

Social Cognition And Functional Brain Imaging: Extending The Broader Autism Phenotype

Evidence suggests that the social cognition deficits prevalent in autism spectrum disorders (ASDs) are widely distributed in first degree and extended relatives. This ¿broader autism phenotype¿ (BAP) can be extended into non-clinical populations and show wide distributions of social behaviors such as empathy and social responsiveness ¿ with ASDs exhibiting these behaviors on the lower ends of the distributions. Little evidence has previously shown relationships between self-report measures of social cognition and more objective tasks such as face perception in functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs). In this study, three specific hypotheses were addressed: a) increased social ability, as measured by an increased Empathy Quotient, decreased Social Responsiveness Scale (SRS-A) score, and increased Social Attribution Task score, will predict increased activation of the fusiform gyrus in response to faces as compared to houses; b) these same measures will predict N170 amplitude and latency showing decreased latency and increased amplitude for faces as compared to houses with increased social ability; c) increased amygdala volume will predict increased fusiform gyrus activation when viewing faces as compared to houses. Findings supported all of the hypotheses. Empathy scores significantly predicted both right FFG activation [F(1,20) = 4.811, p = .041, ß = .450, R2 = 0.20] and left FFG activation [F(1,20) = 7.70, p = .012, ß = .537, R2 = 0.29]. Based on ERP results increased right lateralization face-related N170 was significantly predicted by the EQ [F(1,54) = 6.94, p = .011, ß = .338, R2 = 0.11]. Finally, total amygdala volume significantly predicted right [F(1,20) = 7.217, p = .014, ß = .515, R2 = 0.27] and left [F(1,20) = 36.77, p < .001, ß = .805, R2 = 0.65] FFG activation. Consistent with the a priori hypotheses, traits attributed to the BAP can significantly predict neural responses to faces in a non-clinical population. This is consistent with the face processing deficits seen in ASDs. The findings presented here contribute to the extension of the BAP from unaffected relatives of individuals with ASDs to the general population. These findings also give continued evidence in support of a continuous distribution of traits found in psychiatric illnesses in place of a traditional, dichotomous ¿all-or-nothing¿ diagnostic framework of neurodevelopmental and neuropsychiatric disorders.

Time course based artifact identification for independent components of resting-state FMRI

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

Molecular mechanisms involved in T cell migration across the blood-brain barrier

In the healthy individuum lymphocyte traffic into the central nervous system (CNS) is very low and tightly controlled by the highly specialized blood-brain barrier (BBB). In contrast, under inflammatory conditions of the CNS such as in multiple sclerosis or in its animal model experimental autoimmune encephalomyelitis (EAE) circulating lymphocytes and monocytes/macrophages readily cross the BBB and gain access to the CNS leading to edema, inflammation and demyelination. Interaction of circulating leukocytes with the endothelium of the blood-spinal cord and blood-brain barrier therefore is a critical step in the pathogenesis of inflammatory diseases of the CNS. Leukocyte/endothelial interactions are mediated by adhesion molecules and chemokines and their respective chemokine receptors. We have developed a novel spinal cord window preparation, which enables us to directly visualize CNS white matter microcirculation by intravital fluorescence videomicroscopy. Applying this technique of intravital fluorescence videomicroscopy we could provide direct in vivo evidence that encephalitogenic T cell blasts interact with the spinal cord white matter microvasculature without rolling and that alpha4-integrin mediates the G-protein independent capture and subsequently the G-protein dependent adhesion strengthening of T cell blasts to microvascular VCAM-1. LFA-1 was found to neither mediate the G-protein independent capture nor the G- protein dependent initial adhesion strengthening of encephalitogenic T cell blasts within spinal cord microvessel, but was rather involved in T cell extravasation across the vascular wall into the spinal cord parenchyme. Our observation that G-protein mediated signalling is required to promote adhesion strengthening of encephalitogenic T cells on BBB endothelium in vivo suggested the involvement of chemokines in this process. We found functional expression of the lymphoid chemokines CCL19/ELC and CCL21/SLC in CNS venules surrounded by inflammatory cells in brain and spinal cord sections of mice afflicted with EAE suggesting that the lymphoid chemokines CCL19 and CCL21 besides regulating lymphocyte homing to secondary lymphoid tissue might be involved in T lymphocyte migration into the immuneprivileged CNS during immunosurveillance and chronic inflammation. Here, I summarize our current knowledge on the sequence of traffic signals involved in T lymphocyte recruitment across the healthy and inflamed blood-brain and blood-spinal cord barrier based on our in vitro and in vivo investigations.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Brain atrophy in pure and complicated hereditary spastic paraparesis: a quantitative 3D MRI study

Hereditary spastic paraparesis (HSP) is a heterogeneous group of neurodegenerative disorders with progressive lower limb spasticity, categorized into pure (p-HSP) and complicated forms (c-HSP). The purpose of this study was to evaluate if brain volumes in HSP were altered compared with a control population. Brain volumes were determined in patients suffering from HSP, including both p-HSP (n = 21) and c-HSP type (n = 12), and 30 age-matched healthy controls, using brain parenchymal fractions (BPF) calculated from 3D MRI data in an observer-independent procedure. In addition, the tissue segments of grey and white matter were analysed separately. In HSP patients, BPF were significantly reduced compared with controls both for the whole patient group (P < 0.001) and for both subgroups, indicating considerable brain atrophy. In contrast to controls who showed a decline of brain volumes with age, this physiological phenomenon was less pronounced in HSP. Therefore, global brain parenchyma reduction, involving both grey and white matter, seems to be a feature in both subtypes of HSP. Atrophy was more pronounced in c-HSP, consistent with the more severe phenotype including extramotor involvement. Thus, global brain atrophy, detected by MRI-based brain volume quantification, is a biological marker in HSP subtypes.

FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING

We establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models. The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a functional mixed effect model is fitted to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.

ON THE MERITS OF VOXEL-BASED MORPHOMETRIC PATH-ANALYSIS FOR INVESTIGATING VOLUMETRIC MEDIATION OF A TOXICANT'S INFLUENCE ON COGNITIVE FUNCTION

We previously showed that lifetime cumulative lead dose, measured as lead concentration in the tibia bone by X-ray fluorescence, was associated with persistent and progressive declines in cognitive function and with decreases in MRI-based brain volumes in former lead workers. Moreover, larger region-specific brain volumes were associated with better cognitive function. These findings motivated us to explore a novel application of path analysis to evaluate effect mediation. Voxel-wise path analysis, at face value, represents the natural evolution of voxel-based morphometry methods to answer questions of mediation. Application of these methods to the former lead worker data demonstrated potential limitations in this approach where there was a tendency for results to be strongly biased towards the null hypothesis (lack of mediation). Moreover, a complimentary analysis using anatomically-derived regions of interest volumes yielded opposing results, suggesting evidence of mediation. Specifically, in the ROI-based approach, there was evidence that the association of tibia lead with function in three cognitive domains was mediated through the volumes of total brain, frontal gray matter, and/or possibly cingulate. A simulation study was conducted to investigate whether the voxel-wise results arose from an absence of localized mediation, or more subtle defects in the methodology. The simulation results showed the same null bias evidenced as seen in the lead workers data. Both the lead worker data results and the simulation study suggest that a null-bias in voxel-wise path analysis limits its inferential utility for producing confirmatory results.

Leukaemia, brain tumours and exposure to extremely low frequency magnetic fields: cohort study of Swiss railway employees

AIMS: To investigate the relationship between extremely low frequency magnetic field (ELF-MF) exposure and mortality from leukaemia and brain tumour in a cohort of Swiss railway workers. METHODS: 20,141 Swiss railway employees with 464,129 person-years of follow-up between 1972 and 2002 were studied. Mortality rates for leukaemia and brain tumour of highly exposed train drivers (21 muT average annual exposure) were compared with medium and low exposed occupational groups (i.e. station masters with an average exposure of 1 muT). In addition, individual cumulative exposure was calculated from on-site measurements and modelling of past exposures. RESULTS: The hazard ratio (HR) for leukaemia mortality of train drivers was 1.43 (95% CI 0.74 to 2.77) compared with station masters. For myeloid leukaemia the HR of train drivers was 4.74 (95% CI 1.04 to 21.60) and for Hodgkin's disease 3.29 (95% CI 0.69 to 15.63). Lymphoid leukaemia, non-Hodgkin's disease and brain tumour mortality were not associated with magnetic field exposure. Concordant results were obtained from analyses based on individual cumulative exposure. CONCLUSIONS: Some evidence of an exposure-response association was found for myeloid leukaemia and Hodgkin's disease, but not for other haematopoietic and lymphatic malignancies and brain tumours.

Prospective navigator-echo-based real-time triggering of fetal head movement for the reduction of artifacts

The purpose of this study was to evaluate the neuroimaging quality and accuracy of prospective real-time navigator-echo acquisition correction versus untriggered intrauterine magnetic resonance imaging (MRI) techniques. Twenty women in whom fetal motion artifacts compromised the neuroimaging quality of fetal MRI taken during the 28.7 +/- 4 week of pregnancy below diagnostic levels were additionally investigated using a navigator-triggered half-Fourier acquired single-shot turbo-spin echo (HASTE) sequence. Imaging quality was evaluated by two blinded readers applying a rating scale from 1 (not diagnostic) to 5 (excellent). Diagnostic criteria included depiction of the germinal matrix, grey and white matter, CSF, brain stem and cerebellum. Signal-difference-to-noise ratios (SDNRs) in the white matter and germinal zone were quantitatively evaluated. Imaging quality improved in 18/20 patients using the navigator echo technique (2.4 +/- 0.58 vs. 3.65 +/- 0.73 SD, p < 0.01 for all evaluation criteria). In 2/20 patients fetal movement severely impaired image quality in conventional and navigated HASTE. Navigator-echo imaging revealed additional structural brain abnormalities and confirmed diagnosis in 8/20 patients. The accuracy improved from 50% to 90%. Average SDNR increased from 0.7 +/- 7.27 to 19.83 +/- 15.71 (p < 0.01). Navigator-echo-based real-time triggering of fetal head movement is a reliable technique that can deliver diagnostic fetal MR image quality despite vigorous fetal movement.

Clinical forensic radiology in strangulation victims: forensic expertise based on magnetic resonance imaging (MRI) findings

Based on only one objective and several subjective signs, the forensic classification of strangulation incidents concerning their life-threatening quality can be problematic. Reflecting that it is almost impossible to detect internal injuries of the neck with the standard forensic external examination, we examined 14 persons who have survived manual and ligature strangulation or forearm choke holds using MRI technique (1.5-T scanner). Two clinical radiologists evaluated the neck findings independently. The danger to life was evaluated based on the "classical" external findings alone and in addition to the radiological data. We observed hemorrhaging in the subcutaneous fatty tissue of the neck in ten cases. Other frequent findings were hemorrhages of the neck and larynx muscles, the lymph nodes, the pharynx, and larynx soft tissues. Based on the classical forensic strangulation findings with MRI, eight of the cases were declared as life-endangering incidents, four of them without the presence of petechial hemorrhage but with further signs of impaired brain function due to hypoxia. The accuracy of future forensic classification of the danger to life will probably be increased when it is based not only on one objective and several subjective signs but also on the evidence of inner neck injuries. However, further prospective studies including larger cohorts are necessary to clarify the value of the inner neck injuries in the forensic classification of surviving strangulation victims.

Brain responses to auditory and visual stimulus offset: Shared representations of temporal edges

Edges are crucial for the formation of coherent objects from sequential sensory inputs within a single modality. Moreover, temporally coincident boundaries of perceptual objects across different sensory modalities facilitate crossmodal integration. Here, we used functional magnetic resonance imaging in order to examine the neural basis of temporal edge detection across modalities. Onsets of sensory inputs are not only related to the detection of an edge but also to the processing of novel sensory inputs. Thus, we used transitions from input to rest (offsets) as convenient stimuli for studying the neural underpinnings of visual and acoustic edge detection per se. We found, besides modality-specific patterns, shared visual and auditory offset-related activity in the superior temporal sulcus and insula of the right hemisphere. Our data suggest that right hemispheric regions known to be involved in multisensory processing are crucial for detection of edges in the temporal domain across both visual and auditory modalities. This operation is likely to facilitate cross-modal object feature binding based on temporal coincidence. Hum Brain Mapp, 2008. (c) 2008 Wiley-Liss, Inc.