Cell specific quantitative iron mapping on brain slices by immuno-µPIXE in healthy elderly and Parkinson's disease.

Iron is essential for neurons and glial cells, playing key roles in neurotransmitter synthesis, energy production and myelination. In contrast, high concentrations of free iron can be detrimental and contribute to neurodegeneration, through promotion of oxidative stress. Particularly in Parkinson's disease (PD) changes in iron concentrations in the substantia nigra (SN) was suggested to play a key role in degeneration of dopaminergic neurons in nigrosome 1. However, the cellular iron pathways and the mechanisms of the pathogenic role of iron in PD are not well understood, mainly due to the lack of quantitative analytical techniques for iron quantification with subcellular resolution. Here, we quantified cellular iron concentrations and subcellular iron distributions in dopaminergic neurons and different types of glial cells in the SN both in brains of PD patients and in non-neurodegenerative control brains (Co). To this end, we combined spatially resolved quantitative element mapping using micro particle induced X-ray emission (µPIXE) with nickel-enhanced immunocytochemical detection of cell type-specific antigens allowing to allocate element-related signals to specific cell types. Distinct patterns of iron accumulation were observed across different cell populations. In the control (Co) SNc, oligodendroglial and astroglial cells hold the highest cellular iron concentration whereas in PD, the iron concentration was increased in most cell types in the substantia nigra except for astroglial cells and ferritin-positive oligodendroglial cells. While iron levels in astroglial cells remain unchanged, ferritin in oligodendroglial cells seems to be depleted by almost half in PD. The highest cellular iron levels in neurons were located in the cytoplasm, which might increase the source of non-chelated Fe3+, implicating a critical increase in the labile iron pool. Indeed, neuromelanin is characterised by a significantly higher loading of iron including most probable the occupancy of low-affinity iron binding sites. Quantitative trace element analysis is essential to characterise iron in oxidative processes in PD. The quantification of iron provides deeper insights into changes of cellular iron levels in PD and may contribute to the research in iron-chelating disease-modifying drugs.

Extrapyramidal plasticity predicts recovery after spinal cord injury.

Spinal cord injury (SCI) leads to wide-spread neurodegeneration across the neuroaxis. We explored trajectories of surface morphology, demyelination and iron concentration within the basal ganglia-thalamic circuit over 2 years post-SCI. This allowed us to explore the predictive value of neuroimaging biomarkers and determine their suitability as surrogate markers for interventional trials. Changes in markers of surface morphology, myelin and iron concentration of the basal ganglia and thalamus were estimated from 182 MRI datasets acquired in 17 SCI patients and 21 healthy controls at baseline (1-month post injury for patients), after 3, 6, 12, and 24 months. Using regression models, we investigated group difference in linear and non-linear trajectories of these markers. Baseline quantitative MRI parameters were used to predict 24-month clinical outcome. Surface area contracted in the motor (i.e. lower extremity) and pulvinar thalamus, and striatum; and expanded in the motor thalamus and striatum in patients compared to controls over 2-years. In parallel, myelin-sensitive markers decreased in the thalamus, striatum, and globus pallidus, while iron-sensitive markers decreased within the left caudate. Baseline surface area expansions within the striatum (i.e. motor caudate) predicted better lower extremity motor score at 2-years. Extensive extrapyramidal neurodegenerative and reorganizational changes across the basal ganglia-thalamic circuitry occur early after SCI and progress over time; their magnitude being predictive of functional recovery. These results demonstrate a potential role of extrapyramidal plasticity during functional recovery after SCI.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI.

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

[Computational neuroanatomy and microstructure imaging using magnetic resonance imaging]. / Computationale Neuroanatomie und Mikrostrukturbildgebung mit der Magnetresonanztomographie.

BACKGROUND: Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also greatly improves the sensitivity and specificity with respect to the microstructural characteristics of tissue. OBJECTIVE: Current methodological developments in qMRI are presented, which go beyond morphology because this provides standardized measurements of the microstructure of the brain. The concept of in-vivo histology is introduced, based on biophysical modelling of qMRI data (hMRI) for determination of quantitative histology-like markers of the microstructure. RESULTS: The qMRI metrics can be used as direct biomarkers of the microstructural mechanisms driving observed morphological findings. The hMRI metrics utilize biophysical models of the MRI signal in order to determine 3­dimensional maps of histology-like measurements in the white matter. CONCLUSION: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both scientific and clinical applications. Both approaches improve the comparability across sites and time points, facilitate multicenter and longitudinal studies as well as standardized diagnostics. The hMRI is expected to shed new light on the relationship between brain microstructure, function and behavior both in health and disease. In the future hMRI will play an indispensable role in the field of computational neuroanatomy.

New tissue priors for improved automated classification of subcortical brain structures on MRI.

Despite the constant improvement of algorithms for automated brain tissue classification, the accurate delineation of subcortical structures using magnetic resonance images (MRI) data remains challenging. The main difficulties arise from the low gray-white matter contrast of iron rich areas in T1-weighted (T1w) MRI data and from the lack of adequate priors for basal ganglia and thalamus. The most recent attempts to obtain such priors were based on cohorts with limited size that included subjects in a narrow age range, failing to account for age-related gray-white matter contrast changes. Aiming to improve the anatomical plausibility of automated brain tissue classification from T1w data, we have created new tissue probability maps for subcortical gray matter regions. Supported by atlas-derived spatial information, raters manually labeled subcortical structures in a cohort of healthy subjects using magnetization transfer saturation and R2* MRI maps, which feature optimal gray-white matter contrast in these areas. After assessment of inter-rater variability, the new tissue priors were tested on T1w data within the framework of voxel-based morphometry. The automated detection of gray matter in subcortical areas with our new probability maps was more anatomically plausible compared to the one derived with currently available priors. We provide evidence that the improved delineation compensates age-related bias in the segmentation of iron rich subcortical regions. The new tissue priors, allowing robust detection of basal ganglia and thalamus, have the potential to enhance the sensitivity of voxel-based morphometry in both healthy and diseased brains.

Forming first impressions of others in schizophrenia: impairments in fast processing and in use of spatial frequency information.

Individuals form first impressions of others all the time, which affects their social functioning. Typical adults form threat impressions in faces with neutral expressions quickly, requiring less than 40 ms. These impressions appear to be mediated by low spatial frequency (LSF) content in the images. Little is known, however, about mechanisms of first impression formation in schizophrenia. The current study investigated how quickly individuals with schizophrenia can form consistent impressions of threat compared with controls and explored the mechanisms involved. Patients and controls were presented intact, LSF- or high spatial frequency (HSF)-filtered faces with durations that varied from 39 to 1703 ms and were asked to rate how threatening each face was on a scale from 1 to 5. In order to assess the speed of impression formation for intact faces, correlations were calculated for ratings made at each duration compared to a reference duration of 1703 ms for each group. Controls demonstrated a significant relation for intact faces presented for 39 ms, whereas patients required 390 ms to demonstrate a significant relation with the reference duration. For controls, LSFs primarily contributed to the formation of consistent threat impressions at 39 ms, whereas patients showed a trend for utilizing both LSF and HSF information to form consistent threat impressions at 390 ms. Results indicate that individuals with schizophrenia require a greater integration time to form a stable "first impression" of threat, which may be related to the need to utilize compensatory mechanisms such as HSF, as well as LSF, information.

Disentangling in vivo the effects of iron content and atrophy on the ageing human brain.

Evidence from magnetic resonance imaging (MRI) studies shows that healthy aging is associated with profound changes in cortical and subcortical brain structures. The reliable delineation of cortex and basal ganglia using automated computational anatomy methods based on T1-weighted images remains challenging, which results in controversies in the literature. In this study we use quantitative MRI (qMRI) to gain an insight into the microstructural mechanisms underlying tissue ageing and look for potential interactions between ageing and brain tissue properties to assess their impact on automated tissue classification. To this end we acquired maps of longitudinal relaxation rate R1, effective transverse relaxation rate R2* and magnetization transfer - MT, from healthy subjects (n=96, aged 21-88 years) using a well-established multi-parameter mapping qMRI protocol. Within the framework of voxel-based quantification we find higher grey matter volume in basal ganglia, cerebellar dentate and prefrontal cortex when tissue classification is based on MT maps compared with T1 maps. These discrepancies between grey matter volume estimates can be attributed to R2* - a surrogate marker of iron concentration, and further modulation by an interaction between R2* and age, both in cortical and subcortical areas. We interpret our findings as direct evidence for the impact of ageing-related brain tissue property changes on automated tissue classification of brain structures using SPM12. Computational anatomy studies of ageing and neurodegeneration should acknowledge these effects, particularly when inferring about underlying pathophysiology from regional cortex and basal ganglia volume changes.

Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS.

We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods.

Tissue- and column-specific measurements from multi-parameter mapping of the human cervical spinal cord at 3 T.

The aim of this study was to quantify a range of MR parameters [apparent proton density, longitudinal relaxation time T1, magnetisation transfer (MT) ratio, MT saturation (which represents the additional percentage MT saturation of the longitudinal magnetisation caused by a single MT pulse) and apparent transverse relaxation rate R2*] in the white matter columns and grey matter of the healthy cervical spinal cord. The cervical cords of 13 healthy volunteers were scanned at 3 T using a protocol optimised for multi-parameter mapping. Intra-subject co-registration was performed using linear registration, and tissue- and column-specific parameter values were calculated. Cervical cord parameter values measured from levels C1-C5 in 13 subjects are: apparent proton density, 4822 ± 718 a.u.; MT ratio, 40.4 ± 1.53 p.u.; MT saturation, 1.40 ± 0.12 p.u.; T1 = 1848 ± 143 ms; R2* = 22.6 ± 1.53 s(-1). Inter-subject coefficients of variation were low in both the cervical cord and tissue- and column-specific measurements, illustrating the potential of this method for the investigation of changes in these parameters caused by pathology. In summary, an optimised cervical cord multi-parameter mapping protocol was developed, enabling tissue- and column-specific measurements to be made. This technique has the potential to provide insight into the pathological processes occurring in the cervical cord affected by neurological disorders.

Real-time fMRI neurofeedback: progress and challenges.

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

The impact of physiological noise correction on fMRI at 7 T.

Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3mm isotropic resolution and whole brain image repetition time of 2s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR ~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.

Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ).

Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo neuroimaging captures spatial and temporal patterns of age-related changes of anatomy at the macroscopic scale, our knowledge of the underlying (patho)physiological processes at cellular and molecular levels is still limited. The aim of this study is to explore brain tissue properties in normal ageing using quantitative magnetic resonance imaging (MRI) alongside conventional morphological assessment. Using a whole-brain approach in a cohort of 26 adults, aged 18-85years, we performed voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) of diffusion tensor, magnetization transfer (MT), R1, and R2* relaxation parameters. We found age-related reductions in cortical and subcortical grey matter volume paralleled by changes in fractional anisotropy (FA), mean diffusivity (MD), MT and R2*. The latter were regionally specific depending on their differential sensitivity to microscopic tissue properties. VBQ of white matter revealed distinct anatomical patterns of age-related change in microstructure. Widespread and profound reduction in MT contrasted with local FA decreases paralleled by MD increases. R1 reductions and R2* increases were observed to a smaller extent in overlapping occipito-parietal white matter regions. We interpret our findings, based on current biophysical models, as a fingerprint of age-dependent brain atrophy and underlying microstructural changes in myelin, iron deposits and water. The VBQ approach we present allows for systematic unbiased exploration of the interaction between imaging parameters and extends current methods for detection of neurodegenerative processes in the brain. The demonstrated parameter-specific distribution patterns offer insights into age-related brain structure changes in vivo and provide essential baseline data for studying disease against a background of healthy ageing.

Graph-partitioned spatial priors for functional magnetic resonance images.

Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm(3)) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.

Effective infection, apoptotic cell killing and gene transfer of human hepatoma cells but not primary hepatocytes by parvovirus H1 and derived vectors.

Autonomous parvoviruses preferentially replicate in and kill in vitro-transformed cells and reduce the incidence of spontaneous and implanted tumors in animals. Because of these natural oncotropic and oncolytic properties, parvoviruses deserve to be considered as potential antitumor vectors. Here, we assessed whether parvovirus H1 is able to kill human hepatoma cells by induction of apoptosis but spares primary human liver cells, and whether the former cells can efficiently be transduced by H1 virus-based vectors. Cell death, infectivity, and transgene transduction were investigated in Hep3B, HepG2, and Huh7 cells and in primary human hepatocytes with natural and recombinant H1 virus. All hepatoma cells were susceptible to H1 virus-induced cytolyis. Cell death correlated with H1 virus DNA replication, nonstructural protein expression, and with morphological features of apoptosis. H1 virus-induced apoptosis was more pronounced in p53-deleted Hep3B and p53-mutated Huh7 cells than in HepG2 cells which express wild-type p53. In Hep3B cells, apoptosis was partially inhibited by DEVD-CHO, a caspase-3 inhibitor. In contrast, H1 virus-infected primary hepatocytes were neither positive for nonstructural protein expression nor susceptible to H1 virus-induced killing. Infection with a recombinant parvovirus vector carrying the luciferase gene under control of parvovirus promoter P38 led to higher transgene activities in hepatoma cells than in the hepatocytes. Taken together, H1 virus kills human hepatoma cells at low virus multiplicity but not primary hepatocytes. Thus, recombinant H1 viruses carrying antitumor transgenes may be considered as potential therapeutic options for the treatment of hepatocellular carcinomas.