175 research outputs found
Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI
No full textThe combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals. Recently, simultaneous EEG-fMRI measurements have been used to investigate the relation between the two signals. Previous attempts at the analysis of simultaneous EEG-fMRI data reported significant correlations between regional BOLD activations and modulation of both event-related potential (ERP) and oscillatory EEG power, mostly in the alpha but also in other frequency bands. Beyond the correlation of the two measured brain signals, the relevant issue we address here is the ability of predicting the signal in one modality using information from the other modality. Using multivariate machine learning-based regression, we show how it is possible to predict EEG power oscillations from simultaneously acquired fMRI data during an eyes-open/eyes-closed task using either the original channels or the underlying cortically distributed sources as the relevant EEG signal for the analysis of multimodal data
dMRI: Diffusion Magnetic Resonance Imaging as a Window onto Structural Brain Networks and White Matter Microstructure
No full textDiffusion magnetic resonance imaging (dmri) can be used to probe the connectivity and microstructure of human brain tissue non-invasively in vivo. The diffusion-weighted mr signal has sensitivity to micrometer-scale tissue properties averaged over the imaging voxel size, for example, intra-cellular and extra-cellular volumes and the 3d orientations of axonal bundles. It derives its contrast from sensitivity to the bulk displacement of water through what is known as thermal motion, brownian motion, or passive (self-)diffusion. In the brain, cell membranes, organelles, and myelin sheaths create barriers and form biological compartments that constrain the displacement of water molecules, modifying the statistical behavior of bulk diffusion over time. This review therefore focuses on the use of dmri for estimates of the local orientations and estimates of microstructure of fiber tracts, and on an understanding of dmri signal mechanisms and appropriate signal processing and modeling for this purpose. It first discusses basic diffusion mri acquisition principles and diffusion contrast and the constraints the acquisition places on the modeling of diffusion. It then sets out the diffusion tensor model, the most used model in dmri that underlies diffusion tensor imaging, in a way which prepares a discussion of its limitations. The next sections set out advances in dmri beyond dti as focusing on tractography and connectomics, with a need to accurately model spatially complex fiber configurations and on diffusion microstructure, with a need to accurately model restricted diffusion and compartmentalization. Throughout, the emphasis is on a thorough understanding of basic principles and assumptions underlying techniques, as well as their possibilities and limitations for inference of brain connectivity, with a minimum of technical detail and mathematics. This review ends with an outlook on future developments emanating from current trends
Diffusion weighted magnetic resonance imaging : validation, correction and applications
No full textDiffusion weighted Magnetic Resonance Imaging (DW-MRI) images the structure of the white substance in the brains and connections between various brain parts. To validate this technique a test object (phantom) was developed, the structure of which resembles the structure of the white substance. This phantom was brought on the market and by now over 15 universities and hospitals own one. Furthermore, DW-MRI was applied to a patient with Landau-Kleffner syndrome. This syndrome involves losing the ability of using and understanding language as a consequence of epilepsy. Intensive therapy can restore the language ability. Important connections for language processing look different in these patients and the brains use more visual information in communication. The dissertation also describes further studies with a blindsight patient. In these patients the eyes still function, but the visual information is no longer consciously processed. And yet, the deep brain parts that unconsciously process emotions and visual information appear active. The necessary brain connections were shown in this patient and they were not present in control persons. Therefore, the patient has created new connections
Methods of diffusion-weighted and functional magnetic resonance imaging investigated in the human brain at ultra-high-field
No full textMagnetic resonance imaging (MRI) allows to obtain various types of brain images. A particular MRI method is diffusion tensor imaging (DTI), which can visualize nerve fiber pathways. The method is indirect, involving complicated mathematical models to infer this kind of information from the raw data. Therefore, it has to be investigated whether the results match the actual fiber architecture in practice. Parts of this PhD thesis are concerned with this problem: DTI results were compared to microscopical scans of brain tissue, where nerve fibers had been stained. Doing so, it was shown that DTI indeed works as theoretically supposed to
Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI
No full textThe structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso- and microscopic organization of brain connectivity and anatomy, both in vivo and ex vivo. Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these
High-Resolution Diffusion Tensor Imaging and Tractography of the Human Optic Chiasm at 9.4 T
No full textThe optic chiasm with its complex fiber micro-structure is a challenge for diffusion tensor models and tractography methods. Likewise, it is an ideal candidate for evaluation of diffusion tensor imaging tractography approaches in resolving inter-regional connectivity because the macroscopic connectivity of the optic chiasm is well known. Here, high-resolution (156 pm in-plane) diffusion tensor imaging of the human optic chiasm was performed ex vivo at ultra-high field (9.4 T). Estimated diffusion tensors at this high resolution were able to capture complex fiber configurations such as sharp curves, and convergence and divergence of tracts, but were unable to resolve directions at sites of crossing fibers. Despite the complex microstructure of the fiber paths through the optic chiasm, all known connections could be tracked by a line propagation algorithm. However, fibers crossing from the optic nerve to the contralateral tract were heavily underrepresented, whereas ipsilateral nerve-to-tract connections, as well as tract-to-tract connections, were overrepresented, and erroneous nerve-to-nerve connections were tracked. The effects of spatial resolution and the varying degrees of partial volume averaging of complex fiber architecture on the performance of these methods could be investigated. Errors made by the tractography algorithm at high resolution were shown to increase at lower resolutions closer to those used in vivo. This study shows that increases in resolution, made possible by higher field strengths, improve the accuracy of DTI-based tractography. More generally, post-mortem investigation of fixed tissue samples with diffusion imaging at high field strengths is important in the evaluation of MR-based diffusion models and tractography algorithms
Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models
No full textIn diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200
Investigating human neocortical architecture in 3D:new approaches for clearing, labelling and imaging large samples
No full textBy turning human brain tissue as transparent as glass, the brain’s complex structure can be studied in 3D. This is possible by imaging these very large, transparent samples with a special microscope that can quickly scan huge tissue volumes. By doing so, it was possible to visualise different components of the human neocortex, the most recent and most complex add-on to the brain during evolution
- …
