11,213 research outputs found
fMRI activation detection with EEG priors
The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from MRI gradient-echo phase signal and typically
requires several processing steps. These steps involve phase unwrapping, brain
volume extraction, background phase removal and solving an ill-posed inverse
problem. The resulting susceptibility map is known to suffer from inaccuracy
near the edges of the brain tissues, in part due to imperfect brain extraction,
edge erosion of the brain tissue and the lack of phase measurement outside the
brain. This inaccuracy has thus hindered the application of QSM for measuring
the susceptibility of tissues near the brain edges, e.g., quantifying cortical
layers and generating superficial venography. To address these challenges, we
propose a learning-based QSM reconstruction method that directly estimates the
magnetic susceptibility from total phase images without the need for brain
extraction and background phase removal, referred to as autoQSM. The neural
network has a modified U-net structure and is trained using QSM maps computed
by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82
years were employed for patch-wise network training. The network was validated
on data dissimilar to the training data, e.g. in vivo mouse brain data and
brains with lesions, which suggests that the network has generalized and
learned the underlying mathematical relationship between magnetic field
perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic
susceptibility of anatomical structures near the edges of the brain including
the veins covering the cortical surface, spinal cord and nerve tracts near the
mouse brain boundaries. The advantages of high-quality maps, no need for brain
volume extraction and high reconstruction speed demonstrate its potential for
future applications.Comment: 26 page
- …