Biology subjects

Eklund, A.

Publications and source records attributed to Eklund, A..

6 recordsLinked to original sources

bioRxiv2018

Refacing: reconstructing anonymized facial features using GANs

Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.

neuroscience↗

bioRxiv2018

Using the residual bootstrap to quantify uncertainty in mean apparent propagator MRI

Estimation of noise-induced variability in MAP-MRI is needed to properly characterize the amount of uncertainty in quantities derived from the estimated MAP-MRI coefficients. Bootstrap metrics, such as the standard deviation, provides additional valuable diffusion information in addition to common MAP-MRI parameters, and can be incorporated in MAP-MRI studies to provide more extensive insight. To the best of our knowledge, this is the first paper to study the uncertainty of MAP-MRI derived metrics. The noise variability of quantities of MAP-MRI have been quantified using the residual bootstrap, in which the residuals are resampled using two sampling schemes. The residual bootstrap method can provide empirical distributions for MAP-MRI derived quantities, even when the exact distributions are not easily derived. The residual bootstrap methods are applied to SPARC data and HCP-MGH data, and empirical distributions are obtained for the zero-displacement probabilities. Here, we compare and contrast the residual bootstrap schemes using all shells and within the same shell. We show that residual resampling within each shell generates larger uncertainty than using all shells for the HCP-MGH data. Standard deviation and quartile coefficient maps of the estimated variability are provided.

neuroscience↗

bioRxiv2018

Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with onesample permutation (sign ipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (P = 0.01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

neuroscience↗

bioRxiv2017

Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis

Inference from fMRI data faces the challenge that the hemodynamic system, that relates the underlying neural activity to the observed BOLD fMRI signal, is not known. We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD time series, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way. The prior mean function may be generated from a standard LTI system, based on a canonical hemodynamic response function, or a more elaborate physiological model such as the Balloon model. This gives us the nonparametric flexibility of the GP, but allows the posterior to fall back on the physiologically based prior when the data are weak. Results on simulated data show that even with an erroneous prior for the GP, the proposed model is still able to discriminate between active and non-active voxels in a satisfactory way. The proposed model is also applied to real fMRI data, where our Gaussian process model in several cases finds brain activity where previously proposed LTI models, parametric and nonparametric, does not.

neuroscience↗

bioRxiv2016

BAYESIAN NON-CENTRAL CHI REGRESSION FOR NEUROIMAGING

We propose a regression model for non-central{chi} (NC-{chi}) distributed functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the NC-{chi} distribution to be linked to explanatory variables, with the relevant covariates automatically chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed for simulating from the joint Bayesian posterior distribution of all model parameters and the binary covariate selection indicators. Simulated fMRI data is used to demonstrate that the Rician model is able to localize brain activity much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the theoretically correct Rician model.

neuroscience↗

bioRxiv2016

A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes

We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.

neuroscience↗