Biology subjects

Eklund, A.

Publications and source records attributed to Eklund, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Understanding cognitive function in post-COVID-19 condition with fatigue: an interdisciplinary multimodal investigation

Lingering cognitive symptoms of post-COVID-19 condition (PCC) continue to challenge our understanding, highlighting the need for interdisciplinary research. We investigated whether individuals with PCC and fatigue differ from matched controls across neurocognitive, neuroimaging, and immunological measures, integrating these findings with qualitative phenomenological analyses through multimodal cross-analysis. Individuals with PCC and clinically significant fatigue (n=34), and age-, sex-, and education-matched controls recovered from COVID-19 without PCC (n=37) were included. Assessments encompassed physician evaluation, neuropsychological and functional tests, structural and functional MRI, immunological tests, and qualitative phenomenological analysis of interviews. Data were analyzed using established methods and an interdisciplinary cross-analysis to examine persistent cognitive symptoms in PCC. Group comparisons revealed subtle differences between individuals with PCC and controls, while neurocognitive performance and immunological markers remained within normative ranges. Modest differences in white matter, myelin related, and global brain measures were observed, alongside differences in associations between network connectivity and neurocognitive performance across groups, suggesting reduced network resilience rather than domain specific deficits. This pattern resonated with qualitative phenomenological findings: altered experiences of concentrating and difficulties to execute decisions and actions, despite preserved performance on standard neurocognitive tests. These findings suggest that cognitive symptoms in post-COVID-19 fatigue are associated with subtle neurobiological alterations not captured by standard cognitive assessments. In this highly fatigued sample, converging neuroimaging, neurocognitive and immunological findings point toward reduced network efficiency rather than overt cognitive impairment. Qualitative phenomenological analysis further highlighted altered modes of being in the world characterized by increased cognitive effort and difficulties executing decisions and actions.

neuroscience↗

bioRxiv2025

fMRI-Based CSF Flow Quantification Identifies Cardiac Pulsatility as the Dominant Driver Over Respiratory and Slow Vasomotion Cycles

PurposeCerebrospinal fluid (CSF) flow oscillations have emerged as a potentially important marker related to brain clearance, but their acquisition often relies on specialized imaging MRI sequences. The purpose of this work was to enable quantitative assessment of CSF flow associated with cardiac, respiratory, and slow-vasomotion cycles using widely available functional magnetic resonance imaging (fMRI) acquisitions. MethodsA method was developed to translate fMRI-derived CSF inflow signals into quantitative flow rates. This approach modelled the spin-history of an oscillating ensemble of molecules. Validation was performed using phantom experiments with oscillatory flow at cardiac-, respiratory-, and slow-vasomotion-like frequencies. The method was further applied to resting-state data from 48 older adults (68-82 years; 19 women) to characterize CSF flow at the foramen magnum. ResultsPhantom experiments demonstrated excellent correlations between estimated and true velocities for cardiac- and respiratory-like frequencies (r = 0.94 and 0.97, respectively) and moderate correlation for a slow-vasomotion-like frequency (r = 0.58). In the population cohort, median CSF stroke volumes were 0.86 [0.61, 1.17] mL for the cardiac cycle, 0.44 [0.25, 0.94] mL for the respiratory cycle, and 0.28 [0.14, 0.45] mL for the slow-vasomotion cycle. ConclusionThe proposed spin-history modeling method enabled quantitative estimation of CSF flow components using a conventional fMRI dataset and showed that the cardiac cycle dominates CSF motion at the foramen magnum.

neuroscience↗

bioRxiv2024

Modeling CSF circulation and the glymphatic system during infusion using subject specific intracranial pressures and brain geometries

BackgroundInfusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open question. Here we introduce a computational model that includes the glymphatic system and enables us to determine the importance of 1) brain geometry, 2) intracranial pressure and 3) physiological parameters on the outcome of and response to an infusion test. MethodsWe implemented a seven-compartment multiple network porous medium model with subject specific geometries from MR images. The model consists of the arterial, capillary and venous blood vessels, their corresponding perivascular spaces, and the extracellular space (ECS). Both subject specific brain geometries and subject specific infusion tests were used in the modeling of both healthy adults and iNPH patients. Furthermore, we performed a systematic study of the effect of variations in model parameters. ResultsBoth the iNPH group and the control group reached a similar steady state solution when subject specific geometries under identical boundary conditions was used in simulation. The difference in terms of average fluid pressure and velocity between the iNPH and control groups, was found to be less than 6 % during all stages of infusion in all compartments. With subject specific boundary conditions, the largest computed difference was a 75 % greater fluid speed in the arterial perivascular space (PVS) in the iNPH group compared to the control group. Changes to material parameters changed fluid speeds by several orders of magnitude in some scenarios. A considerable amount of the CSF pass through the glymphatic pathway in our models during infusion, i.e., 28% and 38% in the healthy and iNPH patients, respectively. ConclusionsUsing computational models, we have found the relative importance of subject specific geometries to be less important than individual differences in terms of fluid pressure and flow rate during infusion. Model parameters such as permeabilities and inter-compartment transfer parameters are uncertain but important and have large impact on the simulation results. The computations predicts that a considerable amount of the infused volume pass through the brain either through the perivascular spaces or the extracellular space.

neuroscience↗

bioRxiv2023

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning

Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusion MRI thanks to its sensitivity to microstructural alterations in tissue. The latter has prompted interest in quantitative mapping of the microstructural parameters, such as the fiber orientation distribution function (fODF), which is instrumental for noninvasively mapping the underlying axonal fiber tracts in white matter through a procedure known as tractography. However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. The proposed approach is evaluated on high quality data from the Human Connectome Project, and is shown to generate upsampled results with a greater correspondence to ground truth high-resolution data than can be achieved with ordinary spline interpolation methods.

neuroscience↗

bioRxiv2022

Get a new perspective on EEG: Convolutional neural network encoders for parametric t-SNE.

Backgroundt-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data is usually first transformed into a set of features, but it is not known which features are optimal. New methodThe principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. A simple neighbor distribution based on ranked distances was used for the high-dimensional representation instead of the traditional normal distribution. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: 1) wakefulness and sleep, 2) interictal epileptiform discharges, and 3) seizure activity. ResultsThe CNN encoders for the three datasets produced low-dimensional representations of the datasets with a global and local structure that conformed well to the EEG characters and generalized to new data. Comparison to existing methodsCompared to parametric t-SNE for either a short-time Fourier transforms or wavelet representation of the datasets, the developed CNN encoders performed equally well but generally produced a higher degree of clustering. ConclusionsThe developed principle is promising and could be further developed to create general tools for exploring relations in EEG data, e.g., visual summaries of recordings, and trends for continuous EEG monitoring. It might also be used to generate features for other types of machine learning.

neuroscience↗

bioRxiv2020

Characterization of spatial dynamics of fMRI data in white matter using diffusion-informed white matter harmonics

In this work, we leverage the Laplacian eigenbasis of voxel- wise white matter (WM) graphs derived from diffusion- weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM har- monics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.

neuroscience↗

bioRxiv2020

Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subjects unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Projects 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

neuroscience↗

bioRxiv2020

Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels

BackgroundIn clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New MethodGenerative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21 channel EEGs. 5,144 hours of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing MethodThe results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5 - 100 subjects. ConclusionsUsing neural networks to restore or upsample EEG signals is a viable alternative to interpolation methods.

neuroscience↗

bioRxiv2020

How does group differences in motion scrubbing affect false positives in functional connectivity studies?

Analyzing resting state fMRI data is difficult due to a weak signal and several noise sources. Head motion is also a major problem and it is common to apply motion scrubbing, i.e. to remove time points where a subject has moved more than some pre-defined motion threshold. A problem arises if one cohort on average moves more than another, since the remaining temporal degrees of freedom are then different for the two groups. The effect of this is that the uncertainty of the functional connectivity estimates (e.g. Pearson correlations) are different for the two groups, but this is seldom modelled in resting state fMRI. We demonstrate that group differences in motion scrubbing can result in inflated false positives, depending on how the temporal auto correlation is modelled when performing the Fisher r-to-z transform.

neuroscience↗

bioRxiv2019

Anatomically informed Bayesian spatial priors for fMRI analysis

Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.

neuroscience↗

bioRxiv2019

Evaluation of six phase encoding based susceptibility distortion correction methods for diffusion MRI

PurposeSusceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.\n\nMethodsIn this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP and PA data.\n\nResultsWe found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.\n\nConclusionWe suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. We also suggest that indirect metrics must be interpreted cautiously when evaluating methods for correcting susceptibility distortions in diffusion MRI data.

neuroscience↗

bioRxiv2019

Intracranial pressure elevation alters CSF clearance pathways

BackgroundInfusion testing is a common procedure to determine whether shunting will be beneficial in patients with normal pressure hydrocephalus. The method has a well-developed theoretical foundation and corresponding mathematical models that describe the CSF circulation from the choroid plexus to the arachnoid granulations. Here, we investigate to what extent the proposed glymphatic or paravascular pathway (or similar pathways) modifies the results of the traditional mathematical models.\n\nMethodsWe used a two-compartment model consisting of the subarachnoid space and the paravascular spaces. For the arachnoid granulations, the cribriform plate, capillaries and paravascular spaces, resistances were calculated and used to estimate flow before and during an infusion test. Next, pressure in the subarachnoid space and paravascular spaces were computed. Finally, different variations to the model were tested to evaluate the sensitivity of selected parameters.\n\nResultsAt baseline, we found a very small paravascular flow directed into the subarachnoid space, while 60% of the fluid left through the arachnoid granulations and 40% left through the cribriform plate. However, during the infusion, paravascular flow reversed and 25% of the fluid left through these spaces, while 60% went through the arachnoid granulations and only 15% through the cribriform plate.\n\nConclusionsThe relative distribution of CSF flow to different clearance pathways depends on intracranial pressure (ICP), with the arachnoid granulations as the main contributor to outflow. As such, ICP increase is an important factor that should be addressed when determining the pathways of injected substances in the subarachnoid space.

bioengineering↗

bioRxiv2019

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

biophysics↗

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↗