Biology subjects

Fiebach, J. B.

Publications and source records attributed to Fiebach, J. B..

3 recordsLinked to original sources

bioRxiv2020

A novel approach for assessing hypoperfusion in stroke using spatial independent component analysis of resting-state fMRI data

Individualized treatment of acute stroke depends on the timely detection of ischemia and potentially salvageable tissue in the brain. Using functional MRI (fMRI), it is possible to characterize cerebral blood flow from blood-oxygen-level-dependent (BOLD) signals without the administration of exogenous contrast agents. In this study, we applied spatial independent component analysis to resting-state fMRI data of 37 stroke patients scanned within 24 hours of symptom onset, 17 of whom received follow-up scans the next day. Our analysis revealed "Hypoperfusion spatially-Independent Components" (HICs) whose spatial patterns of BOLD signal resembled regions of delayed perfusion depicted by dynamic susceptibility contrast MRI. These HICs were detected even in the presence of excessive patient motion, and disappeared following successful tissue reperfusion. The unique spatial and temporal features of HICs allowed them to be distinguished with high accuracy from other components in a user-independent manner (AUC = 0.95, accuracy = 0.96, sensitivity = 1.00, specificity = 0.96). Our study therefore presents a new, non-invasive method for assessing blood flow in acute stroke that minimizes interpretative subjectivity and is robust to severe patient motion.

neuroscience↗

bioRxiv2019

A framework for testing different imputation methods for tabular datasets

Background and purposeHandling missing values is a prevalent challenge in the analysis of clinical data. The rise of data-driven models demands an efficient use of the available data. Methods to impute missing values are thus crucial. Here, we developed a publicly available framework to test different imputation methods and compared their impact in a typical stroke clinical dataset as a use case.\n\nMethodsA clinical dataset based on the 1000Plus stroke study with 380 completed-entries patients was used. 13 common clinical parameters including numerical and categorical values were selected. Missing values in a missing-at-random (MAR) and missing-completely-at-random (MCAR) fashion from 0% to 60% were simulated and consequently imputed using the mean, hot-deck, multiple imputation by chained equations, expectation maximization method and listwise deletion. The performance was assessed by the root mean squared error, the absolute bias and the performance of a linear model for discharge mRS prediction.\n\nResultsListwise deletion was the worst performing method and started to be significantly worse than any imputation method from 2% (MAR) and 3% (MCAR) missing values on. The underlying missing value mechanism seemed to have a crucial influence on the identified best performing imputation method. Consequently no single imputation method outperformed all others. A significant performance drop of the linear model started from 11% (MAR+MCAR) and 18% (MCAR) missing values.\n\nConclusionsIn the presented case study of a typical clinical stroke dataset we confirmed that listwise deletion should be avoided for dealing with missing values. Our findings indicate that the underlying missing value mechanism and other dataset characteristics strongly influence the best choice of imputation method. For future studies with similar data structure, we thus suggest to use the developed framework in this study to select the most suitable imputation method for a given dataset prior to analysis.

neuroscience↗

bioRxiv2018

The impact of ischemic stroke on connectivity gradients

Understanding the relationship between localized anatomical damage, reorganization, and functional deficits is a major challenge in stroke research. Previous work has shown that localized lesions cause widespread functional connectivity alterations in structurally intact areas, thereby affecting a whole network of interconnected regions. Recent advances suggest an alternative to discrete functional networks by describing a connectivity space based on a low-dimensional embedding of the full connectivity matrix. The dimensions of this space, described as connectivity gradients, capture the similarity of areas connections along a continuous space. Here, we defined a three-dimensional connectivity space template based on functional connectivity data from healthy controls. By projecting lesion locations into this space, we demonstrate that ischemic strokes resulted in dimension-specific alterations in functional connectivity over the first week after symptoms onset. Specifically, changes in functional connectivity were captured along connectivity Gradients 1 and 3. The degree of change in functional connectivity was determined by the distance from the lesion along these connectivity gradients regardless of the anatomical distance from the lesion. Together, these results provide a novel framework to study reorganization after stroke and suggest that, rather than only impacting on anatomically proximate areas, the indirect effects of ischemic strokes spread along the brain relative to the space defined by its connectivity.

neuroscience↗