Biology subjects

Scheffler, K.

Publications and source records attributed to Scheffler, K..

40 records · Page 3Linked to original sources

bioRxiv2018

Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain

Eigenvector centrality mapping (ECM) is a popular technique for analyzing fMRI data of the human brain. It is used to obtain maps of functional hubs in networks of the brain in a manner similar to Googles PageRank algorithm. ECM attributes a score to the time course of each voxel that reflects its centrality within the network. Voxels that are strongly correlated with many other voxels that are themselves strongly correlated with other voxels receive high scores. Currently, there exist two different implementations ECM, one of which is very fast but limited to one particular type of correlation metric whose interpretation can be problematic. The second implementation supports many different metrics, but it is computationally costly and requires a very large main memory. Here we propose two new implementations of the ECM approach that resolve these issues. The first is based on a new correlation metric that we call \"ReLU correlation (RLC)\". The second method is based on matrix projections. We demonstrate the use of both techniques on standard fMRI data, as well as on high-resolution fMRI data acquired at 9.4 Tesla.

neuroscience↗

bioRxiv2018

Feasibility of Functional MRI at Ultralow Magnetic Field via Changes in Cerebral Blood Volume

We investigate the feasibility of performing functional MRI (fMRI) at ultralow field (ULF) with a Superconducting QUantum Interference Device (SQUID), as used for detecting magnetoencephalography (MEG) signals from the human head. While there is negligible magnetic susceptibility variation to produce blood oxygenation level-dependent (BOLD) contrast at ULF, changes in cerebral blood volume (CBV) may be a sensitive mechanism for fMRI given the five-fold spread in spin-lattice relaxation time (T1) values across the constituents of the human brain. We undertook simulations of functional signal strength for a simplified brain model involving activation of a primary cortical region in a manner consistent with a blocked task experiment. Our simulations involve measured values of T1 at ULF and experimental parameters for the performance of an upgraded ULFMRI scanner. Under ideal experimental conditions we predict a functional signal-to-noise ratio of between 3.1 and 7.1 for an imaging time of 30 minutes, or between 1.5 and 3.5 for a blocked task experiment lasting 7.5 minutes. Our simulations suggest it may be feasible to perform fMRI using a ULFMRI system designed to perform MRI and MEG in situ.

neuroscience↗

bioRxiv2017

Strelka2: Fast and accurate variant calling for clinical sequencing applications

We describe Strelka2 (https://github.com/Illumina/strelka), an open-source small variant calling method for clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model based estimation of indel error parameters from each sample, an efficient tiered haplotype modeling strategy and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperforms current leading tools on both variant calling accuracy and compute cost.

bioinformatics↗

bioRxiv2017

Inflated False Negative Rates Undermine Reproducibility In Task-Based fMRI

Reproducibility is generally regarded as a hallmark of scientific validity. It can be undermined by two very different factors, namely inflated false positive rates or inflated false negative rates. Here we investigate the role of the second factor, i.e. the degree to which true effects are not detected reliably. The availability of large public databases and also supercomputing allows us to tackle this problem quantitatively. Specifically, we estimated the reproducibility in task-based fMRI data over different samples randomly drawn from a large cohort of subjects obtained from the Human Connectome Project. We use the full cohort as a standard of reference to approximate true positive effects, and compute the fraction of those effects that was detected reliably using standard software packages at various smaller sample sizes. We found that with standard sample sizes this fraction was less than 25 percent. We conclude that inflated false negative rates are a major factor that undermine reproducibility. We introduce a new statistical inference algorithm based on a novel test statistic and show that it improves reproducibility without inflating false positive rates.

neuroscience↗