Zurawski, J. | Search bioRxiv

bioRxiv2023

Deconfounded Dimension Reduction via Partial Embeddings

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. Our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

neuroscience↗

bioRxiv2022

Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis

Background/PurposeMulticenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at 4 sites. Materials and MethodsImages were acquired on Siemens or Philips scanners at 3-tesla. Automated white matter lesion detection and whole brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 and T2 (FLAIR) lesions. Random effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites. ResultsRandom effect modeling demonstrated model assumption violations for most comparisons of interest. Non-parametric modeling indicated that site explained > 50% of the variation for most estimated volumes. This expanded to > 75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intra-scanner differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact. ConclusionDifferences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating inter-scanner differences show promise in reducing biases in multi-center studies of MS.

neuroscience↗