Biology subjects

Markiewicz, C.

Publications and source records attributed to Markiewicz, C..

3 recordsLinked to original sources

bioRxiv2021

High-sensitivity detection of facial features on MRI brain scans with a convolutional network

Platforms and institutions that support MRI data sharing need to ensure that identifiable facial features are not present in shared images. Currently, this assessment requires manual effect as no auto-mated tools exist that can efficiently and accurately detect if an image has been "defaced". The scarcity of publicly available data with pre-served facial features, as well as the meager incentives to create such a cohort privately, have averted the development of face-detection models. Here, we introduce a framework to detect whether an input MRI brain scan has been defaced, with the ultimate goal of streamlining it within the submission protocols of MRI data archiving and sharing platforms. We present a binary (defaced/"nondefaced") classifier based on a custom convolutional neural network architecture. We train the model on 980 de-faced MRI scans from 36 different studies that are publicly available at OpenNeuro.org. To overcome the unavailability of nondefaced examples, we augment the dataset by inpainting synthetic faces into each training image. We show the adequacy of such a data augmentation in a cross-validation evaluation. We demonstrate the performance estimated with cross-validation matches that of an evaluation on a held-out dataset (N =581) preserving real faces, and obtain accuracy/sensitivity/speci-ficity scores of 0.978/0.983/0.972, respectively. Data augmentations are key to boosting the performance of models bounded by limited sample sizes and insufficient diversity. Our model contributes towards developing classifiers with[~] 100% sensitivity detecting faces, which is crucial to ensure that no identifiable data are inadvertently made public.

neuroscience↗

bioRxiv2021

TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references --i.e., templates-- and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR --findable, accessible, interoperable, reusable-- principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

neuroscience↗

bioRxiv2018

FMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed with rapid advances in MR data acquisition and image processing techniques. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection comprising participants from 54 different studies in the OpenfMRI repository. We review the distinctive features of fMRIPrep in a qualitative comparison to other preprocessing workflows. We demonstrate that fMRIPrep achieves higher spatial accuracy as it introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.

bioinformatics↗