Biology subjects

Palacios, E. M.

Publications and source records attributed to Palacios, E. M..

3 recordsLinked to original sources

bioRxiv2021

Toward Optimized and Predictive Connectomics at Scale

Probabilistic MRI diffusion tractography is a sophisticated technique to investigate structural connectomes, but its steep computational cost prevents application to broader research and clinical settings. Major speedup can be achieved by reducing the number of tractography streamlines. To ensure this does not degrade connectome quality, we calculate the identifiability of connectomes between test and retest MRI as a proxy for information content. We find that reducing streamline count by up to two orders of magnitude from prevailing levels in literature has no significant impact on identifiability. Incidentally, we also observe that Jaccard similarity is more effective than Pearson correlation in achieving identifiability. This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.

neuroscience↗

bioRxiv2020

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational cost. We developed MaPPeRTrac, a probabilistic tractography pipeline that simplifies and vastly accelerates this process on a wide range of high performance computing (HPC) environments. It fully automates the entire tractography pipeline, from management of raw MRI machine data to edge density imaging (EDI) of the structural connectome. Dependencies are containerized with Docker or Singularity and de-coupled from code to enable rapid proto-typing and modification. Data artifacts are strictly organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming frame-work, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so that it may accelerate brain connectome research for a broader user community.

neuroscience↗

bioRxiv2020

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

The human brain exhibits hierarchical modular organization, which is not depicted by conventional fMRI functional connectivity reconstruction methods such as independent component analysis (ICA). To map hierarchical brain connectivity networks (BCNs), we propose a novel class of deep (multilayer) linear models that are constructed such that each successive layer decomposes the features of the preceding layer. Three of these are multilayer variants of Sparse Dictionary Learning (SDL), Non-Negative Matrix Factorization (NMF) and Fast ICA (FICA). We present a fourth deep linear model, Deep Matrix Fitting (MF), which incorporates both rank reduction for data-driven hyperparameter determination as well as a distributed optimization function. We also introduce a novel framework for theoretical comparison of these deep linear models based on their combination of mathematical operators, the predictions of which are tested using simulated resting state fMRI data with known ground truth BCNs. Consistent with the theoretical predictions, Deep MF and Deep SDL performed best for connectivity estimation of 1st layer networks, whereas Deep FICA and Deep NMF were modestly better for spatial mapping. Deep MF provided the best overall performance, including computational speed. These deep linear models can efficiently map hierarchical BCNs without requiring the manual hyperparameter tuning, extensive fMRI training data or high-performance computing infrastructure needed by deep nonlinear models, such as convolutional neural networks (CNNs) or deep belief networks (DBNs), and their results are also more explainable from their mathematical structure. These benefits gain in importance as continual improvements in the spatial and temporal resolution of fMRI reveal more of the hierarchy of spatiotemporal brain architecture. These new models of hierarchical BCNs may also advance the development of fMRI diagnostic and prognostic biomarkers, given the recent recognition of disparities between low-level vs high-level network connectivity across a wide range of neurological and psychiatric disorders.

neuroscience↗