492 research outputs found
ciftiTools: A package for reading, writing, visualizing and manipulating CIFTI files in R
Surface- and grayordinate-based analysis of MR data has well-recognized
advantages, including improved whole-cortex visualization, the ability to
perform surface smoothing to avoid issues associated with volumetric smoothing,
improved inter-subject alignment, and reduced dimensionality. The CIFTI
grayordinate file format introduced by the Human Connectome Project further
advances grayordinate-based analysis by combining gray matter data from the
left and right cortical hemispheres with gray matter data from the subcortex
and cerebellum into a single file. Analyses performed in grayordinate space are
well-suited to leverage information shared across the brain and across subjects
through both traditional analysis techniques and more advanced statistical
methods, including Bayesian methods. The R statistical environment facilitates
use of advanced statistical techniques, yet little support for grayordinates
analysis has been previously available in R. Indeed, few comprehensive
programmatic tools for working with CIFTI files have been available in any
language. Here, we present the ciftiTools R package, which provides a unified
environment for reading, writing, visualizing, and manipulating CIFTI files and
related data formats. We illustrate ciftiTools' convenient and user-friendly
suite of tools for working with grayordinates and surface geometry data in R,
and we describe how ciftiTools is being utilized to advance the statistical
analysis of grayordinate-based functional MRI data.Comment: 41 pages, 6 figure
Statistical Methods for Functional Magnetic Resonance Imaging Data
Understanding how the brain functions is one of the most important goals in science and medicine today. Functional magnetic resonance imaging (fMRI) is a noninvasive, widely used technology for studying brain function in humans. While fMRI has great potential to shed light on cognitive development, decline and disorders, it also presents statistical and computational challenges due to a myriad of sources of noise and the large size of the data. In this thesis, I propose several methods to improve the analysis of resting-state fMRI, which is used to understand connectivity between different regions of the brain. Specifically, this thesis addresses two primary themes. First, I propose shrinkage estimators for functional connectivity, which improve reliability of subject-level estimates by "borrowing strength" across subjects. Second, I propose a method of identifying artifacts in fMRI data through a novel high-dimensional outlier detection method. The proposed methods can be used together and have the potential to significantly improve our understanding of brain connectivity at the subject level using resting-state fMRI
Fast Bayesian estimation of brain activation with cortical surface fMRI data using EM
Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging
data used to identify areas of the brain that activate during specific tasks or
stimuli. These data are conventionally modeled using a massive univariate
approach across all data locations, which ignores spatial dependence at the
cost of model power. We previously developed and validated a spatial Bayesian
model leveraging dependencies along the cortical surface of the brain in order
to improve accuracy and power. This model utilizes stochastic partial
differential equation spatial priors with sparse precision matrices to allow
for appropriate modeling of spatially-dependent activations seen in the
neuroimaging literature, resulting in substantial increases in model power. Our
original implementation relies on the computational efficiencies of the
integrated nested Laplace approximation (INLA) to overcome the computational
challenges of analyzing high-dimensional fMRI data while avoiding issues
associated with variational Bayes implementations. However, this requires
significant memory resources, extra software, and software licenses to run. In
this article, we develop an exact Bayesian analysis method for the general
linear model, employing an efficient expectation-maximization algorithm to find
maximum a posteriori estimates of task-based regressors on cortical surface
fMRI data. Through an extensive simulation study of cortical surface-based fMRI
data, we compare our proposed method to the existing INLA implementation, as
well as a conventional massive univariate approach employing ad-hoc spatial
smoothing. We also apply the method to task fMRI data from the Human Connectome
Project and show that our proposed implementation produces similar results to
the validated INLA implementation. Both the INLA and EM-based implementations
are available through our open-source BayesfMRI R package.Comment: 29 pages, 10 figures. arXiv admin note: text overlap with
arXiv:2203.0005
A robust multivariate, non-parametric outlier identification method for scrubbing in fMRI
Functional magnetic resonance imaging (fMRI) data contain high levels of
noise and artifacts. To avoid contamination of downstream analyses, fMRI-based
studies must identify and remove these noise sources prior to statistical
analysis. One common approach is the "scrubbing" of fMRI volumes that are
thought to contain high levels of noise. However, existing scrubbing techniques
are based on ad hoc measures of signal change. We consider scrubbing via
outlier detection, where volumes containing artifacts are considered
multidimensional outliers. Robust multivariate outlier detection methods are
proposed using robust distances (RDs), which are related to the Mahalanobis
distance. These RDs have a known distribution when the data are i.i.d. normal,
and that distribution can be used to determine a threshold for outliers where
fMRI data violate these assumptions. Here, we develop a robust multivariate
outlier detection method that is applicable to non-normal data. The objective
is to obtain threshold values to flag outlying volumes based on their RDs. We
propose two threshold candidates that embark on the same two steps, but the
choice of which depends on a researcher's purpose. Our main steps are dimension
reduction and selection, robust univariate outlier imputation to get rid of the
effect of outliers on the distribution, and estimating an outlier threshold
based on the upper quantile of the RD distribution without outliers. The first
threshold candidate is an upper quantile of the empirical distribution of RDs
obtained from the imputed data. The second threshold candidate calculates the
upper quantile of the RD distribution that a nonparametric bootstrap uses to
account for uncertainty in the empirical quantile. We compare our proposed fMRI
scrubbing method to motion scrubbing, data-driven scrubbing, and restrictive
parametric multivariate outlier detection methods
Modelo de jerarquización para determinar el tipo de mantenimiento que incremente la disponibilidad mecánica de molinos de bolas en Compañía Minera Santa Luisa
Este trabajo de investigación se llevó a cabo en la planta concentradora de la Minera Santa Luisa, ubicada en la localidad de Huanzalá, distrito de Huallanca, provincia de Bolognesi y departamento de Ancash, posee diversos equipos críticos, siendo objeto de este estudio los molinos de bolas, los cuales están compuestos por sistemas de transmisión eléctrica, lubricación, inching drive (auxiliar), carga y descarga. El problema en que nos enfocamos son las paradas inesperadas que conllevan al decrecimiento de disponibilidad mecánica de los aparatos en cuestión. Esta investigación es de tipo básico y nivel descriptivo, ya que se determinó las fallas recurrentes del sistema mecánico, para luego hacer un análisis de las mismas (AMEF). Mediante la matriz AHP se pudo determinar que la metodología RCM es el mantenimiento ideal para este tipo de fallas, el cual mediante mejora continua permitió el incremento de disponibilidad
Improving Reliability of Subject-Level Resting-State fMRI Parcellation with Shrinkage Estimators
A recent interest in resting state functional magnetic resonance imaging
(rsfMRI) lies in subdividing the human brain into anatomically and functionally
distinct regions of interest. For example, brain parcellation is often used for
defining the network nodes in connectivity studies. While inference has
traditionally been performed on group-level data, there is a growing interest
in parcellating single subject data. However, this is difficult due to the low
signal-to-noise ratio of rsfMRI data, combined with typically short scan
lengths. A large number of brain parcellation approaches employ clustering,
which begins with a measure of similarity or distance between voxels. The goal
of this work is to improve the reproducibility of single-subject parcellation
using shrinkage estimators of such measures, allowing the noisy
subject-specific estimator to "borrow strength" in a principled manner from a
larger population of subjects. We present several empirical Bayes shrinkage
estimators and outline methods for shrinkage when multiple scans are not
available for each subject. We perform shrinkage on raw intervoxel correlation
estimates and use both raw and shrinkage estimates to produce parcellations by
performing clustering on the voxels. Our proposed method is agnostic to the
choice of clustering method and can be used as a pre-processing step for any
clustering algorithm. Using two datasets---a simulated dataset where the true
parcellation is known and is subject-specific and a test-retest dataset
consisting of two 7-minute rsfMRI scans from 20 subjects---we show that
parcellations produced from shrinkage correlation estimates have higher
reliability and validity than those produced from raw estimates. Application to
test-retest data shows that using shrinkage estimators increases the
reproducibility of subject-specific parcellations of the motor cortex by up to
30%.Comment: body 21 pages, 11 figure
Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors
Large brain imaging databases contain a wealth of information on brain
organization in the populations they target, and on individual variability.
While such databases have been used to study group-level features of
populations directly, they are currently underutilized as a resource to inform
single-subject analysis. Here, we propose leveraging the information contained
in large functional magnetic resonance imaging (fMRI) databases by establishing
population priors to employ in an empirical Bayesian framework. We focus on
estimation of brain networks as source signals in independent component
analysis (ICA). We formulate a hierarchical "template" ICA model where source
signals---including known population brain networks and subject-specific
signals---are represented as latent variables. For estimation, we derive an
expectation maximization (EM) algorithm having an explicit solution. However,
as this solution is computationally intractable, we also consider an
approximate subspace algorithm and a faster two-stage approach. Through
extensive simulation studies, we assess performance of both methods and compare
with dual regression, a popular but ad-hoc method. The two proposed algorithms
have similar performance, and both dramatically outperform dual regression. We
also conduct a reliability study utilizing the Human Connectome Project and
find that template ICA achieves substantially better performance than dual
regression, achieving 75-250% higher intra-subject reliability
Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation
In task fMRI analysis, OLS is typically used to estimate task-induced
activation in the brain. Since task fMRI residuals often exhibit temporal
autocorrelation, it is common practice to perform prewhitening prior to OLS to
satisfy the assumption of residual independence, equivalent to GLS. While
theoretically straightforward, a major challenge in prewhitening in fMRI is
accurately estimating the residual autocorrelation at each location of the
brain. Assuming a global autocorrelation model, as in several fMRI software
programs, may under- or over-whiten particular regions and fail to achieve
nominal false positive control across the brain. Faster multiband acquisitions
require more sophisticated models to capture autocorrelation, making
prewhitening more difficult. These issues are becoming more critical now
because of a trend towards subject-level analysis, where prewhitening has a
greater impact than in group-average analyses. In this article, we first
thoroughly examine the sources of residual autocorrelation in multiband task
fMRI. We find that residual autocorrelation varies spatially throughout the
cortex and is affected by the task, the acquisition method, modeling choices,
and individual differences. Second, we evaluate the ability of different
AR-based prewhitening strategies to effectively mitigate autocorrelation and
control false positives. We find that allowing the prewhitening filter to vary
spatially is the most important factor for successful prewhitening, even more
so than increasing AR model order. To overcome the computational challenge
associated with spatially variable prewhitening, we developed a computationally
efficient R implementation based on parallelization and fast C++ backend code.
This implementation is included in the open source R package BayesfMRI.Comment: 26 pages with 1 page of appendix, 11 figures with 1 figure of
supplementary figur
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