424 research outputs found
Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model
Typical fMRI studies have focused on either the mean trend in the
blood-oxygen-level-dependent (BOLD) time course or functional connectivity
(FC). However, other statistics of the neuroimaging data may contain important
information. Despite studies showing links between the variance in the BOLD
time series (BV) and age and cognitive performance, a formal framework for
testing these effects has not yet been developed. We introduce the Variance
Design General Linear Model (VDGLM), a novel framework that facilitates the
detection of variance effects. We designed the framework for general use in any
fMRI study by modeling both mean and variance in BOLD activation as a function
of experimental design. The flexibility of this approach allows the VDGLM to i)
simultaneously make inferences about a mean or variance effect while
controlling for the other and ii) test for variance effects that could be
associated with multiple conditions and/or noise regressors. We demonstrate the
use of the VDGLM in a working memory application and show that engagement in a
working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure
SAFS: A Deep Feature Selection Approach for Precision Medicine
In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others
The comparison of two Zagreb-Fermat eccentricity indices
In this paper, we focus on comparing the first and second Zagreb-Fermat
eccentricity indices of graphs. We show that holds for all acyclic and unicyclic graphs.
Besides, we verify that the inequality may not be applied to graphs with at
least two cycles
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Deep convolutional neural networks (CNNs) trained with logistic and softmax
losses have made significant advancement in visual recognition tasks in
computer vision. When training data exhibit class imbalances, the class-wise
reweighted version of logistic and softmax losses are often used to boost
performance of the unweighted version. In this paper, motivated to explain the
reweighting mechanism, we explicate the learning property of those two loss
functions by analyzing the necessary condition (e.g., gradient equals to zero)
after training CNNs to converge to a local minimum. The analysis immediately
provides us explanations for understanding (1) quantitative effects of the
class-wise reweighting mechanism: deterministic effectiveness for binary
classification using logistic loss yet indeterministic for multi-class
classification using softmax loss; (2) disadvantage of logistic loss for
single-label multi-class classification via one-vs.-all approach, which is due
to the averaging effect on predicted probabilities for the negative class
(e.g., non-target classes) in the learning process. With the disadvantage and
advantage of logistic loss disentangled, we thereafter propose a novel
reweighted logistic loss for multi-class classification. Our simple yet
effective formulation improves ordinary logistic loss by focusing on learning
hard non-target classes (target vs. non-target class in one-vs.-all) and turned
out to be competitive with softmax loss. We evaluate our method on several
benchmark datasets to demonstrate its effectiveness.Comment: AAAI2020. Previously this appeared as arXiv:1906.04026v2, which was
submitted as a replacement by acciden
Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks
Understanding human fetal neurodevelopment is of great clinical importance as
abnormal development is linked to adverse neuropsychiatric outcomes after
birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have
provided new insight into development of the human brain before birth, but
these studies have predominately focused on brain functional connectivity (i.e.
Fisher z-score), which requires manual processing steps for feature extraction
from fMRI images. Deep learning approaches (i.e., Convolutional Neural
Networks) have achieved remarkable success on learning directly from image
data, yet have not been applied on fetal fMRI for understanding fetal
neurodevelopment. Here, we bridge this gap by applying a novel application of
deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI
data. Specifically, we test a supervised CNN framework as a data-driven
approach to isolate variation in fMRI signals that relate to younger v.s. older
fetal age groups. Based on the learned CNN, we further perform sensitivity
analysis to identify brain regions in which changes in BOLD signal are strongly
associated with fetal brain age. The findings demonstrate that deep CNNs are a
promising approach for identifying spontaneous functional patterns in fetal
brain activity that discriminate age groups. Further, we discovered that
regions that most strongly differentiate groups are largely bilateral, share
similar distribution in older and younger age groups, and are areas of
heightened metabolic activity in early human development.Comment: 9 page
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