371 research outputs found
PATH AND DIRECTION DISCOVERY IN INDIVIDUAL DYNAMIC MODELS: A REGULARIZED HYBRID UNIFIED STRUCTURAL EQUATION MODELING WITH LATENT VARIABLE
There recently has been growing interest in the study of psychological and neurologicalprocesses at an individual level. One goal in such endeavors is to construct person-specific dynamicassessments using time series techniques such as Vector Autoregressive (VAR) models. Within thepsychometric field, researchers have developed psychometric modeling frameworks to estimatedifferent variants of VAR models. These modeling frameworks estimate the dynamic relations (e.g.,temporal and contemporaneous) unpacked in a multivariate time series data. However, two problemsexist with current VAR specifications: 1) VAR models are restricted in that contemporaneousrelations are typically modeled either as undirected relations among residuals or directed relationsamong observed variables, but not both; 2) current estimation frameworks are limited by thereliance on stepwise model building procedures. This study adopts a new modeling approach, i.e.,LASSO regularized hybrid unified Structural Equation Model (SEM), for a global search andestimation of a more flexible VAR representation. The present study to our knowledge is the firstapplication of the recently developed regularized SEM technique to the estimation of a type of timeseries SEM, which points to a promising future for statistical learning in psychometric models.Doctor of Philosoph
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable
preprocessing steps in neuroimaging studies. The aim is to extract the brain
from raw imaging scans (i.e., extraction step), align it with a target brain
image (i.e., registration step) and label the anatomical brain regions (i.e.,
segmentation step). Conventional studies typically focus on developing separate
methods for the extraction, registration and segmentation tasks in a supervised
setting. The performance of these methods is largely contingent on the quantity
of training samples and the extent of visual inspections carried out by experts
for error correction. Nevertheless, collecting voxel-level labels and
performing manual quality control on high-dimensional neuroimages (e.g., 3D
MRI) are expensive and time-consuming in many medical studies. In this paper,
we study the problem of one-shot joint extraction, registration and
segmentation in neuroimaging data, which exploits only one labeled template
image (a.k.a. atlas) and a few unlabeled raw images for training. We propose a
unified end-to-end framework, called JERS, to jointly optimize the extraction,
registration and segmentation tasks, allowing feedback among them.
Specifically, we use a group of extraction, registration and segmentation
modules to learn the extraction mask, transformation and segmentation mask,
where modules are interconnected and mutually reinforced by self-supervision.
Empirical results on real-world datasets demonstrate that our proposed method
performs exceptionally in the extraction, registration and segmentation tasks.
Our code and data can be found at https://github.com/Anonymous4545/JERSComment: Published as a research track paper at KDD 2023. Code:
https://github.com/Anonymous4545/JER
Cognitive Profiles and Hub Vulnerability in Parkinson's Disease
The clinicopathological correlations between aspects of cognition, disease severity and imaging in Parkinson's Disease (PD) have been unclear. We studied cognitive profiles, demographics, and functional connectivity patterns derived from resting-state fMRI data (rsFC) in 31 PD subjects from the Parkinson's Progression Markers Initiative (PPMI) database. We also examined rsFC from 19 healthy subjects (HS) from the Pacific Parkinson's Research Centre. Graph theoretical measures were used to summarize the rsFC patterns. Canonical correlation analysis (CCA) was used to relate separate cognitive profiles in PD that were associated with disease severity and demographic measures as well as rsFC network measures. The CCA model relating cognition to demographics suggested female gender and education supported cognitive function in PD, age and depression scores were anti-correlated with overall cognition, and UPDRS had little influence on cognition. Alone, rsFC global network measures did not significantly differ between PD and controls, yet some nodal network measures, such as network segregation, were distinguishable between PD and HS in cortical âhubâ regions. The CCA model relating cognition to rsFC global network values, which was not related to the other CCA model relating cognition to demographic information, suggested modularity, rich club coefficient, and transitivity was also broadly related to cognition in PD. Our results suggest that education, aging, comorbidity, and gender impact cognition more than overall disease severity in PD. Cortical âhubâ regions are vulnerable in PD, and impairments of processing speed, attention, scanning abilities, and executive skills are related to enhanced functional segregation seen in PD
The Gaussian graphical model in cross-sectional and time-series data
We discuss the Gaussian graphical model (GGM; an undirected network of
partial correlation coefficients) and detail its utility as an exploratory data
analysis tool. The GGM shows which variables predict one-another, allows for
sparse modeling of covariance structures, and may highlight potential causal
relationships between observed variables. We describe the utility in 3 kinds of
psychological datasets: datasets in which consecutive cases are assumed
independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n
= 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In
time-series analysis, the GGM can be used to model the residual structure of a
vector-autoregression analysis (VAR), also termed graphical VAR. Two network
models can then be obtained: a temporal network and a contemporaneous network.
When analyzing data from multiple subjects, a GGM can also be formed on the
covariance structure of stationary means---the between-subjects network. We
discuss the interpretation of these models and propose estimation methods to
obtain these networks, which we implement in the R packages graphicalVAR and
mlVAR. The methods are showcased in two empirical examples, and simulation
studies on these methods are included in the supplementary materials.Comment: Accepted pending revision in Multivariate Behavioral Researc
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
There is a recent trend to leverage the power of graph neural networks (GNNs)
for brain-network based psychiatric diagnosis, which,in turn, also motivates an
urgent need for psychiatrists to fully understand the decision behavior of the
used GNNs. However, most of the existing GNN explainers are either post-hoc in
which another interpretive model needs to be created to explain a well-trained
GNN, or do not consider the causal relationship between the extracted
explanation and the decision, such that the explanation itself contains
spurious correlations and suffers from weak faithfulness. In this work, we
propose a granger causality-inspired graph neural network (CI-GNN), a built-in
interpretable model that is able to identify the most influential subgraph
(i.e., functional connectivity within brain regions) that is causally related
to the decision (e.g., major depressive disorder patients or healthy controls),
without the training of an auxillary interpretive network. CI-GNN learns
disentangled subgraph-level representations {\alpha} and \b{eta} that encode,
respectively, the causal and noncausal aspects of original graph under a graph
variational autoencoder framework, regularized by a conditional mutual
information (CMI) constraint. We theoretically justify the validity of the CMI
regulation in capturing the causal relationship. We also empirically evaluate
the performance of CI-GNN against three baseline GNNs and four state-of-the-art
GNN explainers on synthetic data and three large-scale brain disease datasets.
We observe that CI-GNN achieves the best performance in a wide range of metrics
and provides more reliable and concise explanations which have clinical
evidence.Comment: 45 pages, 13 figure
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