Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and... more
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of ...
Research Interests:
Tremor in Parkinson’s disease (PD) has distinct responsiveness to dopamine, which is supposed not be exclusively related to dopamine deficiency but has a close relationship with cholinergic system. This phenomenon indicates that... more
Tremor in Parkinson’s disease (PD) has distinct responsiveness to dopamine, which is supposed not be exclusively related to dopamine deficiency but has a close relationship with cholinergic system. This phenomenon indicates that cholinergic system may be an important regulatory for distinct dopamine responsiveness of parkinsonian tremor. Through investigating the alterations of cholinergic and dopaminergic network during levodopa administration, we aimed at exploring the mechanisms of differed dopamine responsiveness of parkinsonian tremor. Fifty-two PD patients with tremor were enrolled. MRI scanning, UPDRS III and its sub-symptom scores were collected in OFF and ON status (dopaminergic challenge test). Then, patients were divided into two groups (dopamine-resistant tremor and dopamine-responsive tremor) according to the tremor change rate median score. Dopaminergic and cholinergic network were obtained. LASSO regression was conducted to identify functional connectivity with distin...
Research Interests:
We present a new estimator for precision matrix in high dimensional Gaussian graphical models. At the core of the proposed estimator is a collection of node-wise linear regression with nonconvex penalty. In contrast to existing estimators... more
We present a new estimator for precision matrix in high dimensional Gaussian graphical models. At the core of the proposed estimator is a collection of node-wise linear regression with nonconvex penalty. In contrast to existing estimators for Gaussian graphical models with O(s √ log d/n) estimation error bound in terms of spectral norm, where s is the maximum degree of a graph, the proposed estimator could attain O(s/ √ n+ √ log d/n) spectral norm based convergence rate in the best case, and it is no worse than exiting estimators in general. In addition, our proposed estimator enjoys the oracle property under a milder condition than existing estimators. We show through extensive experiments on both synthetic and real datasets that our estimator outperforms the state-of-the-art estimators.
Research Interests:
We propose an accelerated stochastic block coordinate descent algorithm for nonconvex optimization under sparsity constraint in the high dimensional regime. The core of our algorithm is leveraging both stochastic partial gradient and full... more
We propose an accelerated stochastic block coordinate descent algorithm for nonconvex optimization under sparsity constraint in the high dimensional regime. The core of our algorithm is leveraging both stochastic partial gradient and full partial gradient restricted to each coordinate block to accelerate the convergence. We prove that the algorithm converges to the unknown true parameter at a linear rate, up to the statistical error of the underlying model. Experiments on both synthetic and real datasets backup our theory.
Research Interests:
We study the estimation of the latent variable Gaussian graphical model (LVGGM), where the precision matrix is the superposition of a sparse matrix and a low-rank matrix. In order to speed up the estimation of the sparse plus low-rank... more
We study the estimation of the latent variable Gaussian graphical model (LVGGM), where the precision matrix is the superposition of a sparse matrix and a low-rank matrix. In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization and an efficient alternating gradient descent algorithm with hard thresholding to solve it. Our algorithm is orders of magnitude faster than the convex relaxation based methods for LVGGM. In addition, we prove that our algorithm is guaranteed to linearly converge to the unknown sparse and low-rank components up to the optimal statistical precision. Experiments on both synthetic and genomic data demonstrate the superiority of our algorithm over the state-of-the-art algorithms and corroborate our theory.
Research Interests:
Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,”... more
Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,” existing approaches for Granger causal inference in high-dimensional data lack the means to characterize the uncertainty associated with Granger causality estimates (e.g., p-values and confidence intervals). We make two contributions in this work. First, we introduce a novel asymptotically unbiased Granger causality estimator with corresponding test statistics and confidence intervals to allow, for the first time, uncertainty characterization in high-dimensional Granger causal inference. Second, we introduce a novel method for false discovery rate control that achieves higher power in multiple testing than existing techniques and that can cope with dependent test statistics and dependent observations. We corroborate our theoretical results with experime...
Research Interests:
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and... more
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare mo...
Research Interests:
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed... more
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.
Research Interests:
We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR... more
We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (). In particular, the forecasts based on our model suggest that the peak date of the US, New York state, and California state are 06/01/2020, 05/10/2020, and 07/01/2020 respectively. In addition, the estimatedof the US, New York state, and California state are 2.5, 3.6 and 2.2 respectively. The prediction results for all states in the US can be found on our project website:https://covid19.uclaml.org, which are...
Research Interests:
Rapid eye movement sleep behavior disorder (RBD) has a strong association with alpha synucleinpathies such as Parkinson's disease (PD) and PD patients with RBD tend to have a poorer prognosis. However, we still know little about the... more
Rapid eye movement sleep behavior disorder (RBD) has a strong association with alpha synucleinpathies such as Parkinson's disease (PD) and PD patients with RBD tend to have a poorer prognosis. However, we still know little about the pathogenesis of RBD in PD. Therefore, we aim to detect the alterations of structural correlation network (SCN) in PD patients with and without RBD. A total of 191 PD patients, including 51 patients with possible RBD (pRBD) and 140 patients with non-possible RBD, and 76 normal controls were included in the present study. Structural brain networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches. There was no difference in global properties among the three groups. Significant enhanced regional nodal measures in limbic system, frontal-temporal regions, and occipital regions and decreased nodal measures in cerebellum were found in PD patients with pRBD (PD-pRBD) compar...
Research Interests:
The relationship between cerebrospinal fluid (CSF) proteins and brain function in Parkinson's disease (PD) is not explained clearly. We investigated the correlations between CSF proteins and spontaneous neuronal activity in PD... more
The relationship between cerebrospinal fluid (CSF) proteins and brain function in Parkinson's disease (PD) is not explained clearly. We investigated the correlations between CSF proteins and spontaneous neuronal activity in PD patients via fractional amplitude of low-frequency fluctuation (fALFF) using the Parkinson's Progression Markers Initiative database. Twenty-eight PD patients underwent resting-state functional magnetic resonance imaging in "off" status and lumbar puncture within a month. Correlation analyses between CSF proteins and fALFF value in whole brain as well as clinical assessment scores were performed. We found CSF total tau (t-tau) level was negatively correlated with fALFF in posterior cingulate gyrus. And fALFF in posterior cingulate gyrus was positively correlated with Hopkins Verbal Learning Test-Revised recognition discrimination index. Besides, alpha-synuclein (α-syn) level was correlated with fALFF in bilateral inferior frontal gyrus. This ...
Research Interests:
This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks,... more
This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare dif...
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Background Cancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical... more
Background Cancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering) to identify subtypes based on gene expression data. The network-level interaction among genes, which is key to understanding the molecular perturbations in cancer, has been rarely considered during the clustering process. The motivation of our work is to develop a method that effectively incorporates molecular interaction networks into the clustering process to improve cancer subtype identification. Results We have developed a new clustering algorithm for cancer subtype identification, called “network-assisted co-clustering for the identification of cancer subtypes” (NCIS). NCIS combines gene network information to simultaneously group samples and genes into biologically meaningful clusters. Prior to clustering, we assign weights to genes ba...
Research Interests:
Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large‐scale dynamic integration and segregation of the... more
Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large‐scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting‐state blood‐oxygen‐level‐dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical–cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that co...
Research Interests:
Research Interests: Biomedical Engineering, Magnetic Resonance Imaging, Biomarkers, Medicine, Molecular Imaging, and 15 moreBrain, Humans, Female, Male, Iron, Substantia nigra, Clinical Sciences, Middle Aged, Globus Pallidus, Reproducibility of Results, Disease Progression, Sensitivity and Specificity, Quantitative Susceptibility Mapping, Parkinson Disease, and Red nucleus
Research Interests:
Research Interests:
Research Interests:
Research Interests: Psychology, Cognitive Science, Magnetic Resonance Imaging, Medicine, Brain, and 14 moreHumans, Female, Voxel Based Morphometry, Male, Aged, Middle Aged, Functional Neuroimaging, Disease Progression, Voxel, Neurosciences, Gray Matter, Parkinson Disease, Case Control Studies, and Severity of Illness Index
The progression of Parkinson's disease (PD) seems to vary according to the disease stage, which greatly influences the management of PD patients. However, the underlying mechanism of progression in PD remains unclear. This study was... more
The progression of Parkinson's disease (PD) seems to vary according to the disease stage, which greatly influences the management of PD patients. However, the underlying mechanism of progression in PD remains unclear. This study was designed to explore the progressive pattern of iron accumulation at different stages in PD patients. Sixty right-handed PD patients and 40 normal controls were recruited. According to the disease stage, 45 patients with Hoehn-Yahr stage ≤ 2.5 and 15 patients with Hoehn-Yahr stage ≥ 3 were grouped into early-stage PD (EPD) and late-stage PD (LPD) groups, respectively. The iron content in the cardinal subcortical nuclei covering the cerebrum, cerebellum and midbrain was measured using quantitative susceptibility mapping (QSM). The substantia nigra pars compacta (SNc) showed significantly increased QSM values in the EPD patients compared with the controls. In the LPD patients, while the SNc continued to show increased QSM values compared with the contro...
Research Interests: Biomedical Engineering, Magnetic Resonance Imaging, Biomarkers, Medicine, Molecular Imaging, and 15 moreBrain, Humans, Female, Male, Iron, Substantia nigra, Clinical Sciences, Middle Aged, Globus Pallidus, Reproducibility of Results, Disease Progression, Sensitivity and Specificity, Quantitative Susceptibility Mapping, Parkinson Disease, and Red nucleus
Research Interests:
Research Interests:
Research Interests:
Hemiparkinsonism duration in patients with Parkinson's disease (PD) is a key time window to study early pathology of PD. We aimed to comprehensively explore the alterations of deformation and structural network in PD patients with... more
Hemiparkinsonism duration in patients with Parkinson's disease (PD) is a key time window to study early pathology of PD. We aimed to comprehensively explore the alterations of deformation and structural network in PD patients with hemiparkinsonism, which could potentially disclose the early biomarker for PD. Thirty-one PD patients with hemiparkinsonism and 37 age- and gender- matched normal controls were included in the present study. First of all, we normalized the left hemisphere of structural images as the contralateral side to the affected limbs. Deformation-based morphometry (DBM) was conducted to evaluate the brain atrophy and/or enlargement. structural networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches (e.g., small-worldness, global, and nodal measures). Significantly decreased deformation values were observed in the temporoparietal regions like bilateral middle temporal gyri, ip...
Research Interests:
Although a substantial body of previous functional magnetic resonance imaging (fMRI) studies have revealed different brain responses to external stimuli in chronic cigarette smokers compared with nonsmokers, only a few studies assessed... more
Although a substantial body of previous functional magnetic resonance imaging (fMRI) studies have revealed different brain responses to external stimuli in chronic cigarette smokers compared with nonsmokers, only a few studies assessed brain spontaneous activity in the resting state in chronic smokers. The aim of this study was to investigate alterations of brain activity during the resting state in chronic smokers using fractional amplitude of low-frequency fluctuation (fALFF). In the present study, 55 smokers and 49 healthy nonsmokers were included. All the subjects underwent resting-state fMRI scans and the data were analyzed by the fALFF approach. The smokers showed significantly decreased fALFF in the left precuneus, right inferior temporal and occipital gyrus(ITG/IOG), while significantly increased fALFF in the right caudate. Subsequent correlation analysis revealed that the fALFF values of the left precuneus and right ITG/IOG were positively correlated with years of smoking a...
Research Interests:
Research Interests:
Background Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease (PD) across early stage to... more
Background Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01–0.027 Hz; slow-4: 0.027–0.073 Hz; slow-3: 0.073–0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies whil...
Research Interests:
It is well known that disruption of basal ganglia function generates the motor symptoms in PD, however, these are presented in a heterogeneous manner; patients can be divided into tremor-dominant and akinesia/rigidity-dominant subtypes.... more
It is well known that disruption of basal ganglia function generates the motor symptoms in PD, however, these are presented in a heterogeneous manner; patients can be divided into tremor-dominant and akinesia/rigidity-dominant subtypes. To date, it is unknown if these differences in the motor symptoms could be explained by differences on the functional connectivity of basal ganglia with specific brain regions. In this study, we aimed to explore the alterations of the network-based and global functional connectivity linking to basal ganglia between the PD-TD and PD-AR patients. One hundred and six PD patients and 52 normal controls were recruited. According to the subscales of UPDRS motor scale, PD patients were divided into the PD-TD (n = 57) and PD-AR (n = 49) subtypes. We performed independent component analysis to identify basal ganglia network (BGN) involving connected brain regions having coactivation with basal ganglia. Eigenvector centrality mapping were processed and the eig...
Research Interests:
Background: Patients with the postural instability and gait difficulty (PIGD) subtype of Parkinson disease (PD) are at a higher risk of dysfunction and are less responsive to dopamine replacement therapy. The PIGD subtype was found to... more
Background: Patients with the postural instability and gait difficulty (PIGD) subtype of Parkinson disease (PD) are at a higher risk of dysfunction and are less responsive to dopamine replacement therapy. The PIGD subtype was found to largely associate with white matter lesions, but details of the diffusion changes within these lesions have not been fully investigated. Voxel-based analysis for diffusion tensor imaging data is one of the preferred measures to compare diffusion changes in each voxel in any part of the brain. PD patients with the PIGD (n=12) and non-PIGD subtypes (n=12) were recruited to compare diffusion differences in fractional anisotropy, axial diffusivity, and radial diffusivity with voxel-based analysis. Significantly reduced fractional anisotropy in bilateral superior longitudinal fasciculus, bilateral anterior corona radiata, and the left genu of the corpus callosum were shown in the PIGD subtype compared with the non-PIGD subtype. Increased radial diffusivity ...