250 research outputs found
Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning
It is well believed that video captioning is a fundamental but challenging
task in both computer vision and artificial intelligence fields. The prevalent
approach is to map an input video to a variable-length output sentence in a
sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless,
the training of RNN still suffers to some degree from vanishing/exploding
gradient problem, making the optimization difficult. Moreover, the inherently
recurrent dependency in RNN prevents parallelization within a sequence during
training and therefore limits the computations. In this paper, we present a
novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks
(dubbed as TDConvED) that fully employ convolutions in both encoder and decoder
networks for video captioning. Technically, we exploit convolutional block
structures that compute intermediate states of a fixed number of inputs and
stack several blocks to capture long-term relationships. The structure in
encoder is further equipped with temporal deformable convolution to enable
free-form deformation of temporal sampling. Our model also capitalizes on
temporal attention mechanism for sentence generation. Extensive experiments are
conducted on both MSVD and MSR-VTT video captioning datasets, and superior
results are reported when comparing to conventional RNN-based encoder-decoder
techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8%
to 67.2% on MSVD.Comment: AAAI 201
Network-Based Genome Wide Study of Hippocampal Imaging Phenotype In Alzheimer's Disease To Identify Functional Interaction Modules
Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases
Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules
Motivation:
Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity.
Results:
We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype
Evaluation of electroacupuncture as a non-pharmacological therapy for astrocytic structural aberrations and behavioral deficits in a post-ischemic depression model in mice
BackgroundAscending clinical evidence supports that electroacupuncture (EA) is effective in treating post-ischemic depression (PID), but little is known about how it works at the cellular level. Astrocytes are exquisitely sensitive to their extracellular environment, and under stressful conditions, they may experience aberrant structural remodeling that can potentially cause neuroplastic disturbances and contribute to subsequent changes in mood or behavior.ObjectivesThis study aimed to investigate the effect of EA on behavioral deficits associated with PID in mice and verify the hypothesis that astrocytic morphology may be involved in this impact.MethodsWe established a PID animal model induced by transient bilateral common carotid artery occlusion (BCCAO, 20 min) and chronic restraint stress (CRS, 21 days). EA treatment (GV20 + ST36) was performed for 3 weeks, from Monday to Friday each week. Depressive- and anxiety-like behaviors and sociability were evaluated using SPT, FST, EPM, and SIT. Immunohistochemistry combined with Sholl and cell morphological analysis was utilized to assess the process morphology of GFAP+ astrocytes in mood-related regions. The potential relationship between morphological changes in astrocytes and behavioral output was detected by correlation analysis.ResultsBehavioral assays demonstrated that EA treatment induced an overall reduction in behavioral deficits, as measured by the behavioral Z-score. Sholl and morphological analyses revealed that EA prevented the decline in cell complexity of astrocytes in the prefrontal cortex (PFC) and the CA1 region of the hippocampus, where astrocytes displayed evident deramification and atrophy of the branches. Eventually, the correlation analysis showed there was a relationship between behavioral emotionality and morphological changes.ConclusionOur findings imply that EA prevents both behavioral deficits and structural abnormalities in astrocytes in the PID model. The strong correlation between behavioral Z-scores and the observed morphological changes confirms the notion that the weakening of astrocytic processes may play a crucial role in depressive symptoms, and astrocytes could be a potential target of EA in the treatment of PID
Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in Alzheimer’s disease
Many recent imaging genetic studies focus on detecting the associations between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs). Although there exist a large number of generalized multivariate regression analysis methods, few of them have used diagnosis information in subjects to enhance the analysis performance. In addition, few of models have investigated the identification of multi-modality phenotypic patterns associated with interesting genotype groups in traditional methods. To reveal disease-relevant imaging genetic associations, we propose a novel diagnosis-guided multi-modality (DGMM) framework to discover multi-modality imaging QTs that are associated with both Alzheimer's disease (AD) and its top genetic risk factor (i.e., APOE SNP rs429358). The strength of our proposed method is that it explicitly models the priori diagnosis information among subjects in the objective function for selecting the disease-relevant and robust multi-modality QTs associated with the SNP. We evaluate our method on two modalities of imaging phenotypes, i.e., those extracted from structural magnetic resonance imaging (MRI) data and fluorodeoxyglucose positron emission tomography (FDG-PET) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results demonstrate that our proposed method not only achieves better performances under the metrics of root mean squared error and correlation coefficient but also can identify common informative regions of interests (ROIs) across multiple modalities to guide the disease-induced biological interpretation, compared with other reference methods
Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease
Neuroimaging genetics has attracted growing attention and interest, which
is thought to be a powerful strategy to examine the influence of genetic
variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or
functions of human brain. In recent studies, univariate or multivariate
regression analysis methods are typically used to capture the effective
associations between genetic variants and quantitative traits (QTs) such as
brain imaging phenotypes. The identified imaging QTs, although associated with
certain genetic markers, may not be all disease specific. A useful, but
underexplored, scenario could be to discover only those QTs associated with both
genetic markers and disease status for revealing the chain from genotype to
phenotype to symptom. In addition, multimodal brain imaging phenotypes are
extracted from different perspectives and imaging markers consistently showing
up in multimodalities may provide more insights for mechanistic understanding of
diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a
general framework to exploit multi-modal brain imaging phenotypes as
intermediate traits that bridge genetic risk factors and multi-class disease
status. We applied our proposed method to explore the relation between the
well-known AD risk SNP APOE rs429358 and three baseline brain
imaging modalities (i.e., structural magnetic resonance imaging (MRI),
fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir
PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that
our proposed method not only helps improve the performances of imaging genetic
associations, but also discovers robust and consistent regions of interests
(ROIs) across multi-modalities to guide the disease-induced interpretation
Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm
Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. In imaging genetics, genes associated with a phenotype should at least expressed in the phenotypical region. We study the association between the genotype and amyloid imaging data and propose a transcriptome-guided SCCA framework that incorporates the gene expression information into the SCCA criterion. An alternating optimization method is used to solve the formulated problem. Although the problem is not biconcave, a closed-form solution has been found for each subproblem. The results on real data show that using the gene expression data to guide the feature selection facilities the detection of genetic markers that are not only associated with the identified QTs, but also highly expressed there
Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis
Motivation: Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.
Results: The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer’s Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation
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