A technical review of canonical correlation analysis for neuroscience applications
Collecting comprehensive data sets of the same subject has become a standard in
neuroscience research and uncovering multivariate relationships among collected data sets …
neuroscience research and uncovering multivariate relationships among collected data sets …
Imaging genomics: data fusion in uncovering disease heritability
K Hartmann, CY Sadée, I Satwah… - Trends in molecular …, 2023 - cell.com
Sequencing of the human genome in the early 2000s enabled probing of the genetic basis
of disease on a scale previously unimaginable. Now, two decades later, after interrogating …
of disease on a scale previously unimaginable. Now, two decades later, after interrogating …
Deep learning enables superior photoacoustic imaging at ultralow laser dosages
Optical‐resolution photoacoustic microscopy (OR‐PAM) is an excellent modality for in vivo
biomedical imaging as it noninvasively provides high‐resolution morphologic and functional …
biomedical imaging as it noninvasively provides high‐resolution morphologic and functional …
Data-driven process monitoring using structured joint sparse canonical correlation analysis
In order to improve the performance of canonical correlation analysis (CCA) based methods
for process monitoring, this brief proposes a novel process monitoring approach using the …
for process monitoring, this brief proposes a novel process monitoring approach using the …
Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics
The advances in technologies for acquiring brain imaging and high-throughput genetic data
allow the researcher to access a large amount of multi-modal data. Although the sparse …
allow the researcher to access a large amount of multi-modal data. Although the sparse …
Joint-channel-connectivity-based feature selection and classification on fNIRS for stress detection in decision-making
M Huang, X Zhang, X Chen, Y Mai, X Wu… - … on Neural Systems …, 2022 - ieeexplore.ieee.org
Stress is one of the contributing factors affecting decision-making. Therefore, early stress
recognition is essential to improve clinicians' decision-making performance. Functional near …
recognition is essential to improve clinicians' decision-making performance. Functional near …
Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data
Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized
by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an …
by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an …
Learning high-order multi-view representation by new tensor canonical correlation analysis
Canonical correlation analysis (CCA) has attracted great interest in multi-view
representation. However, most of the CCA methods heavily rely on the matrix structure …
representation. However, most of the CCA methods heavily rely on the matrix structure …
Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases
T Wang, X Chen, J Zhang, Q Feng, M Huang - Medical Image Analysis, 2023 - Elsevier
Imaging genetics is a crucial tool that is applied to explore potentially disease-related
biomarkers, particularly for neurodegenerative diseases (NDs). With the development of …
biomarkers, particularly for neurodegenerative diseases (NDs). With the development of …
Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network
The severity evaluation of Parkinson's disease (PD) is of great significance for the treatment
of PD. However, existing methods either have limitations based on prior knowledge or are …
of PD. However, existing methods either have limitations based on prior knowledge or are …