The structure is the message: Preserving experimental context through tensor decomposition
Recent biological studies have been revolutionized in scale and granularity by multiplex and
high-throughput assays. Profiling cell responses across several experimental parameters …
high-throughput assays. Profiling cell responses across several experimental parameters …
Decoding task-based fMRI data with graph neural networks, considering individual differences
Task fMRI provides an opportunity to analyze the working mechanisms of the human brain
during specific experimental paradigms. Deep learning models have increasingly been …
during specific experimental paradigms. Deep learning models have increasingly been …
Classification of adolescent major depressive disorder via static and dynamic connectivity
This paper introduces an approach for classifying adolescents suffering from MDD using
resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients …
resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients …
Predicting biological gender and intelligence from fMRI via dynamic functional connectivity
Objective: This paper explores the predictive capability of dynamic functional connectivity
extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast …
extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast …
[HTML][HTML] Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI
This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD)
in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing …
in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing …
Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints
LD Kuang, ZM He, J Zhang, F Li - Biomedical Signal Processing and …, 2023 - Elsevier
Multi-group fMRI data may possess different types of subjects, tasks, scans, etc. Fortunately,
coupled canonical polyadic decomposition (CCPD) requires multiple tensor datasets to …
coupled canonical polyadic decomposition (CCPD) requires multiple tensor datasets to …
Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition
Y Jonmohamadi, S Muthukumaraswamy, J Chen… - Brain Topography, 2020 - Springer
The fusion of simultaneously recorded EEG and fMRI data is of great value to neuroscience
research due to the complementary properties of the individual modalities. Traditionally …
research due to the complementary properties of the individual modalities. Traditionally …
[HTML][HTML] Robust brain network identification from multi-subject asynchronous fMRI data
We describe a novel method for robust identification of common brain networks and their
corresponding temporal dynamics across subjects from asynchronous functional MRI (fMRI) …
corresponding temporal dynamics across subjects from asynchronous functional MRI (fMRI) …
HOTTBOX: Higher order tensor ToolBOX
HOTTBOX is a Python library for exploratory analysis and visualisation of multi-dimensional
arrays of data, also known as tensors. The library includes methods ranging from standard …
arrays of data, also known as tensors. The library includes methods ranging from standard …
Shift-invariant rank-(L, L, 1, 1) BTD with 3D spatial pooling and orthonormalization: Application to multi-subject fMRI data
LD Kuang, HP Zhang, H Zhu, S He, W Li, Y Gui… - … Signal Processing and …, 2024 - Elsevier
Abstract The rank-(L, L, 1, 1) block term decomposition (BTD) model shows better separation
performance for multi-subject fMRI data by preserving the high-way structure of fMRI data …
performance for multi-subject fMRI data by preserving the high-way structure of fMRI data …