Modern views of machine learning for precision psychiatry
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …
the advent of functional neuroimaging, novel technologies and methods provide new …
Role of deep learning in classification of brain MRI images for prediction of disorders: a survey of emerging trends
PR Verma, AK Bhandari - Archives of Computational Methods in …, 2023 - Springer
Image classification is the act of labeling groups of pixels or voxels of an image based on
some rules. It finds applications in medical image analysis, and satellite image identification …
some rules. It finds applications in medical image analysis, and satellite image identification …
Characterizing functional brain networks via spatio-temporal attention 4D convolutional neural networks (STA-4DCNNs)
Characterizing individualized spatio-temporal patterns of functional brain networks (FBNs)
via functional magnetic resonance imaging (fMRI) provides a foundation for understanding …
via functional magnetic resonance imaging (fMRI) provides a foundation for understanding …
Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs)
Mounting evidence has demonstrated that complex brain function processes are realized by
the interaction of holistic functional brain networks which are spatially distributed across …
the interaction of holistic functional brain networks which are spatially distributed across …
Differentiable neural architecture search for optimal spatial/temporal brain function network decomposition
It has been a key topic to decompose the brain's spatial/temporal function networks from 4D
functional magnetic resonance imaging (fMRI) data. With the advantages of robust and …
functional magnetic resonance imaging (fMRI) data. With the advantages of robust and …
fmri brain decoding and its applications in brain–computer interface: A survey
Brain neural activity decoding is an important branch of neuroscience research and a key
technology for the brain–computer interface (BCI). Researchers initially developed simple …
technology for the brain–computer interface (BCI). Researchers initially developed simple …
Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks
For the diagnosis and precise treatment of acute ischemic stroke, predicting the final location
and volume of lesions is of great clinical interest. Current deep learning-based prediction …
and volume of lesions is of great clinical interest. Current deep learning-based prediction …
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 …
Computing personalized brain functional networks from fMRI using self-supervised deep learning
A novel self-supervised deep learning (DL) method is developed to compute personalized
brain functional networks (FNs) for characterizing brain functional neuroanatomy based on …
brain functional networks (FNs) for characterizing brain functional neuroanatomy based on …
Evolutional neural architecture search for optimization of spatiotemporal brain network decomposition
Using deep neural networks (DNNs) to explore spatial patterns and temporal dynamics of
human brain activities has been an important yet challenging problem because the artificial …
human brain activities has been an important yet challenging problem because the artificial …