Bayesian graphical models for modern biological applications
Y Ni, V Baladandayuthapani, M Vannucci… - Statistical Methods & …, 2022 - Springer
Graphical models are powerful tools that are regularly used to investigate complex
dependence structures in high-throughput biomedical datasets. They allow for holistic …
dependence structures in high-throughput biomedical datasets. They allow for holistic …
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy …
A Shoeibi, N Ghassemi, M Khodatars, P Moridian… - Cognitive …, 2023 - Springer
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger.
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and …
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and …
Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models
The study of functional brain networks has grown rapidly over the past decade. While most
functional connectivity (FC) analyses estimate one static network structure for the entire …
functional connectivity (FC) analyses estimate one static network structure for the entire …
Inferring effective connectivity networks from fMRI time series with a temporal entropy-score
Inferring brain-effective connectivity networks from neuroimaging data has become a very
hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based …
hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based …
Identifying covariate-related subnetworks for whole-brain connectome analysis
Whole-brain connectome data characterize the connections among distributed neural
populations as a set of edges in a large network, and neuroscience research aims to …
populations as a set of edges in a large network, and neuroscience research aims to …
Network structure learning under uncertain interventions
F Castelletti, S Peluso - Journal of the American Statistical …, 2023 - Taylor & Francis
Abstract Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the
network of dependencies among variables, a task which is of primary interest in many fields …
network of dependencies among variables, a task which is of primary interest in many fields …
Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach
Age-associated changes in brain function play an important role in the development of
neurodegenerative diseases. Although previous work has examined age-related changes in …
neurodegenerative diseases. Although previous work has examined age-related changes in …
Dynamic covariance estimation via predictive Wishart process with an application on brain connectivity estimation
Modelling the complex dependence in multivariate time series data is a fundamental
problem in statistics and machine learning. Traditionally, the task has been approached with …
problem in statistics and machine learning. Traditionally, the task has been approached with …
A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks
Static and dynamic functional network connectivity (FNC) are typically studied separately,
which makes us unable to see the full spectrum of connectivity in each analysis. Here, we …
which makes us unable to see the full spectrum of connectivity in each analysis. Here, we …