[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
Deep learning for the harmonization of structural MRI scans: a survey
Medical imaging datasets for research are frequently collected from multiple imaging centers
using different scanners, protocols, and settings. These variations affect data consistency …
using different scanners, protocols, and settings. These variations affect data consistency …
Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Abstract Machine learning can be used to define subtypes of psychiatric conditions based
on shared biological foundations of mental disorders. Here we analyzed cross-sectional …
on shared biological foundations of mental disorders. Here we analyzed cross-sectional …
Macroscale connectome topographical structure reveals the biomechanisms of brain dysfunction in Alzheimer's disease
The intricate spatial configurations of brain networks offer essential insights into
understanding the specific patterns of brain abnormalities and the underlying biological …
understanding the specific patterns of brain abnormalities and the underlying biological …
A dorsomedial prefrontal cortex-based dynamic functional connectivity model of rumination
Rumination is a cognitive style characterized by repetitive thoughts about one's negative
internal states and is a common symptom of depression. Previous studies have linked trait …
internal states and is a common symptom of depression. Previous studies have linked trait …
Federated learning for healthcare applications
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …
become critical for healthcare tasks like in medical image analysis and human behavior …
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Brain imaging research enjoys increasing adoption of supervised machine learning for
single-participant disease classification. Yet, the success of these algorithms likely depends …
single-participant disease classification. Yet, the success of these algorithms likely depends …
Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism
Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms.
The complexity of factors, including inter-site and developmental differences, hinders the …
The complexity of factors, including inter-site and developmental differences, hinders the …
Ci-gnn: A granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis
There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-
network based psychiatric diagnosis, which, in turn, also motivates an urgent need for …
network based psychiatric diagnosis, which, in turn, also motivates an urgent need for …
Aberrant large-scale network interactions across psychiatric disorders revealed by large-sample multi-site resting-state functional magnetic resonance imaging …
Abstract Background and Hypothesis Dynamics of the distributed sets of functionally
synchronized brain regions, known as large-scale networks, are essential for the emotional …
synchronized brain regions, known as large-scale networks, are essential for the emotional …