Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …
offer a non-invasive approach to map** the structure and function of the brain …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Brain aging patterns in a large and diverse cohort of 49,482 individuals
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as
well as by age-related and often coexisting pathologies. Magnetic resonance imaging and …
well as by age-related and often coexisting pathologies. Magnetic resonance imaging and …
Characterizing heterogeneity in neuroimaging, cognition, clinical symptoms, and genetics among patients with late-life depression
Importance Late-life depression (LLD) is characterized by considerable heterogeneity in
clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological …
clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological …
Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment,
especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple …
especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple …
Data-driven modelling of neurodegenerative disease progression: thinking outside the black box
Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …
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 …
[HTML][HTML] Applications of generative adversarial networks in neuroimaging and clinical neuroscience
Generative adversarial networks (GANs) are one powerful type of deep learning models that
have been successfully utilized in numerous fields. They belong to the broader family of …
have been successfully utilized in numerous fields. They belong to the broader family of …
Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia
Technical developments and improved access to neuroimaging techniques have brought us
closer to understanding the neuropathological origins of schizophrenia. Using data-driven …
closer to understanding the neuropathological origins of schizophrenia. Using data-driven …
[HTML][HTML] Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning
Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
innate traits of irreversibility with subtle and gradual progression. These characteristics make …