Deep-learning-based diagnosis and prognosis of Alzheimer's disease: A comprehensive review
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment recommendations, deep
neural networks and in particular convolutional neural networks have advanced to a …
neural networks and in particular convolutional neural networks have advanced to a …
Advancements in Alzheimer's disease classification using deep learning frameworks for multimodal neuroimaging: A comprehensive review
Over the past years, Alzheimer's disease has emerged as a serious concern for people's
health. Researchers are facing challenges in effectively categorizing and diagnosing the …
health. Researchers are facing challenges in effectively categorizing and diagnosing the …
Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps
Finding an interpretable and compact representation of complex neuroimaging data is
extremely useful for understanding brain behavioral map** and hence for explaining the …
extremely useful for understanding brain behavioral map** and hence for explaining the …
Individual deviations from normative electroencephalographic connectivity predict antidepressant response
Background Antidepressant medications yield unsatisfactory treatment outcomes in patients
with major depressive disorder (MDD) with modest advantages over the placebo, partly due …
with major depressive disorder (MDD) with modest advantages over the placebo, partly due …
Normative modeling via conditional variational autoencoder and adversarial learning to identify brain dysfunction in alzheimer's disease
Normative modeling is an emerging and promising approach to effectively study disorder
heterogeneity in individual participants. In this study, we propose a novel normative …
heterogeneity in individual participants. In this study, we propose a novel normative …
Leveraging computational intelligence techniques for diagnosing degenerative nerve diseases: a comprehensive review, open challenges, and future research …
Degenerative nerve diseases such as Alzheimer's and Parkinson's diseases have always
been a global issue of concern. Approximately 1/6th of the world's population suffers from …
been a global issue of concern. Approximately 1/6th of the world's population suffers from …
Learning pathological representations in neuroimaging: Predicting psychiatric diagnosis by integrating heterogeneity constraints
R Louiset - 2024 - theses.hal.science
The biological mechanisms that underlie the symptoms of psychiatric diseases, such as
schizophrenia, bipolar, or autistic disorders, are still poorly understood in many regards. One …
schizophrenia, bipolar, or autistic disorders, are still poorly understood in many regards. One …
[PDF][PDF] Non-linearity matters: a deep learning solution to generalization of hidden brain patterns across population cohorts
The increasing number of neuroimaging scans in recent years has facilitated the use of
complex nonlinear approaches to analyzing such data. More specifically, deep learning …
complex nonlinear approaches to analyzing such data. More specifically, deep learning …
Computer Aided Alzheimer's Disease Diagnosis from Brain Imaging Dataset-A Review
U Bharathi, S Chitrakala - 2023 Intelligent Computing and …, 2023 - ieeexplore.ieee.org
Alzheimer's Disease (AD) is a neurological deterio-rating condition that cannot be reversed
and make distinctive by a gradual decrease in logical and analytical functions. Detecting AD …
and make distinctive by a gradual decrease in logical and analytical functions. Detecting AD …