Machine learning in mental health: a scoping review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review
Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
A data augmentation-based framework to handle class imbalance problem for Alzheimer's stage detection
Alzheimer's Disease (AD) is the most common form of dementia. It gradually increases from
mild stage to severe, affecting the ability to perform common daily tasks without assistance. It …
mild stage to severe, affecting the ability to perform common daily tasks without assistance. It …
Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning
A recent imaging modality Diffusion Tensor Imaging completes information used from
Structural MRI in studies of Alzheimer disease. A large number of recent studies has …
Structural MRI in studies of Alzheimer disease. A large number of recent studies has …
Machine learning in acute ischemic stroke neuroimaging
Machine Learning (ML) through pattern recognition algorithms is currently becoming an
essential aid for the diagnosis, treatment, and prediction of complications and patient …
essential aid for the diagnosis, treatment, and prediction of complications and patient …
[HTML][HTML] Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Combining machine learning with neuroimaging data has a great potential for early
diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it …
diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it …
Multimodal analysis of functional and structural disconnection in A lzheimer's disease using multiple kernel SVM
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between
spatially segregated brain regions which may be related to both local gray matter (GM) …
spatially segregated brain regions which may be related to both local gray matter (GM) …
A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis
Objective Damage to the cerebral tissue structural connectivity associated with amyotrophic
lateral sclerosis (ALS), which extends beyond the motor pathways, can be visualised by …
lateral sclerosis (ALS), which extends beyond the motor pathways, can be visualised by …