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Understanding Brain Disorders 2022 : Deep Learning Techniques for Understanding Brain Disorders

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Link: https://peerj.com/special-issues/116-dl-brain-disorders
 
When N/A
Where N/A
Submission Deadline Dec 2, 2022
Categories    artificial intelligence   data mining   machine learning   neural networks
 

Call For Papers

The brain is one of the most complex organs of the human body. Modern understanding of brain disorders is shaped by multiple non-invasive modalities of data that can be acquired from the human brain, such as EEG, fMRI, PET and fNIRS. Due to the high dimensional nature of these data, understanding patterns in the data and their discriminability across disorders has been a challenge. The advent of Deep learning (DL) models has begun to address this challenge, through pattern recognition, classification, detection, diagnosis, augmentation and segmentation. The cross-pollination of ideas from neuroscience, neurology, psychiatry, neuroimaging and computer science is required for this budding field to attain its full potential.

The purpose of this Special Issue is to showcase research where ideas from deep learning within the field of engineering and computer science are used to understand brain function in the healthy brain (neuroscience) as well as those in neurological and psychiatric brain disorders with the help of neuroimaging data. Applications to neurological disorders include (but are not limited to) Alzheimer's Disease and Dementia, movement disorders including Parkinson's Disease, Stroke, Epilepsy, Amyotrophic Lateral Sclerosis, Brain Injury and Brain Tumours while psychiatric disorders include Schizophrenia spectrum, Autism spectrum, ADHD, Depression, PTSD, eating disorders, anxiety disorders, etc.

Researchers are encouraged to submit manuscripts related to classification, regression, detection, diagnosis, prediction of treatment outcomes, augmentation or segmentation methods based on DL for publication. Approaches to explainable AI, which enable a scientific understanding of features in the data (and hence aspects of brain function) that are most important for driving the model’s prediction are very much encouraged. This special issue especially welcomes submissions that depict the end-to-end technological viewpoint that uses automated informatics systems to solve single or multiple cases of healthcare advancements.

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