Emerging Artificial intelligence Technologies for Neurological and Neuropsychiatric Research

Neurological research is a very fast field of study that enhances our understanding of how the nervous system operates on a regular basis and can assist researchers in discovering solutions to issues that affect the body, neurological system, and brain. One of the basic goals of neurological research is to improve the understanding of the causes and mechanisms of different types of neurological disabilities and mental disorders, as well as improve the detection, diagnosis, and prognosis of these disorders. Over the past few decades, artificial intelligence (AI) has emerged as a powerful tool with the potential to significantly enhance neurological research. Recent advances in AI, including machine learning (ML) and deep learning (DL) technologies, have enabled the analysis of vast amounts of neurological data that were previously too complex for traditional data-processing techniques.

The recent advances in the bioinformatics data, as well as brain imaging techniques (e.g., diffusion tensor imaging (DTI), functional MRI (fMRI), computed tomography (CT), Positron emission tomography (PET), electroencephalogram (EEG), and functional near-infrared spectroscopy (fNIRS)), lead to a revolution source for large medical data that need to be analyzed. Recent advances in AI technologies at the forefront of the field have paved the way for the performance of these analyses.
The research topic focuses on the present AI-based systems for analyzing neurological research and brain data, which have the potential to aid patients suffering from debilitating conditions. Researchers from across the globe are invited to submit their original research articles that involves novel methodologies and/or new applications. We encourage carefully motivated articles with explainable results, which have depth and innovation in both methodology and brain/disorder sides.

Potential interests include but are not limited to the following areas:
 Examination of the advanced technologies and current trends of AI for advanced neurological research
 Utilization of advanced AI-based methods to better analyze electroencephalogram (EEG) brain data
 Integration of state-of-the-art deep learning (DL) techniques for advanced analysis of neurological research data
 Analysis of big data for neurological research purposes
 Analysis of brain bioinformatics and neuro-imaging data, including diffusion tensor imaging (DTI), functional MRI (fMRI), computed tomography (CT), positron emission tomography (PET), electroencephalogram (EEG), and functional near-infrared spectroscopy (fNIRS)
 Application of automated AI-based methods to detect, classify, and predict brain cancer
 Development of interpretable and explainable AI-based systems for neurological applications
 Implementation of novel AI-based applications for different types of neurological disabilities, including Alzheimer’s disease, stroke, epilepsy, autism, dyslexia, and Parkinson’s disorder.
 Investigation of new AI-based applications for different types of mental disabilities, including attention deficit-hyperactivity disorder, bipolar disorder, and schizophrenia.