About this Research Topic
Computational methods are critical to translational neuroscience as they can automatically generate hypothesis, detect biomarkers for diagnosis, prognosis, or treatment selection, decode subject heterogeneity in complex neurological diseases, and generate interpretable models revealing biological mechanisms.
The goal of this Research Topic is to focus on computational methods for translational brain-behavior analysis.
Computational models can include machine learning algorithms, mathematical models, signal and image processing, computer vision, big data analytics, and statistical analyses. Such algorithms or models can reveal the mechanism underlying normal or diseased processes such as neurodegenerative disorders, (Alzheimer’s, Parkinson’s, and ALS) mood and anxiety disorders (schizophrenia, dementia, depression, anxiety, and PTSD), neurodevelopmental disorders: (autism), chronic conditions (epilepsy and pain), disorders of vision and hearing and brain injuries (stroke, spinal cord injury, and traumatic brain injury)
We take particular interest in manuscripts that focus on the following topics:
• Interpretable machine learning methods for brain-behavior analysis:
• AI-enhanced therapeutics methods informed by computational tools, such as precise neuromodulation.
Potential topics can include, but are not limited to:
• AI-enhanced neuromodulation tools such as TMS;
• Neural decoding and BCI;
• Clinical decision support system and biomarkers for diagnosis, prognosis, and treatment selection;
• Computational study to reveal disease mechanism;
• Imaging genetics;
• Real-time signal processing;
• Scalable algorithms to process large brain datasets.
Keywords: Theory and modeling, Machine learning, Neuroimaging, Translational research, Clinical research
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.