Abstract: An artificial intelligence (AI) and machine learning (ML) based graphical user interface (GUI) system for early detection of depression symptoms using facial expression recognition and electroencephalogram is provided via receiving image data of a biological subject under examination for depression; receiving, from the biological subject, electroencephalogram data; analyzing the image data, via a first machine learning model, to identify an emotional state conveyed by a face of the biological subject; analyzing the electroencephalogram data, via a second machine learning model, to identify a severity level of depression in the biological subject; and providing the emotional state identified by the first machine learning model and the severity level identified by the second machine learning model to a graphical user interface.

Abstract: The invention features methods for the treatment of one or more symptoms of microcephaly associated autism disorders e.g., Christianson syndrome, by administering agents that increase the level or activity of BDNF or TrkB in the brain of the patient. These agents may include BDNF, a BDNF agonist, a BDNF mimetic, a TrkB agonist, a cell expressing recombinant BDNF, a BDNF encoding recombinant nucleic acid molecule encapsidated within a recombinant virus, and an agent that decreases the acidity of endosomes.