Yanli Zhang-James profile picture
315 464-3441

Yanli Zhang-James, MD, PhD

3732B Institute For Human Performance (IHP)
505 Irving Avenue
Syracuse, NY 13210
Yanli Zhang-James's email address generated as an image

CURRENT APPOINTMENTS

Associate Professor of Psychiatry and Behavioral Sciences

LANGUAGES

English
Chinese

RESEARCH PROGRAMS AND AFFILIATIONS

Psychiatry and Behavioral Sciences

RESEARCH INTERESTS

Dr. Zhang-James primarily focuses her research on the etiology and mechanisms of neuropsychiatric disorders with an onset during childhood. Her work spans across interdisciplinary fields, with a particular emphasis on pioneering data-driven approaches and leveraging the power of AI and machine learning to improve our understanding of neuropsychiatric conditions, as well as their diagnosis and treatment.

EDUCATION

Additional Training: SUNY Upstate Medical University, 2008, Psychiatric Genetics
PhD: SUNY Upstate Medical University, 2007, Neuroscience
MS: Tongji Medical University, Wuhan, China, 2002, Neuroscience
MD: Tongji Medical University, Wuhan, China, 1999

RESEARCH ABSTRACT

Yanli Zhang-James, MD, PhD, is a trained physician and neuroscientist. Her primary research focus is etiological and mechanistic study of complex neuropsychiatric disorders. Her lab is one of the leading research groups on study of an ADHD and autism risk gene, SLC9A9. Her research also encompasses wide interdisciplinary fields and is part of several international consortiums and collaborative projects, such as the AGGRESOTYPE consortium, the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium, and the CoCA (Comorbid Conditions of ADHD) project. She is committed to developing cutting-edge methods to advance neuropsychiatric research and to improve our understanding and treatment of the most chronic and severe neuropsychiatric disorders.

 

Functional studies of gene SLC9A9 in ADHD and Autism.

Gene SLC9A9 encodes an endosomal membrane protein that is involved in pH regulation of the endosome compartment, an important system for regulation of synaptic functions.  SLC9A9 is risk-gene for ADHD and autism spectrum disorder (ASD), but was also implicated in Alzheimer’s disease, multiple sclerosis and cancer. For the past years, my lab has been studying SLC9A9 function and effects of the disease-associated mutations on molecular networks, cellular functions and behavioral phenotypes using various in vitro and in vivo models. We have also pioneered a drug repositioning approach to tap into novel therapeutic ideas that maybe readily tested in animal models.

  1. Zhang-James, Y., M. Vaudel, O. Mjaavatten, F. S. Berven, J. Haavik and S. V. Faraone (2019). "Effect of disease-associated SLC9A9 mutations on protein-protein interaction networks: implications for molecular mechanisms for ADHD and autism." Atten Defic Hyperact Disord 11(1): 91-105.
  2. Patak J, Hess JL, Zhang-James Y, Glatt SJ, Faraone SV. (2017) SLC9A9 Co-expression modules in autism-associated brain regions. Autism Res. 10(3):414-429.
  3. Yang L, Faraone SV, Zhang-James Y. (2016) Autism spectrum disorder traits in Slc9a9 knock-out mice. Am J Med Genet B Neuropsychiatr Genet. 171B(3):363-76
  4. Faraone, SV, and Zhang-James, Y (2013). Can Sodium/Hydrogen Exchange Inhibitors be Repositioned for ADHD?:  An in silico Approach. Am J Med Genet B Neuropsychiatr Genet. 162B(7): 711-717

 

Neuropsychiatric Disorders and Comorbidities.

I have taken a cross-disciplinary and network/pathway-based approach to study the genetic underpinning of complex neuropsychiatric disorders and their comorbidities. My work has contributed a better understand of the neurobiological basis underlying these neuropsychiatric conditions and their comorbidities, as well as novel biomarkers and therapeutic targets.

  1. Y, Zhang-James, Q Chen, R. Kuja-Halkola, P. Lichtenstein, H. Larsson, and Faraone SV. 2019. Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry Data (2019) doi: https://doi.org/10.1101/661983 https://www.biorxiv.org/content/10.1101/661983v1
  2. Zhang-James Y, Fernàndez-Castillo N, Hess JL, Malki K, Glatt SJ, Cormand B, Faraone SV. An integrated analysis of genes and functional pathways for aggression in human and rodent models. Mol Psychiatry. 2018 Jun 1
  3. Antshel, K. M., Y. Zhang-James and S. V. Faraone (2013). "The comorbidity of ADHD and autism spectrum disorder." Expert Rev Neurother 13(10): 1117-1128.
  4. Zhang-James, Y. and S. V. Faraone (2016). "Genetic architecture for human aggresion: A study of gene-phenotype relationship in OMIM." Am J Med Genet B Neuropsychiatr Genet 171(5): 641-649.

 

Machine Learning Models to Identify Brain Imaging Biomarkers

Selective vulnerability of brain regions and cell types is a common feature for mental illness and neuropsychiatric disorders. Why are some brain regions impaired and others spared in their pathophysiology?  My recent research effort has been to apply innovative machine learning (ML) models to magnetic resonance imaging (MRI) data and to extract discriminative brain structures and circuits to 1) classify case vs controls, 2) predict disease severity and clinical outcomes, and 3) monitor disease progression and treatment effects.

  1. Zhang-James Y, Glatt SJ, and Faraone SV. Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data. Lecture Notes in Computer Science. 2019 (In Press).
  2. Zhang-James Y, Helminen EC, Liu J, the ENIGMA-ADHD Working Group, Franke, B, Hoogman M, Faraone SV. 2019. Machine Learning Classification of Attention-Deficit/Hyperactivity Disorder Using Structural MRI Data. https://www.biorxiv.org/content/10.1101/546671v2?rss=1 doi: https://doi.org/10.1101/546671
  3. Thompson, P. M., N. Jahanshad, C. Ching, R.K, L. E. Salminen, S. I. Thomopoulos, J. Bright, B. T. Baune, S. Bertolín, J. Bralten, ….,Y Zhang-James, …., and The ENIGMA Consortium. 2019 ENIGMA and Global Neuroscience: A Decade of Large-Scale Studies of the Brain in Health and Disease across 43 Countries. https://psyarxiv.com/qnsh7

PUBLICATIONS

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