Neurobehavioral Clinical Research Section
B.S., University of Kansas
M.S., University of Pittsburgh
Ph.D., Carnegie Mellon University
Dr. Gustavo Sudre previously worked with Professor Tom Mitchell at Carnegie Mellon University investigating how the human brain represents information. More specifically, they looked for semantic information in the brain while subjects perform several cognitive tasks and used a multi-modal imaging approach (fMRI, MEG, EEG, ECoG) paired with machine learning techniques to classify different brain states. Previously, Dr. Sudre was part of the Human Rehab Neural Engineering Lab at the University of Pittsburgh, studying the control of movement to develop new technologies for assisting and restoring motor function after nervous system injury and limb loss. The focus of that project was how the nervous system processes proprioception, and Dr. Sudre analyzed magnetoencephalography (MEG) data to investigate the influence of somatosensory input in motor cortex activity. By using MEG to visualize brain processes, he sought to not only assess the interaction among brain regions processing sensory information related to visually guided movement, but also to identify optimal regions that show significant signal modulation that can be used to control brain-machine interfaces in real-time. Dr. Sudre has also been involved in several projects with Dr. Anto Bagic at the University of Pittsburgh Medical Center investigating the use of MEG for different clinical applications.
Dr. Gustavo Sudre works with Dr. Philip Shaw in the Neurobehavioral Clinical Research section of the Social and Behavioral Research Branch. The main goal of his research is to integrate neural, genomic and behavioral data, with the added dimension of longitudinal design, to study both typical and atypical development. A current focus is on one of the most common neuropsychiatric disorders of childhood: attention deficit hyperactivity disorder (ADHD). Dr. Sudre uses his strong grounding in bioinformatics to tackle computational challenges inherent in processing, analyzing and combining big data in genomics and neuroimaging.
Sudre, G., Sharp, W., Kundzicz, P. et al. (2021) Predicting the course of ADHD symptoms through the integration of childhood genomic, neural, and cognitive features. Mol Psychiatry 26, 4046–4054.
Sudre, G. Shaw, P. (2021) “Adolescent Attention-Deficit/Hyperactivity Disorder: Understanding Teenage Symptom Trajectories.” Biological psychiatry 89.2 (2021): 152–161.
Sudre, G.*, Frederick, J.*, Sharp, W. et al. (2019). Mapping associations between polygenic risks for childhood neuropsychiatric disorders, symptoms of attention deficit hyperactivity disorder, cognition, and the brain. Molecular Psychiatry, doi. 10.1038/s41380-019-0350-3.
Sudre, G*, Chen*, Y. C., Sharp, W, et al. (2018). Neuroanatomic, epigenetic and genetic differences in monozygotic twins discordant for attention deficit hyperactivity disorder. Molecular Psychiatry, Molecular Psychiatry 23, 683–690.
Sudre, G. Mangalmurti, A., Shaw, P. (2018). Growing out of attention deficit hyperactivity disorder: Insights from the ‘remitted’ brain. Neuroscience & Biobehavioral Reviews. (94) 198-209.
Sudre G., Choudhury S., Szekely, E. et al. (2017). Estimating the heritability of structural and functional brain connectivity in families affected by Attention Deficit Hyperactivity Disorder. JAMA Psychiatry, Jan 1;74(1):76-84.
Sudre, G., Szekely E., Sharp, W., et al. (2017) Multimodal mapping of the brain’s functional connectivity and the adult outcome of attention deficit hyperactivity disorder. Proceedings of the National Academy of Sciences, 114 (44) 11787-11792.
Sudre, G.*, Szekely, E.*, Sharp, W., et al. (2017). Defining the neural substrate of the adult outcome of childhood ADHD: a multimodal neuroimaging study of response inhibition. American Journal of Psychiatry, Sep 1;174(9):867-876.
Sudre, G.*, Shaw, P.*, Wharton, A., et al. (2015). White matter microstructure and the variable adult outcome of childhood Attention Deficit Hyperactivity Disorder. Neuropsychopharmacology, Feb;40(3):746-54.
Last updated: February 18, 2022