Overview
Jia Guo, PhD, is an Assistant Professor of Neurobiology (in Psychiatry) at Columbia University and is affiliated with the Department of Biomedical Engineering. He is a neuroimaging scientist and neural engineer whose research integrates artificial intelligence, advanced magnetic resonance imaging (MRI), and magnetic resonance spectroscopy (MRS) to understand brain vulnerability, resilience, and disease progression across aging, Alzheimer's disease, and neuropsychiatric disorders.
A major focus of Dr. Guo's laboratory is the development of AI-enabled quantitative neuroimaging biomarkers that extract physiologically and molecularly meaningful information from routinely acquired MRI. His group develops approaches to characterize cerebral blood volume, brain aging, and disease-related brain pathology, with the broader goal of transforming conventional structural MRI into a scalable window into brain pathophysiology.
In parallel, his laboratory combines high-field MRI and MRS, translational animal models, and human neuroimaging to investigate metabolic, vascular, and circuit mechanisms underlying selective brain vulnerability. By integrating mechanistic neuroscience with scalable AI imaging, his research aims to develop quantitative biomarkers for early detection, disease stratification, prognosis, and therapeutic monitoring.
Areas of Expertise / Conditions Treated
- Dementia
- Psychosis
Academic Appointments
- Assistant Professor of Neurobiology (in Psychiatry)
Gender
- Male
Credentials & Experience
Honors & Awards
2026–2029 Columbia Alzheimer's Disease Research Center (P30) Development Award
2026–2028 Columbia SNF Center for Precision Psychiatry Seed Grant Award
2025–2028 Hevolution Foundation / American Federation for Aging Research (AFAR) New Investigator Award
2025–2027 Harrington Discovery Institute / HHF Early-Career Research Grant Award
2022–2024 Brain & Behavior Research Foundation (BBRF) NARSAD Young Investigator Award
Research
The Guo Laboratory develops AI-powered neuroimaging technologies to measure brain physiology and pathology across scales and species. The laboratory integrates structural and functional MRI, cerebral blood volume (CBV) imaging, magnetic resonance spectroscopy, molecular imaging, and deep learning to investigate how regional brain vulnerability and resilience emerge during normal aging and neuropsychiatric and neurodegenerative disease.
AI-Enabled Quantitative Neuroimaging
One major research direction is the development of AI models that derive quantitative physiological and molecular imaging phenotypes from conventional MRI. This work includes AI-derived cerebral blood volume, brain-age phenotypes, and emerging imaging markers of disease-related molecular pathology. The long-term goal is to build scalable imaging biomarkers that can be applied to large clinical and population datasets and ultimately enable individualized characterization of brain health and disease.
Mechanisms of Brain Vulnerability and Resilience
A complementary research program investigates the biological mechanisms underlying selective brain vulnerability and resilience. Using high-field MRI and MRS in translational animal models together with human neuroimaging, the laboratory studies cerebral metabolism, glutamatergic dysfunction, vascular physiology, and hippocampal and cortical vulnerability across schizophrenia, normal aging, and Alzheimer's disease.
Translational Precision Neuroimaging
Together, these programs seek to bridge molecular mechanisms, brain physiology, artificial intelligence, and clinical neuroscience. The laboratory's long-term objective is to develop imaging biomarkers that can support early disease detection, biological stratification, prognosis, and assessment of therapeutic response.
Selected Publications
1. Schizophrenia / hippocampal vulnerability: Provenzano FA, Guo J, Wall MM, et al. Hippocampal pathology in clinical high-risk patients and the onset of schizophrenia. Biological Psychiatry. 2020;87(3):234-242.
2. AI-derived physiological imaging: Liu C, Zhu N, Sun H, et al. Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains. Frontiers in Aging Neuroscience. 2022;14:923673.
3. Quantitative AI-MRS: Wu CJ, Kegeles LS, Rothman DL, Guo J. Q-MRS: Quantitative Magnetic Resonance Spectral Analysis Using Deep Learning. NMR in Biomedicine. 2026;39(5):e70279.
4. AI + disease prediction: Zhang J, Rao VM, Tian Y, et al. Detecting schizophrenia with 3D structural brain MRI using deep learning. Scientific Reports. 2023;13:14433.
5. Brain aging / physiological BrainAGE: Jomsky J, Li Z, Igwe KC, et al. Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps. Brain Communications. 2026.