About

Yi Fan, M.D., Ph.D., is the Richard H. Chamberlain Professor in Research Oncology II at the University of Pennsylvania's Perelman School of Medicine. He is a member of the Abramson Cancer Center, the Institute for Immunology, and the Cardiovascular Institute at the University of Pennsylvania. His research investigates how the tissue microenvironment governs disease progression and therapeutic response in cancer and regenerative medicine. His central objective is to reprogram the microenvironment to suppress tumor growth, enhance anti-tumor immunity, and promote tissue repair following injury. His work focuses on vascular regulation of tissue immunity, exploring the role of the vasculature as a dynamic regulator of tissue immunity and repair. He has shown that tumor-associated endothelial cells undergo mesenchymal transformation, promoting pathological vascular remodeling and therapeutic resistance. His research has identified key regulators of endothelial plasticity, such as PAK4 and PHGDH, and developed strategies to enhance CAR-T cell efficacy through vascular de-transformation. Additionally, he studies tumor immune suppression, aiming to overcome barriers to T cell–based immunotherapies by reprogramming immune cell populations within the tumor microenvironment, including macrophages, MDSCs, dendritic cells, and fibroblasts. His contributions extend to understanding how immune modulation can be harnessed for tissue repair in cardiovascular disease, and he is investigating innovative radiotherapy approaches such as FLASH radiotherapy, which can spare normal tissue while reshaping tumor immunity. His work has led to the development of combinatorial vasculoimmunotherapy approaches and small-molecule agents capable of reversing immunosuppression, thereby overcoming resistance to immunotherapy in glioblastoma and other cancers. His research aims to establish new paradigms in cancer therapy and tissue regeneration by targeting the microenvironment and immune regulation.

Research topics

• Psychology
• Neuroscience
• Cognitive psychology
• Cognitive science
• Biology
• Medicine
• Internal medicine
• Psychiatry
• Physics

Selected publications

• Age-dependent acceleration of structural brain aging in medication-free major depressive disorder linked to neuroanatomical phenotype findings from COORDINATE-MDD consortium

  medRxiv · 2026-04-08

  articleOpen access

  Background: Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and/or reliance on summary-level data. Methods: Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results: MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions: Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.

  PublisherOA PDFDOI

• Mapping developmental patterns of intrinsic timescale

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-09

  articleOpen access

  Abstract Intrinsic timescale is a commonly used measure of spontaneous neural dynamics that quantifies the temporal window of processing of neuronal populations. Intrinsic timescale displays a hierarchical cortical organization across multiple species and imaging modalities, with shorter timescales in sensorimotor cortex compared to association cortex. However, less is known about how intrinsic timescale evolves during human brain development and whether its cortical maturation patterns generalize to independent developmental samples. Here we estimate the intrinsic timescale in two independent datasets of youth (HCPD: n =565; HBN: n =729; age range 8–22 years) and investigate its neurodevelopmental patterns. We find that developmental changes in the intrinsic timescale follow a hierarchical pattern that recapitulates an axis spanning sensorimotor to association cortices (S–A axis). Our analysis of an independent healthy young adult dataset (HCPYA: n =973, age range 22–37 years) underscores the specificity of these developmental findings, suggesting that the intrinsic timescale develops along the S–A axis in youth and stabilizes in adulthood. Together, these results reveal convergence between major axes of cortical organization and development, highlighting intrinsic timescale as a principled marker of hierarchical brain maturation in youth.

  PublisherOA PDFDOI

• Current and Future Applications of PET Radiomics in Radiation Oncology

  PET Clinics · 2025-02-05 · 3 citations

  reviewOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Special Issue: Nanoprobes and Biomedical Imaging

  Nano Biomedicine and Engineering · 2025-03-01

  articleOpen access
  PublisherOA PDFDOI

• Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-10

  preprintOpen accessSenior authorCorresponding

  Abstract Functional MRI studies have identified functional connectivity (FC) patterns associated with behavioral traits using whole-brain or region-wise predictive models. However, whole-brain approaches often suffer from limited generalizability and interpretability due to the high-dimensionality of FC data. Conversely, region-wise models inherently isolate predictions, ineffective for characterizing contributions of the whole brain FC patterns in predicting a target trait. In this study, we propose an interpretable predictive model that learns fine-grained FC patterns predictive of behavioral traits, jointly at the regional and participant levels, to characterize the overall association of FC patterns with a target trait. Our model learns both a relevance score and a dedicated prediction model for each brain region, then integrates the regional predictions to generate a participant-level prediction, capturing the collective association of FC patterns with the trait. We validated our method using FC data from 6798 participants in the Adolescent Brain and Cognitive Development (ABCD) study for predicting cognition. Our interpretable predictive model identified the cingulo-parietal, retrosplenial-temporal, dorsal attention, salience, and cingulo-opercular networks as collectively predictive of cognitive traits. The interpretable model significantly improved prediction accuracy and facilitated the characterization of fine-grained differences in FC patterns across cognitive domains. Furthermore, the learned relevance scores enhanced region-wise predictions of longitudinal cognitive measures in the ABCD cohort and cognitive traits in an external Human Connectome Project Development (HCP-D) cohort. These findings suggest that our method effectively characterizes generalizable and fine-grained FC patterns linked to cognition in youth.

  PublisherOA PDFDOI

• SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-05 · 1 citations

  preprintOpen accessSenior authorCorresponding

  To achieve fast and accurate cortical surface reconstruction from brain magnetic resonance images (MRIs), we develop a method to jointly reconstruct the inner (white-gray matter interface), outer (pial), and midthickness surfaces, regularized by their interdependence. Rather than reconstructing these surfaces separately without taking into consideration their interdependence as in most existing methods, our method learns three diffeomorphic deformations jointly to optimize the midthickness surface to lie halfway between the inner and outer cortical surfaces and simultaneously deforms it inward and outward towards the inner and outer cortical surfaces, respectively. The surfaces are encouraged to have a spherical topology by regularization terms for non-negativeness of the cortical thickness and symmetric cycle-consistency of the coupled surface deformations. The coupled reconstruction of cortical surfaces also facilitates an accurate estimation of the cortical thickness based on the diffeomorphic deformation trajectory of each vertex on the surfaces. Validation experiments have demonstrated that our method achieves state-of-the-art cortical surface reconstruction performance in terms of accuracy and surface topological correctness on large-scale MRI datasets, including ADNI, HCP, and OASIS.

  PublisherOA PDFDOI

• Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics

  Communications Medicine · 2025-11-15 · 3 citations

  articleOpen access

  Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone. Using the semi-supervised machine learning algorithm, Heterogeneity through Discriminative Analysis (HYDRA), we had identified two neuroanatomical dimensions in deeply phenotyped (i.e., comprehensively assessed across neuroimaging, clinical, and behavioural domains), medication-free participants with MDD from the COORDINATE-MDD consortium. In the present study, we apply this pre-trained HYDRA model to the UK Biobank (UKB) to validate these dimensions in a large general population and a subsample with current depressive symptoms. Dimension 2 (D2), compared to Dimension 1 (D1), is characterized by reduced grey and white matter volumes and limited treatment response to antidepressant and placebo medications. Out-of-sample validation in the UKB general population (n = 37,235) confirms these neuroanatomical features and reveals D2 associations with cognitive impairments, adverse life events, self-harm and suicide attempts, a pro-atherogenic lipid profile, and genetic links to neurodegenerative traits. Similar profiles are observed in the UKB subsample with current depressive symptoms (n = 1455). D1 and D2 represent distinct neurobiological mechanisms underlying MDD. The validation in a general population-based cohort and in a cohort sample with depressive symptoms delineates mechanisms underlying heterogeneity in MDD. Major depressive disorder is a common and disabling condition, but people differ greatly in their symptoms and responses to treatment. We used brain scans and machine learning to identify two patterns of brain structure linked to depression. One pattern showed relatively preserved brain volume and was associated with better treatment response. The other showed widespread reductions in brain volume and was related to poorer memory and thinking skills, greater exposure to adverse life events, increased risk of self-harm, and metabolic and genetic changes. These findings were confirmed in a large general population sample as well as in people with current depressive symptoms. The results suggest that depression includes distinct brain-based subtypes, which may help explain differences in treatment response and guide the development of more personalised approaches. Xiao, Woodham, Cui, et al. apply machine learning to brain MRI data from major depression and the UK Biobank. They identify two neuroanatomical dimensions, one linked to preserved brain structure and healthier outcomes, and the other to reduced volumes, impaired cognition, self-harm, and adverse metabolic and genetic profiles.

  PublisherOA PDFDOI

• Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling

  Proceedings of the National Academy of Sciences · 2025-10-16

  articleOpen accessSenior authorCorresponding

  Brain-wide association studies using functional MRI have advanced our understanding of how behavioral traits relate to individual variability in brain function. These studies typically identify functional connectivity (FC) patterns linked to behavioral traits using either whole-brain or region-wise predictive models. However, whole-brain models often struggle with generalizability and interpretability due to the high dimensionality of FC data, while region-wise models isolate predictions, limiting their ability to capture the integrated contributions of brain-wide FC patterns. In this study, we introduce an interpretable predictive model that learns fine-grained FC patterns predictive of behavioral traits, jointly at the regional and participant levels, to characterize the overall association of FC patterns with a target trait. Our model jointly learns a relevance score and a dedicated prediction function for each brain region, then integrates the regional predictions using the relevance scores as weights to generate a participant-level prediction, capturing the collective association of FC patterns with the trait. We validated our method using FC data from 6,798 participants in the Adolescent Brain and Cognitive Development (ABCD) study to predict cognition. Our model identified the cingulo-parietal, retrosplenial-temporal, dorsal attention, and cingulo-opercular networks as collectively predictive of cognitive traits, achieved competitive prediction accuracy, and enabled detailed characterization of fine-grained FC differences across cognitive domains. The learned relevance scores enhanced region-wise predictions of longitudinal cognitive measures in the ABCD cohort and cognitive traits in the Human Connectome Project Development cohort. These findings suggest that our method effectively characterizes generalizable and fine-grained FC patterns linked to cognition in youth.

  PublisherDOI

• 291 Structure/function mechanism of a highly efficacious potentiator X316761

  Journal of Cystic Fibrosis · 2025-10-01

  article
  PublisherDOI

• SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations

  IEEE Transactions on Medical Imaging · 2025-07-02

  articleOpen accessSenior author

  To achieve fast and accurate cortical surface reconstruction from brain magnetic resonance images (MRIs), we develop a method to jointly reconstruct the inner (white-gray matter interface), outer (pial), and midthickness surfaces, regularized by their interdependence. Rather than reconstructing these surfaces separately without taking into consideration their interdependence as in most existing methods, our method learns three diffeomorphic deformations jointly to optimize the midthickness surface to lie halfway between the inner and outer cortical surfaces and simultaneously deforms it inward and outward towards the inner and outer cortical surfaces, respectively. The surfaces are encouraged to have a spherical topology by regularization terms for non-negativeness of the cortical thickness and symmetric cycle-consistency of the coupled surface deformations. The coupled reconstruction of cortical surfaces also facilitates an accurate estimation of the cortical thickness based on the diffeomorphic deformation trajectory of each vertex on the surfaces. Validation experiments have demonstrated that our method achieves state-of-the-art cortical surface reconstruction performance in terms of accuracy and surface topological correctness on large-scale MRI datasets, including ADNI, HCP, and OASIS. The code is available at: https://github.com/MLDataAnalytics/SurfNet.

  PublisherOA PDFDOI

Recent grants

• Anatomic biomarkers of chronic kidney disease progression among children

  NIH · $469k · 2019–2022

• Fast and robust deep learning tools for analysis of neuroimaging data of Alzheimer's disease

  NIH · $3.4M · 2021–2027

• Learning radiomic signatures to early predict response of rectal cancer patients to neoadjuvant chemoradiation therapy

  NIH · $380k · 2018–2020

• Interpretable Deep Learning for Analyzing Brain Development Heterogeneity with Personalized Functional Networks Across Multi-Site Data

  NIH · $5.3M · 2016–2029

• Individualized Closed Loop TMS for Working Memory Enhancement

  NIH · $3.5M · 2019–2024

Frequent coauthors

• Christos Davatzikos

  University of Pennsylvania

  105 shared

• Theodore D. Satterthwaite

  Children's Hospital of Philadelphia

  101 shared

• Hongming Li

  Guangdong Provincial Hospital of Traditional Chinese Medicine

  90 shared

• Chuanjun Zhuo

  Nankai University

  71 shared

• Russell T. Shinohara

  67 shared

• Ruben C. Gur

  Children's Hospital of Philadelphia

  60 shared

• Haochang Shou

  University of Pennsylvania

  59 shared

• Raquel E. Gur

  Children's Hospital of Philadelphia

  58 shared

Labs

• Fan LabPI

Awards & honors

• Richard H. Chamberlain Professor in Research Oncology II
• Member, Abramson Cancer Center, University of Pennsylvania
• Member, Institute for Immunology, University of Pennsylvania
• Member, Cardiovascular Institute, University of Pennsylvania