Brain Tumors

BOLD Asynchrony

BOLD asynchrony elucidates tumor burden in IDH-mutated gliomas (Neuro-Oncology, 2022) – BOLD asynchrony is proportional to tumor burden in IDH-mutated gliomas, is more sensitive to tumor burden than standard-of-care MR imaging, and can be used for neurosurgical planning of extent of resection.

Asynchrony in Peritumoral Resting-State Blood Oxygen Level–Dependent fMRI Predicts Meningioma Grade and Invasion (American Journal of Neuroradiology, 2021) – High-grade infiltrating meningioma disrupts neurovascular coupling which results in elevated BOLD asynchrony.

Extent of BOLD vascular dysregulation is greater in diffuse gliomas without isocitrate dehydrogenase 1 R132H mutation (Radiology, 2018) – The spatial distribution of BOLD asynchrony differs between IDH1 wild-type and IDH1 mutated gliomas. BOLD asynchrony extends further beyond visible tumor in IDH1 wild-type tumors suggesting a greater degree of infiltration. Gross total resection of IDH1 mutated tumors results in 5-10% of residual tumor. Gross total resection of IDH1 wild type tumors results in ~40% of residual tumor, suggesting one of the main factors in the high rate of recurrence in IDH1 wild-type gliomas.

Local glioma cells are associated with vascular dysregulation (American Journal of Neuroradiology, 2018) – The BOLD asynchrony signal is directly related to tumor burden indicating that infiltrating glioma cells disrupt neurovascular coupling.

Glioblastoma induces vascular dysregulation in non-enhancing peritumoral regions in humans (American Journal of Roentgenology, 2016) – A description of the methodology behind the BOLD asynchrony technique for detecting non-enhancing glioma.


Optimizing neuro-oncology imaging: a review of deep learning approaches for glioma imaging (Cancers, 2019)

A Simple Automated Method for Detecting Recurrence in High-Grade Glioma (American Journal of Neuroradiology, 2019)

A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies (American Journal of Neuroradiology, 2018)

Imaging Genetic Heterogeneity in Glioblastoma and Other Glial Tumors: Review of Current Methods and Future Directions (American Journal of Roentgenology, 2018)

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas (American Journal of Neuroradiology, 2018)

Sodium fluorescein facilitates guided sampling of diagnostic tumor tissue in non-enhancing gliomas (Neurosurgery, 2017)


Glioma-Induced Alterations in Neuronal Activity and Neurovascular Coupling during Disease Progression (Cell Reports, 2020)