# Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $523,997

## Abstract

Abstract. Long-term survival of patients with glioblastomas (GBM) are associated with two competing priorities:
1) gross total resection and 2) preservation of the patient’s function. Stereotactic navigation, in which
reconstructed magnetic resonance images (MRI) of the brain are used for real-time intraoperative anatomic
guidance, has become an essential tool for tumor resection. Further, there are emerging insights that glioma-
specific perturbations of the functional organization of the brain impact the patient’s survival. However, the
current barrier is that there is no FDA approved navigation system that enables the surgeon to visualize the
functional architecture of the brain and the impact a tumor has on the brain’s network organization to inform
prognosis. Resting state functional MRI (rs-fMRI) has emerged as a powerful tool for mapping clinically relevant
brain networks and defining critical glioma-neuronal interactions. rs-fMRI is highly efficient, task independent,
and multiple resting state networks (RSNs) can be mapped simultaneously. With this in mind, the long-term goal
of our research is to improve treatment, survival, and quality of life for patients with brain tumors by improving
the identification of eloquent cortex and providing actionable metrics for survival prognosis to best tailor a
patient’s care. In our first Academic Industry Partnership between Washington University and Medtronic we were
extremely productive in creating an integrated brain-mapping navigation technology using rs-fMRI. Specifically,
we created a robust image acquisition/analysis pipeline that includes pre-processing of raw data, quality control
analytics, and clinical validation demonstrating superior performance over task-based fMRI. We have also been
leaders in deriving prognostic radiomic biomarkers from rs-fMRI. In this continuation, we will build on these
successes. The overall objective is to create advanced rs-fMRI machine learning (ML) tools to more efficiently
and accurately define functional cortex and provide preoperative prognostic metrics of survival as a
comprehensive surgical/care navigation system. We have the expertise, infrastructure, and data, to advance rs-
fMRI to be a powerful tool for neurosurgical decision support. The proposal entails three specific aims: 1)
Advance an ML algorithm to enable more accurate and data efficient rs-fMRI brain-mapping software, 2) Create
an rs-fMRI ML algorithm to preoperatively predict survival in glioblastoma (GBM) patients, and 3) Validate impact
of mapping and prognostic algorithms on clinical decision making in prospective feasibility clinical trial. The
expected outcome of this work will be an integrated imaging/surgical navigation technology using rs-fMRI for
clinical decision support with defined performance, clinical validation, and a regulatory path for FDA clearance.
Thus, this proposal is innovative because 1) the software will map networks with substantially shorter image
acquisition times...

## Key facts

- **NIH application ID:** 10891476
- **Project number:** 5R01CA203861-08
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Eric CLAUDE Leuthardt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $523,997
- **Award type:** 5
- **Project period:** 2017-01-17 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10891476

## Citation

> US National Institutes of Health, RePORTER application 10891476, Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning (5R01CA203861-08). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10891476. Licensed CC0.

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