# MULTIMODAL MRI TO PREDICT DBS MOTOR AND COGNITIVE OUTCOMES IN PARKINSON’S DISEASE

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $661,509

## Abstract

PROJECT SUMMARY/ABSTRACT
Implantable deep brain stimulation (DBS) is a second-line surgical neuromodulation for Parkinson's disease (PD)
that can provide significant relief of motor symptoms when medications become less effective, however there
are currently no reliable predictors of therapeutic efficacy. While the gold standard suggests that a patient will
benefit from DBS if their motor symptoms respond to PD medications with at least 30% improvement, the pre-
dictive accuracy of this criteria is variable across studies, and has been disproportionately evaluated in the con-
text of only one of two common brain targets for PD. A lack of reliable prognostic criteria to predict overall out-
comes with DBS, including risk for cognitive side-effects in balance with motor symptom improvement, has led
to variable patient outcomes. Some not considered candidates by the gold standard have been reported to re-
spond well to DBS, while others have experienced limited benefit despite strong candidacy and well positioned
electrodes. With over 4000 DBS surgeries performed in the US for PD each year, there is an increasing demand
for better prognostic tools and streamlined approaches to inform optimal candidate and brain target selection.
We aim to address this unmet need by leveraging advanced MRI techniques for improved prediction of patient
outcomes after one year of DBS. Previous studies have shown that measures of brain connectivity derived from
functional MRI (fMRI) and diffusion tensor imaging (DTI), can be used to predict motor symptom response to
DBS. Brain iron accumulation in the basal ganglia, a marker of PD severity derived from susceptibility contrast
on T2* MRI, has also shown promise for predicting DBS motor outcomes. However, practical implementation of
the results from previous studies in the pre-operative setting is limited by the use of normative connectomes,
post-operative electrode coordinates, and less sensitive susceptibility techniques for prediction, along with out-
come data from only one of two brain targets for PD. To overcome these limitations, we will use patient-specific
pre-operative MRI data to predict outcomes for both PD targets. Specifically, we propose a novel multivariate
approach that incorporates fMRI and DTI with quantitative susceptibility mapping (QSM), a superior susceptibility
technique to T2* MRI, to enhance prediction accuracy. By using complimentary features of disease burden that
are highly relevant to DBS effects on brain connectivity and individual basal ganglia structures, we expect that
our approach will improve upon the current gold standard.
In 100 patients with PD undergoing DBS, we aim to: 1) evaluate the impact of 3T MRI on clinical prediction of
motor outcomes, 2) identify MR and clinical features most relevant for predicting overall versus individual motor
and cognitive outcomes, and 3) investigate additional variance in patient outcomes explained by post-operative
targeting accuracy. The results wi...

## Key facts

- **NIH application ID:** 10565545
- **Project number:** 1R01NS130066-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Melanie A Morrison
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $661,509
- **Award type:** 1
- **Project period:** 2022-09-19 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10565545

## Citation

> US National Institutes of Health, RePORTER application 10565545, MULTIMODAL MRI TO PREDICT DBS MOTOR AND COGNITIVE OUTCOMES IN PARKINSON’S DISEASE (1R01NS130066-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10565545. Licensed CC0.

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