Evidence map›Paper›PMID 40990107›Full record

ArticleMovement disorders : official journal of the Movement Disorder Society2026

Preoperative Functional Connectivity Predicts Antiparkinson Drug Change after Deep Brain Stimulation.

David Mikhael, Skyler Deutsch, Juhi Mehta, Sarah Wang, John Kornak, Philip A Starr, Jill L Ostrem, Doris D Wang, Ian O Bledsoe, Melanie A Morrison

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Movement disorders : official journal of the Movement Disorder Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

David MikhaelDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-6188-3906
Skyler DeutschDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Juhi MehtaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Sarah WangDepartment of Neurology, University of California, San Francisco, San Francisco, California, USA.
John KornakDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
Philip A StarrUCSF-UC Berkeley Joint PhD Program in Bioengineering, University of California, San Francisco, Berkeley, California, USA.
Jill L OstremDepartment of Neurology, University of California, San Francisco, San Francisco, California, USA.
Doris D WangUCSF-UC Berkeley Joint PhD Program in Bioengineering, University of California, San Francisco, Berkeley, California, USA.
Ian O BledsoeDepartment of Neurology, University of California, San Francisco, San Francisco, California, USA.
Melanie A MorrisonDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.

Funding

MULTIMODAL MRI TO PREDICT DBS MOTOR AND COGNITIVE OUTCOMES IN PARKINSON’S DISEASER01NS130066 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Melanie A Morrison · 2022 to 2026
$3.3M
NINDS NIH HHS R01 NS130066NINDS NIH HHS R01NS130066
6 · The paper itself

Abstract

backgroundDeep brain stimulation (DBS) can effectively ameliorate motor symptoms in Parkinson's disease (PD), but patient outcomes remain variable. Clinical predictors lack reliability and only explain a small proportion of outcome variance, outlining a need for biomarkers that can enhance prediction accuracy. Functional magnetic resonance imaging (fMRI) could address this, offering insight into the relative impact of functional brain health on DBS outcomes.

methodsPreoperative resting-state fMRI was retrospectively collected for 120 patients with PD and DBS targeting the subthalamic nucleus or pallidum. Motor network connectivity was computed, and clinical predictors extracted including age, sex, target, hemisphere treated, and preoperative medication responsiveness. Pre-to-post changes in antiparkinson medications defined outcomes. Regression analysis selected important connectivity features and evaluated additional outcome variance explained by fMRI when combined with clinical predictors. Last, classification analysis assessed fMRI feature specificity to PD compared to Huntington's disease and healthy controls.

resultsAlongside clinical predictors, fMRI features explained more outcome variance (R

conclusionPatient-specific motor connectivity enhances DBS outcome prediction and could aid detection and monitoring of basal ganglia disorders. Future work will validate and extend our models for multi-parameter MR prediction of standardized outcomes, toward the development of decision support tools for DBS. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Indexed as

Antiparkinson AgentsDeep Brain StimulationParkinson DiseaseAgedFemaleGlobus PallidusHumansHuntington DiseaseMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesSubthalamic NucleusAntiparkinson Agentsdeep brain stimulationfunctional magnetic resonance imagingHuntington's diseaselevodopa equivalent daily doseParkinson's disease

Identifiers

PMID40990107
PMCPMC12882056

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.