2024 American Academy of Neurology Abstract Website

Objective:

To determine the feasibility and reliability of using precision-mapping techniques for people with Parkinson disease.

Background:

Standard resting-state functional connectivity (RSFC) approaches collect small amounts of data, typically ≤ 10 min, and rely on group-average network definitions. An innovative new approach applies precision-mapping techniques, with > 40min of data, to identify individual-level RSFC network maps. Precision-mapping RSFC reveals individual differences in network size, strength, and location.

Design/Methods:

We tested the feasibility and reliability of precision-mapping RSFC for people with Parkinson disease. Participants completed multiple fMRI sessions (3-5) up to seven months apart. Using stringent motion censoring, we determined the amount of low-motion, high quality fMRI data per person to establish feasibility. We compared the similarity of RSFC maps across sessions to examine stability and applied split-half analyses to measure the reliability of RSFC maps based on amount fMRI data.

Results:

Preliminary analyses reveal the high feasibility and strong reliability of precision-mapping RSFC for people with Parkinson disease. All participants completed multiple fMRI sessions with large amounts of low motion data for each person (>40 min per person, frame retention average = 75%). Individual participant RSFC maps were stable across sessions (r > 0.7) and highly reliable with >40min of data (split-half reliability, r > 0.8).

Conclusions:

These results demonstrate the feasibility and reliability of using the precision-mapping technique for identifying individual-level RSFC networks in Parkinson disease. With this approach, it will now be possible to examine how individual-level variability of RSFC networks relates to variability in clinical manifestations and predicts progression of Parkinson disease.