Precision-mapping Functional Connectivity in Parkinson Disease: Feasibility & Reliability (P7-3.005)

可靠性(半导体) 功能连接 神经科学 疾病 医学 计算机科学 物理医学与康复 心理学 内科学 物理 功率(物理) 量子力学
作者
Meghan C. Campbell,Sarah C. Grossen,Emma Carr,Abdulmunaim M. Eid,Scott A. Norris,Jake Chernicky,Ally Dworetsky,Caterina Gratton
出处
期刊:Neurology [Lippincott Williams & Wilkins]
卷期号:102 (17_supplement_1) 被引量:1
标识
DOI:10.1212/wnl.0000000000206385
摘要

To determine the feasibility and reliability of using precision-mapping techniques for people with Parkinson disease. 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. 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. 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). 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. Disclosure: The institution of Meghan C. Campbell has received research support from NIH. The institution of Meghan C. Campbell has received research support from NIH. The institution of Meghan C. Campbell has received research support from McDonnell Center for Systems Neuroscience. The institution of Meghan C. Campbell has received research support from WUSM Radiology Department. The institution of Meghan C. Campbell has received research support from NIH. Meghan C. Campbell has received personal compensation in the range of $0-$499 for serving as a Grant Reviewer with Parkinson Foundation. Meghan C. Campbell has received personal compensation in the range of $500-$4,999 for serving as a Grant Reviewer with Department of Defense. Ms. Grossen has nothing to disclose. Ms. Carr has nothing to disclose. Dr. Eid has nothing to disclose. The institution of Dr. Norris has received research support from NIH, DMRF, Dysphonia International. Ms. Dworetsky has nothing to disclose. Prof. Gratton has nothing to disclose.
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