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Meghan C. Campbell

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2 papers

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YNICL Journal 2026 Journal Article

Towards precision functional brain network mapping in Parkinson’s disease

Jacob Chernicky
Ally Dworetsky
Sarah Grossen
Emma Carr
Abdulmunaim Eid
Meghan C. Campbell
Caterina Gratton

BACKGROUND: Parkinson's disease (PD) is a complex neurodegenerative condition that leads to widespread disruption of large-scale brain networks and is further complicated by substantial individual variability in symptomology, progression rates, and treatment response. Consequently, the investigation of individual differences in networks measured via resting state functional connectivity (RSFC) may provide insight. However, most RSFC studies are unable to identify interindividual differences due to poor reliability and group average network definitions. "Precision" RSFC addresses these shortcomings through extended data collection, strict denoising, and individual network definition, but remains untested in PD. OBJECTIVES: To evaluate the feasibility and reliability of precision RSFC studies in PD. METHODS: We collected > 100 min of RSFC data from 20 PD and 10 healthy control participants. We evaluated the level of motion, reliability and stability of RSFC measures in each participant, as well as compared to a conventional 5 min of RSFC data. These measures were benchmarked against HC to evaluate comparability. In addition, we created individualized brain network measures in PD participants to establish feasibility in this population. RESULTS: Using precision RSFC methods, the PD group produced reliable and stable measures of brain networks that were comparable in quality to healthy controls and substantially exceeded those derived from conventional approaches (whole-brain reliability: 5 min. r = 0.60 ± 0.06, 40 min. r = 0.88 ± 0.04; within-person stability: 5 min. r = 0.40 ± 0.08, 25 min. r = 0.68 ± 0.07; ps < 0.001). Individualized network maps in people with PD captured variation both from group-averaged templates and between individuals, including within motor-related networks. CONCLUSION: Precision RSFC is feasible and reliable in individuals with PD. This approach holds promise for advancing personalized diagnostics and identifying brain-based biomarkers underlying clinical variability in PD.

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YNIMG Journal 2020 Journal Article

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

Caterina Gratton
Ally Dworetsky
Rebecca S. Coalson
Babatunde Adeyemo
Timothy O. Laumann
Gagan S. Wig
Tania S. Kong
Gabriele Gratton

Denoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i. e. , ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (>0. 1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2. 0–2. 5 s) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2. 0 to 2. 5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.

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