» Articles » PMID: 24736173

Reliability of Functional Magnetic Resonance Imaging Activation During Working Memory in a Multi-site Study: Analysis from the North American Prodrome Longitudinal Study

Abstract

Multi-site neuroimaging studies offer an efficient means to study brain functioning in large samples of individuals with rare conditions; however, they present new challenges given that aggregating data across sites introduces additional variability into measures of interest. Assessing the reliability of brain activation across study sites and comparing statistical methods for pooling functional data are critical to ensuring the validity of aggregating data across sites. The current study used two samples of healthy individuals to assess the feasibility and reliability of aggregating multi-site functional magnetic resonance imaging (fMRI) data from a Sternberg-style verbal working memory task. Participants were recruited as part of the North American Prodrome Longitudinal Study (NAPLS), which comprises eight fMRI scanning sites across the United States and Canada. In the first study sample (n=8), one participant from each home site traveled to each of the sites and was scanned while completing the task on two consecutive days. Reliability was examined using generalizability theory. Results indicated that blood oxygen level-dependent (BOLD) signal was reproducible across sites and was highly reliable, or generalizable, across scanning sites and testing days for core working memory ROIs (generalizability ICCs=0.81 for left dorsolateral prefrontal cortex, 0.95 for left superior parietal cortex). In the second study sample (n=154), two statistical methods for aggregating fMRI data across sites for all healthy individuals recruited as control participants in the NAPLS study were compared. Control participants were scanned on one occasion at the site from which they were recruited. Results from the image-based meta-analysis (IBMA) method and mixed effects model with site covariance method both showed robust activation in expected regions (i.e. dorsolateral prefrontal cortex, anterior cingulate cortex, supplementary motor cortex, superior parietal cortex, inferior temporal cortex, cerebellum, thalamus, basal ganglia). Quantification of the similarity of group maps from these methods confirmed a very high (96%) degree of spatial overlap in results. Thus, brain activation during working memory function was reliable across the NAPLS sites and both the IBMA and mixed effects model with site covariance methods appear to be valid approaches for aggregating data across sites. These findings indicate that multi-site functional neuroimaging can offer a reliable means to increase power and generalizability of results when investigating brain function in rare populations and support the multi-site investigation of working memory function in the NAPLS study, in particular.

Citing Articles

Identifying subgroups of urge suppression in Obsessive-Compulsive Disorder using machine learning.

Eng G, De Nadai A, Collins K, Recchia N, Tobe R, Bragdon L J Psychiatr Res. 2024; 177:129-139.

PMID: 39004004 PMC: 11409861. DOI: 10.1016/j.jpsychires.2024.06.052.


Somatotopic disruption of the functional connectivity of the primary sensorimotor cortex in complex regional pain syndrome type 1.

Hotta J, Saari J, Harno H, Kalso E, Forss N, Hari R Hum Brain Mapp. 2023; 44(17):6258-6274.

PMID: 37837646 PMC: 10619416. DOI: 10.1002/hbm.26513.


Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data.

Artiles O, Al Masry Z, Saeed F Neuroinformatics. 2023; 21(4):651-668.

PMID: 37581850 PMC: 11877654. DOI: 10.1007/s12021-023-09639-1.


A longitudinal multi-scanner multimodal human neuroimaging dataset.

Hawco C, Dickie E, Herman G, Turner J, Argyelan M, Malhotra A Sci Data. 2022; 9(1):332.

PMID: 35701471 PMC: 9198098. DOI: 10.1038/s41597-022-01386-3.


Relationships between interoceptive sensibility and resting-state functional connectivity of the insula in obsessive-compulsive disorder.

Eng G, Collins K, Brown C, Ludlow M, Tobe R, Iosifescu D Cereb Cortex. 2022; 32(23):5285-5300.

PMID: 35257146 PMC: 9712718. DOI: 10.1093/cercor/bhac014.


References
1.
Wager T, Nichols T . Optimization of experimental design in fMRI: a general framework using a genetic algorithm. Neuroimage. 2003; 18(2):293-309. DOI: 10.1016/s1053-8119(02)00046-0. View

2.
Voytek B, Knight R . Prefrontal cortex and basal ganglia contributions to visual working memory. Proc Natl Acad Sci U S A. 2010; 107(42):18167-72. PMC: 2964236. DOI: 10.1073/pnas.1007277107. View

3.
Suckling J, Ohlssen D, Andrew C, Johnson G, Williams S, Graves M . Components of variance in a multicentre functional MRI study and implications for calculation of statistical power. Hum Brain Mapp. 2007; 29(10):1111-22. PMC: 6871081. DOI: 10.1002/hbm.20451. View

4.
Yendiki A, Greve D, Wallace S, Vangel M, Bockholt J, Mueller B . Multi-site characterization of an fMRI working memory paradigm: reliability of activation indices. Neuroimage. 2010; 53(1):119-31. PMC: 3810293. DOI: 10.1016/j.neuroimage.2010.02.084. View

5.
Cicchetti D, Sparrow S . Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. Am J Ment Defic. 1981; 86(2):127-37. View