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001038546 0247_ $$2doi$$a10.1101/2024.11.26.625368
001038546 037__ $$aFZJ-2025-01529
001038546 1001_ $$0P:(DE-Juel1)190306$$aSasse, Leonard$$b0
001038546 245__ $$aProcrustes Alignment in Individual-level Analyses of Functional Gradients
001038546 260__ $$c2024
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001038546 520__ $$aFunctional connectivity (FC) gradients provide valuable insights into individual differences in brain organization, yet aligning these gradients across individuals poses challenges. Procrustes alignment is often employed to standardize gradients across multiple subjects, but the choice of the number of gradients used in alignment introduces complexities that may impact individual-level analyses. In this study, we systematically investigate the impact of varying gradient counts in Procrustes alignment on the principal FC gradient, using data from four resting state fMRI datasets, including the Human Connectome Project (HCP-YA), Amsterdam Open MRI Collection (AOMIC) PIOP1 and PIOP2, and Cambridge Centre for Ageing and Neuroscience (Cam-CAN). We find that increasing the number of gradients used in alignment enhances identification accuracy but can reduce differential identifiability, as additional gradients risk introducing nuisance signals such as motion back into the principal gradient. To further probe these effects, machine learning to predict fluid intelligence and age, and a motion prediction analysis, revealing that higher alignment gradient counts may leak information from lower gradients into the principal gradient for individual-level analyses. These findings highlight the trade-off between alignment precision and the potential reintroduction of noise.
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001038546 7001_ $$0P:(DE-Juel1)187055$$aPaquola, Casey$$b1
001038546 7001_ $$0P:(DE-Juel1)177727$$aDukart, Juergen$$b2
001038546 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b3
001038546 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b4
001038546 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b5$$eCorresponding author
001038546 773__ $$a10.1101/2024.11.26.625368
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001038546 9141_ $$y2024
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