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Hi all,
I am processing a large longitudinal dataset of T1-weighted anatomical
images through the recon-all pipeline (N=680). I know that the gold
standard for quality control following recon-all is visual inspection of
the output surfaces and parcellations. However, given the large sample
size, I was writing to see if the community has any recommendations for
automatic QC pipelines.
I read a few papers discussing the use of Euler numbers to detect outlier
datasets for exclusion (see citations below). Does anyone have any other
recommendations?
Thank you in advance!
Best,
Jenna
References:
1. Rosen, A. F., Roalf, D. R., Ruparel, K., Blake, J., Seelaus, K., Villa,
L. P., ... & Satterthwaite, T. D. (2018). Quantitative assessment of
structural image quality. *Neuroimage*, *169*, 407-418.
2. de Lange, A. M. G., Kaufmann, T., Quintana, D. S., Winterton, A.,
Andreassen, O. A., Westlye, L. T., & Ebmeier, K. P. (2021). Prominent
health problems, socioeconomic deprivation, and higher brain age in lonely
and isolated individuals: A population-based study. *Behavioural Brain
Research*, *414*, 113510.