Journal
NEUROIMAGE
Volume 57, Issue 1, Pages 19-21Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.02.076
Keywords
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Funding
- NCCIH NIH HHS [RC1 AT005728-02] Funding Source: Medline
- NCRR NIH HHS [P41 RR014075-09, S10 RR019307, S10 RR023401, U24 RR021382, S10 RR023043, U24 RR021382-05, S10 RR023043-01, S10 RR023401-01A2, P41 RR014075, S10 RR019307-01] Funding Source: Medline
- NIA NIH HHS [R01 AG022381, R01 AG022381-10] Funding Source: Medline
- NIBIB NIH HHS [R01 EB006758-04, R01 EB006758] Funding Source: Medline
- NIMH NIH HHS [U01 MH093765-03, U01 MH093765] Funding Source: Medline
- NINDS NIH HHS [R01 NS070963-02, P01 NS058793, R01 NS052585-05, R01 NS042861, R21 NS072652, R01 NS052585, R01 NS042861-09, R21 NS072652-02, R01 NS070963, P01 NS058793-05] Funding Source: Medline
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Longitudinal image processing procedures frequently transfer or pool information across time within subject, with the dual goals of reducing the variability and increasing the accuracy of the derived measures. In this note, we discuss common difficulties in longitudinal image processing, focusing on the introduction of bias, and describe the approaches we have taken to avoid them in the FreeSurfer longitudinal processing stream. (C) 2011 Elsevier Inc. All rights reserved.
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