Top

Published in:

01-04-2022 | Magnetic Resonance Imaging | Functional Neuroradiology

Different FreeSurfer versions might generate different statistical outcomes in case–control comparison studies

Authors: Pavel Filip, Petr Bednarik, Lynn E. Eberly, Amir Moheet, Alena Svatkova, Heidi Grohn, Anjali F. Kumar, Elizabeth R. Seaquist, Silvia Mangia

Published in: Neuroradiology | Issue 4/2022

Abstract

Purpose

Neuroimaging pipelines have long been known to generate mildly differing results depending on various factors, including software version. While considered generally acceptable and within the margin of reasonable error, little is known about their effect in common research scenarios such as inter-group comparisons between healthy controls and various pathological conditions. The aim of the presented study was to explore the differences in the inferences and statistical significances in a model situation comparing volumetric parameters between healthy controls and type 1 diabetes patients using various FreeSurfer versions.

Methods

T1- and T2-weighted structural scans of healthy controls and type 1 diabetes patients were processed with FreeSurfer 5.3, FreeSurfer 5.3 HCP, FreeSurfer 6.0 and FreeSurfer 7.1, followed by inter-group statistical comparison using outputs of individual FreeSurfer versions.

Results

Worryingly, FreeSurfer 5.3 detected both cortical and subcortical volume differences out of the preselected regions of interest, but newer versions such as FreeSurfer 5.3 HCP and FreeSurfer 6.0 reported only subcortical differences of lower magnitude and FreeSurfer 7.1 failed to find any statistically significant inter-group differences.

Conclusion

Since group averages of individual FreeSurfer versions closely matched, in keeping with previous literature, the main origin of this disparity seemed to lie in substantially higher within-group variability in the model pathological condition. Ergo, until validation in common research scenarios as case–control comparison studies is included into the development process of new software suites, confirmatory analyses utilising a similar software based on analogous, but not fully equivalent principles, might be considered as supplement to careful quality control.

Available only for authorised users

Bednarik P, Moheet AA, Grohn H, Kumar AF, Eberly LE, Seaquist ER, Mangia S (2017) Type 1 Diabetes and impaired awareness of hypoglycemia are associated with reduced brain gray matter volumes. Front Neurosci 11:529. https://doi.org/10.3389/fnins.2017.00529CrossRefPubMedPubMedCentral

Bigler, E. D., Skiles, M., Wade, B. S. C., Abildskov, T. J., Tustison, N. J., Scheibel, R. S., et al. (2020). FreeSurfer 5.3 versus 6.0: are volumes comparable? A chronic effects of neurotrauma consortium study. Brain Imaging and Behavior, 14(5), 1318–1327. https://doi.org/10.1007/s11682-018-9994-x

Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ (2004) A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23(2):724–738CrossRef

Cardinale F, Chinnici G, Bramerio M, Mai R, Sartori I, Cossu M et al (2014) Validation of FreeSurfer-estimated brain cortical thickness: comparison with histologic measurements. Neuroinformatics 12(4):535–542CrossRef

Chepkoech J-L, Walhovd KB, Grydeland H, Fjell AM, Initiative ADN (2016) Effects of change in FreeSurfer version on classification accuracy of patients with Alzheimer’s disease and mild cognitive impairment. Hum Brain Mapp 37(5):1831–1841CrossRef

Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRef

Filip P, Canna A, Moheet A, Bednarik P, Grohn H, Li X et al (2020) Structural Alterations in Deep Brain Structures in Type 1 Diabetes. Diabetes. https://doi.org/10.2337/db19-1100CrossRefPubMedPubMedCentral

Fischl B (2012) FreeSurfer. NeuroImage 62(2):774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021CrossRefPubMed

Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL et al (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105–124CrossRef

10.

Glatard, T., Lewis, L. B., Ferreira da Silva, R., Adalat, R., Beck, N., Lepage, C., et al. (2015). Reproducibility of neuroimaging analyses across operating systems. Front Neuroinform, 9. https://doi.org/10.3389/fninf.2015.00012

11.

Gronenschild EHBM, Habets P, Jacobs HIL, Mengelers R, Rozendaal N, van Os J, Marcelis M (2012) The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS ONE 7(6):e38234. https://doi.org/10.1371/journal.pone.0038234CrossRefPubMedPubMedCentral

12.

Hammers A, Heckemann R, Koepp MJ, Duncan JS, Hajnal JV, Rueckert D, Aljabar P (2007) Automatic detection and quantification of hippocampal atrophy on MRI in temporal lobe epilepsy: a proof-of-principle study. Neuroimage 36(1):38–47CrossRef

13.

Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S et al (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32(1):180–194CrossRef

14.

Hlavatá, P., Linhartová, P., Šumec, R., Filip, P., Světlák, M., Baláž, M., et al. (2020). Behavioral and Neuroanatomical Account of Impulsivity in Parkinson’s Disease. Front Neurol, 10. https://doi.org/10.3389/fneur.2019.01338

15.

Hughes TM, Ryan CM, Aizenstein HJ, Nunley K, Gianaros PJ, Miller R et al (2013) Frontal gray matter atrophy in middle aged adults with type 1 diabetes is independent of cardiovascular risk factors and diabetes complications. J Diabetes Complications 27(6):558–564. https://doi.org/10.1016/j.jdiacomp.2013.07.001CrossRefPubMed

16.

Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B et al (2009) MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 46(1):177–192CrossRef

17.

Messina D, Cerasa A, Condino F, Arabia G, Novellino F, Nicoletti G et al (2011) Patterns of brain atrophy in Parkinson’s disease, progressive supranuclear palsy and multiple system atrophy. Parkinsonism Relat Disord 17(3):172–176. https://doi.org/10.1016/j.parkreldis.2010.12.010CrossRefPubMed

18.

Morey RA, Petty CM, Xu Y, Hayes JP, Wagner HR II, Lewis DV et al (2009) A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. Neuroimage 45(3):855–866CrossRef

19.

Morey RA, Selgrade ES, Wagner HR, Huettel SA, Wang L, McCarthy G (2010) Scan–rescan reliability of subcortical brain volumes derived from automated segmentation. Hum Brain Mapp 31(11):1751–1762PubMedPubMedCentral

20.

Moulton CD, Costafreda SG, Horton P, Ismail K, Fu CHY (2015) Meta-analyses of structural regional cerebral effects in type 1 and type 2 diabetes. Brain Imaging Behav 9(4):651–662. https://doi.org/10.1007/s11682-014-9348-2CrossRefPubMed

21.

Musen G, Lyoo IK, Sparks CR, Weinger K, Hwang J, Ryan CM et al (2006) Effects of type 1 diabetes on gray matter density as measured by voxel-based morphometry. Diabetes 55(2):326–333CrossRef

22.

Nordenskjöld R, Malmberg F, Larsson E-M, Simmons A, Brooks SJ, Lind L et al (2013) Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements. Neuroimage 83:355–360. https://doi.org/10.1016/j.neuroimage.2013.06.068CrossRefPubMed

23.

Rajagopalan V, Pioro EP (2015) Disparate voxel based morphometry (VBM) results between SPM and FSL softwares in ALS patients with frontotemporal dementia: which VBM results to consider? BMC Neurol 15(1):32. https://doi.org/10.1186/s12883-015-0274-8CrossRefPubMedPubMedCentral

24.

Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH et al (2002) Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology 58(5):695–701CrossRef

25.

Sargolzaei S, Sargolzaei A, Cabrerizo M, Chen G, Goryawala M, Noei S et al (2015) A practical guideline for intracranial volume estimation in patients with Alzheimer’s disease. BMC Bioinformatics 16(Suppl 7):S8. https://doi.org/10.1186/1471-2105-16-S7-S8CrossRefPubMedPubMedCentral

26.

Tae WS, Kim SS, Lee KU, Nam E-C, Kim KW (2008) Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder. Neuroradiology 50(7):569CrossRef

27.

Vasconcellos LF, Pereira JS, Adachi M, Greca D, Cruz M, Malak AL, Charchat-Fichman H (2018) Volumetric brain analysis as a predictor of a worse cognitive outcome in Parkinson’s disease. J Psychiatr Res 102:254–260. https://doi.org/10.1016/j.jpsychires.2018.04.016CrossRefPubMed

28.

Wessels AM, Simsek S, Remijnse PL, Veltman DJ, Biessels GJ, Barkhof F et al (2006) Voxel-based morphometry demonstrates reduced grey matter density on brain MRI in patients with diabetic retinopathy. Diabetologia 49(10):2474–2480. https://doi.org/10.1007/s00125-006-0283-7CrossRefPubMed

Title: Different FreeSurfer versions might generate different statistical outcomes in case–control comparison studies
Authors: Pavel Filip
Petr Bednarik
Lynn E. Eberly
Amir Moheet
Alena Svatkova
Heidi Grohn
Anjali F. Kumar
Elizabeth R. Seaquist
Silvia Mangia
Publication date: 01-04-2022
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Type 1 Diabetes
Published in: Neuroradiology / Issue 4/2022
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-021-02862-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Different FreeSurfer versions might generate different statistical outcomes in case–control comparison studies

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 4/2022

Correction to: The role of shuntography in diagnosis of mechanic complications after implantation of ventriculoperitoneal shunts in patients with idiopathic normal pressure hydrocephalus: a retrospective clinical evaluation

Usefulness of rapid MR angiography using two-point Dixon for evaluating carotid and aortic plaques

Spatial coefficient of variation of arterial spin labeling MRI for detecting hemodynamic disturbances measured with 15O-gas PET in patients with moyamoya disease

“Within a minute” detection of focal cortical dysplasia

Value of ultra-high field MRI in patients with suspected focal epilepsy and negative 3 T MRI (EpiUltraStudy): protocol for a prospective, longitudinal therapeutic study

Dynamic susceptibility MR perfusion imaging of the brain: not a question of contrast agent molarity