FMRI ÇALIŞMALARINDA GEREKLİ ÖRNEK BÜYÜKLÜĞÜNÜN PRATİK BİR TAHMİNİ

Year 2023, , 56 - 63, 31.12.2023

Cemre Candemir

https://doi.org/10.22531/muglajsci.1282492

Abstract

Fonksiyonel Manyetik Rezonans Görüntüleme (fMRI) çalışmalarında, fMRI verilerindeki değişkenlik, analizin karmaşıklığı ve çoklu karşılaştırmalar için düzeltme ihtiyacı, uygun örneklem büyüklüğünü belirlemeyi zorlaştırır. Bu nedenle, güç analizi, güvenilir ve istatistiksel olarak anlamlı sonuçlar elde etmek için gereken uygun örneklem büyüklüğünü belirlemek için kullanılan önemli bir araç haline gelir. Bu bağlamda, bu çalışma, bir fMRI çalışması için güç analizi yapma ve örneklem büyüklüğünü tahmin etme sürecini temsil etmeyi amaçlamaktadır. Bunu yapmak için, farklı deneysel görev tasarımlarına sahip işlevsel, duyuşsal, davranışsal ve bilişsel üç veri seti kullanılır. Bu çalışma, güç analizinin nasıl yürütüleceğine ve çeşitli fMRI çalışmaları için örneklem boyutunun nasıl tahmin edileceğine ilişkin adım adım bir kılavuz sağlar.

Keywords

Deneysel ödev tasarımı, fMRI, katılımcı sayısı, güç analizi, örneklem büyüklüğü, istatistiksel anlamlılık

A PRACTICAL ESTIMATION OF THE REQUIRED SAMPLE SIZE IN FMRI STUDIES

Abstract

In functional Magnetic Resonance Imaging (fMRI) studies, the variability in fMRI data, the complexity of the analysis, and the need to correct for multiple comparisons make determining the appropriate sample size challenging. Hence, power analysis becomes an important tool to use for determining the appropriate sample size needed to achieve reliable and statistically significant results. In this context, this study aims to represent the process of conducting a power analysis and estimating the sample size for an fMRI study. To do this, three functional, affective, behavioral, and cognitive, data sets having different experimental task designs are used. This study provides a step-by-step guide on how to conduct a power analysis and estimate the sample size for various fMRI studies.

Keywords

Experimental task design, fMRI, number of participants, power analysis, sample size, statistical significance

Thanks

The author thanks all Socat neuroscience team and Prof. Dr. Ali Saffet Gönül for his encouragement and supervision.

References

• H. Aerts et al., “Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling,” Sci. Data, vol. 9, no. 1, Art. no. 1, Nov. 2022, doi: 10.1038/s41597-022-01806-4.
• S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation.,” Proc. Natl. Acad. Sci. U. S. A., vol. 87, no. 24, pp. 9868–9872, Dec. 1990.
• R. T. Constable, “Challenges in fMRI and Its Limitations,” in Functional MRI: Basic Principles and Clinical Applications, S. H. Faro and F. B. Mohamed, Eds., New York, NY: Springer, 2006, pp. 75–98. doi: 10.1007/0-387-34665-1_4.
• P. Lafaye de Micheaux, B. Liquet, S. Marque, and J. Riou, “Power and sample size determination in clinical trials with multiple primary continuous correlated endpoints,” J. Biopharm. Stat., vol. 24, no. 2, pp. 378–397, 2014, doi: 10.1080/10543406.2013.860156.
• J. Durnez et al., “Power and sample size calculations for fMRI studies based on the prevalence of active peaks.” bioRxiv, p. 049429, Apr. 20, 2016. doi: 10.1101/049429.
• J. A. Mumford, “A power calculation guide for fMRI studies,” Soc. Cogn. Affect. Neurosci., vol. 7, no. 6, pp. 738–742, Aug. 2012, doi: 10.1093/scan/nss059.
• K. S. Button et al., “Power failure: why small sample size undermines the reliability of neuroscience,” Nat. Rev. Neurosci., vol. 14, no. 5, Art. no. 5, May 2013, doi: 10.1038/nrn3475.
• E. Vul and H. Pashler, “Voodoo and circularity errors,” NeuroImage, vol. 62, no. 2, pp. 945–948, Aug. 2012, doi: 10.1016/j.neuroimage.2012.01.027.
• T. Yarkoni, “Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power-Commentary on Vul et al. (2009),” Perspect. Psychol. Sci. J. Assoc. Psychol. Sci., vol. 4, no. 3, pp. 294–298, May 2009, doi: 10.1111/j.1745-6924.2009.01127.x.
• R. A. Poldrack, P. C. Fletcher, R. N. Henson, K. J. Worsley, M. Brett, and T. E. Nichols, “Guidelines for reporting an fMRI study,” NeuroImage, vol. 40, no. 2, pp. 409–414, Apr. 2008, doi: 10.1016/j.neuroimage.2007.11.048.
• B. Thirion, P. Pinel, S. Mériaux, A. Roche, S. Dehaene, and J.-B. Poline, “Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses,” NeuroImage, vol. 35, no. 1, pp. 105–120, Mar. 2007, doi: 10.1016/j.neuroimage.2006.11.054.
• C. Pernet and J.-B. Poline, “Improving functional magnetic resonance imaging reproducibility,” GigaScience, vol. 4, p. 15, Dec. 2015, doi: 10.1186/s13742-015-0055-8.
• Q. Guo, L. Thabane, G. Hall, M. McKinnon, R. Goeree, and E. Pullenayegum, “A systematic review of the reporting of sample size calculations and corresponding data components in observational functional magnetic resonance imaging studies,” NeuroImage, vol. 86, pp. 172–181, Feb. 2014, doi: 10.1016/j.neuroimage.2013.08.012.
• D. Szucs and J. PA. Ioannidis, “Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals,” NeuroImage, vol. 221, p. 117164, Nov. 2020, doi: 10.1016/j.neuroimage.2020.117164.
• H. R. Cremers, T. D. Wager, and T. Yarkoni, “The relation between statistical power and inference in fMRI,” PLOS ONE, vol. 12, no. 11, p. e0184923, Nov. 2017, doi: 10.1371/journal.pone.0184923.
• “Stata Programs for Data Analysis.” https://stats.oarc.ucla.edu/stata/ado/analysis/ (accessed Apr. 11, 2023).
• O. T. Bişkin, C. Candemir, A. S. Gonul, and M. A. Selver, “Diverse Task Classification from Activation Patterns of Functional Neuro-Images Using Feature Fusion Module,” Sensors, vol. 23, no. 7, Art. no. 7, Jan. 2023, doi: 10.3390/s23073382.
• J. Ashburner, “SPM: A history,” NeuroImage, vol. 62, no. 2, pp. 791–800, Aug. 2012, doi: 10.1016/j.neuroimage.2011.10.025.
• “NeuroPower | NeuroPowerTools.” http://neuropowertools.org/neuropower/neuropowerstart/ (accessed Apr. 11, 2023).
• K. J. Worsley, “Developments in random field theory,” in Human Brain Function, R. S. J. Frackowiak, K. J. Friston, C. Frith, R. Dolan, K. J. Friston, C. J. Price, S. Zeki, J. Ashburner, and W. D. Penny, Eds., 2nd ed.Academic Press, 2003.
• W. Haynes, “Benjamini–Hochberg Method,” in Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K.-H. Cho, and H. Yokota, Eds., New York, NY: Springer, 2013, pp. 78–78. doi: 10.1007/978-1-4419-9863-7_1215.
• B. Walsh, “Multiple comparisons,” Cornell University, 2004.
• H. Naouma and T. C. Pataky, “A comparison of random-field-theory and false-discovery-rate inference results in the analysis of registered one-dimensional biomechanical datasets,” PeerJ, vol. 7, p. e8189, Dec. 2019, doi: 10.7717/peerj.8189.
• R. A. Poldrack et al., “Scanning the horizon: towards transparent and reproducible neuroimaging research,” Nat. Rev. Neurosci., vol. 18, no. 2, pp. 115–126, Feb. 2017, doi: 10.1038/nrn.2016.167.