Abstract
Advancements in population neuroscience are spurred by the availability of large scale, open datasets, such as the Human Connectome Project or recently introduced UK Biobank. With the increasing data availability, analyses of brain imaging data employ more and more sophisticated machine learning algorithms. However, all machine learning algorithms must balance generalization and complexity. As the detail of neuroimaging data leads to high-dimensional data spaces, model complexity and hence the chance of overfitting increases. Different methodological approaches can be applied to alleviate the problems that arise in high-dimensional settings by reducing the original information into meaningful and concise features. One popular approach is dimensionality reduction, which allows to summarize high-dimensional data into low-dimensional representations while retaining relevant trends and patterns. In this paper, principal component analysis (PCA) is discussed as widely used dimensionality reduction method based on current examples of population-based neuroimaging analyses.
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References
Editorial. Daunting data. Nature. 2016;539:467–8.
Smith SM, Nichols TE. Statistical challenges in “big data” human neuroimaging. Neuron. 2018;97:263. https://doi.org/10.1016/j.neuron.2017.12.018.
Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K. The WU-minn human connectome project: an overview. NeuroImage. 2013;80:62. https://doi.org/10.1016/j.neuroimage.2013.05.041.
Thompson PM, Stein JL, Medland SE, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8:153. https://doi.org/10.1007/s11682-013-9269-5.
Jack CR, Barnes J, Bernstein MA, et al. Magnetic resonance imaging in Alzheimer’s disease neuroimaging initiative 2. Alzheimers Dement. 2015;11:740. https://doi.org/10.1016/j.jalz.2015.05.002.
Weiner MW, Veitch DP, Aisen PS, et al. 2014 Update of the Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement. 2015;11:e1. https://doi.org/10.1016/j.jalz.2014.11.001.
Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 2005;1:55. https://doi.org/10.1016/j.jalz.2005.06.003.
Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779.
Miller KL, Alfaro-Almagro F, Bangerter NK, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19:1523. https://doi.org/10.1038/nn.4393.
Kernbach JM, Yeo BTT, Smallwood J, et al. Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants. Proc Natl Acad Sci U S A. 2018;115(48):12295–300.
Kiesow H, Dunbar RIM, Kable JW, Kalenscher T, Vogeley K, Schilbach L, Marquand AF, Wiecki TV, Bzdok D. 10,000 social brains: sex differentiation in human brain anatomy. Sci Adv. 2020;6:eaa1170. https://doi.org/10.1126/sciadv.aaz1170.
Spreng RN, Dimas E, Mwilambwe-Tshilobo L, et al. The default network of the human brain is associated with perceived social isolation. Nat Commun. 2020;11(1):6393.
Breiman L. Statistical modeling: the two cultures. Stat Sci. 2001;16:199.
Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. New York, NY: Springer; 2009.
Abu-Mostafa YS, Malik M-I, Lin HT. Learning from data: a short course. Chicago, IL: AMLBook; 2012. https://doi.org/10.1108/17538271011063889.
Alfaro-Almagro F, Jenkinson M, Bangerter NK, et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage. 2018;166:400. https://doi.org/10.1016/j.neuroimage.2017.10.034.
Craddock RC, James GA, Holtzheimer PE, Hu XP, Mayberg HS. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp. 2012;33:1914. https://doi.org/10.1002/hbm.21333.
James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in R. New York, NY: Springer; 2013.
Smith SM, Nichols TE, Vidaurre D, Winkler AM, Behrens TEJ, Glasser MF, Ugurbil K, Barch DM, Van Essen DC, Miller KL. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015;18:1565. https://doi.org/10.1038/nn.4125.
Smith SM, Hyvärinen A, Varoquaux G, Miller KL, Beckmann CF. Group-PCA for very large fMRI datasets. NeuroImage. 2014;101:738. https://doi.org/10.1016/j.neuroimage.2014.07.051.
Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004;23:137. https://doi.org/10.1109/TMI.2003.822821.
Lever J, Krzywinski M, Altman N. Points of significance: principal component analysis. Nat Methods. 2017;14:641. https://doi.org/10.1038/nmeth.4346.
Hastie T, Tibshirani R, James G, Witten D. An introduction to statistical learning. New York, NY: Springer; 2006. https://doi.org/10.1016/j.peva.2007.06.006.
Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: the lasso and generalizations. New York, NY: Chapman and Hall; 2015. https://doi.org/10.1201/b18401.
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J.M.K. and D.D. are supported by the Bundesministerium für Bildung und Forschung (BMBF COMPLS3-022).
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None of the authors has any conflict of interest to disclose.
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Kernbach, J.M., Ort, J., Hakvoort, K., Clusmann, H., Delev, D., Neuloh, G. (2022). Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience. In: Staartjes, V.E., Regli, L., Serra, C. (eds) Machine Learning in Clinical Neuroscience. Acta Neurochirurgica Supplement, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-85292-4_8
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