Decision page intermittent crash
A decision page crash was observed in branch peer-review-dashboard
, commit f8a7cd59 . (I also observed a decision page crash in earlier commit a96c3ce2 but wasn't logging network responses, so not sure if its cause was the same.) Seems to be infrequent.
A CommsErrorBanner was shown, and this was in the console:
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"abstract": "<p class=\"paragraph\">This technical note describes the effects of different data reduction methods and sample sizes for neuroimaging studies in the context of canonical correlation analysis (CCA). CCA is a multivariate statistical technique which has gained increasing popularity in neuroimaging research in recent years. Here, we investigate the parcellation methods’ impact on elucidating neuroanatomical relationships (based on cortical thickness) with known risk factors related to Alzheimer’s disease risk using data from the UK Biobank. The cortical thickness values were parcellated using four common methods in neuroimaging (atlas-based parcellation, spectral clustering, principal component analysis, and independent component analysis) and results from CCA were compared. The results show that the choice of parcellation technique impacts the strength and significance of the correlation between the brain and behaviours. Principal component analysis and independent component analysis result in the strongest correlations. Additionally, we show that regardless of parcellation technique, smaller sample sizes of participants result in inflated correlation strength and significance. </p>",
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"submission": "{\"Year\":\"\",\"cover\":\"<p class=\\\"paragraph\\\">Please find enclosed the technical note entitled “The impact of study design choices on significance and generalizability of canonical correlation analysis in neuroimaging studies”, by Grace Pigeau and colleagues, for consideration for publication in your journal. </p><p class=\\\"paragraph\\\">In recent years, canonical correlation analysis (CCA) has become an increasingly popular tool for neuroimaging studies - particularly those investigating relationships between the human brain and behaviours. Despite this, little investigation has been done to evaluate how data processing decisions, and to a certain extent sample size considerations, impact CCA results. Furthermore, data reduction techniques are typically applied before CCA. However, many different data techniques are used and these differences are often not taken into consideration when the results are compared. </p><p class=\\\"paragraph\\\">We used a subset of data from over 20,000 participants from the UK Biobank to examine the impact of these data reduction strategies and sample size on the strength of the output correlations, the canonical variates, and the stability of the results. To this end, we examine how these choices impact the results and their interpretation when examining the relationship between neuroanatomy and modifiable risk factors for Alzheimer’s disease. The examination of this relationship is one of the intended uses of the data available from the UK Biobank, making it well suited for this experiment. </p><p class=\\\"paragraph\\\">We found that the choice of data reduction technique impacts the strength and significance of the correlation between the brain and behaviours. Additionally, we found that smaller sample sizes of participants result in inflated correlation strength and significance, regardless of the data reduction technique used. </p><p class=\\\"paragraph\\\">These findings are important as they demonstrate that although CCA is a promising multivariate approach which holds many advantages in exploring the relationship between the human brain and behavior, it cannot be used or interpreted without restriction. This is relevant to neuroimaging researchers as it could have a significant impact on the way future studies are conducted and older CCA studies are interpreted. </p><p class=\\\"paragraph\\\">We declare no conflict of interest and all authors have approved of this manuscript. Please address all correspondence to myself and Dr. Mallar Chakravarty. </p>\",\"label\":\"EIC HE Decision Needed\",\"title\":\"\",\"ethics\":\"\",\"authors\":[{\"id\":\"95cc130a-64a6-431d-94a8-70f1e6211ca6\",\"email\":\"grace.pigeau@mail.mcgill.ca\",\"lastName\":\"Pigeau\",\"firstName\":\"Grace\",\"affiliation\":\"McGill University\"},{\"id\":\"299dd1c7-1d21-4243-a063-f779a3e07569\",\"email\":\"\",\"lastName\":\"Costantino\",\"firstName\":\"Manuela\",\"affiliation\":\"\"},{\"id\":\"03971581-36fc-42d0-8e44-9bbb7750f1f3\",\"email\":\"\",\"lastName\":\"Devenyi\",\"firstName\":\"Gabriel A.\",\"affiliation\":\"\"},{\"id\":\"44233828-2a08-4a9e-af0e-4d16b56db848\",\"email\":\"\",\"lastName\":\"Bussy\",\"firstName\":\"Aurelie\",\"affiliation\":\"\"},{\"id\":\"1d284804-3dda-4d07-a843-fb6a0cbaee83\",\"email\":\"\",\"lastName\":\"Parent\",\"firstName\":\"Olivier\",\"affiliation\":\"\"},{\"id\":\"84ab9c73-8e3b-40c5-beeb-45d3af08b599\",\"email\":\"mallar.chakravarty@douglas.mcgill.ca\",\"lastName\":\"Chakravarty\",\"firstName\":\"Mallar\",\"affiliation\":\"\"}],\"content\":[\"on\"],\"abstract\":\"\",\"dataCode\":[\"on\",\"on\"],\"suggested\":\"Laura Wisse (Sweden) laura.wisse@med.lu.se, Janaina Mourao-Miranda (UCL) j.mourao-miranda@ucl.ac.uk, Andre Altmann (UCL) a.altmann@ucl.ac.uk, Ali Khan (Western) alik@robarts.ca\",\"objectType\":[],\"references\":\"<p class=\\\"paragraph\\\">Barcikowski, R. S., & Stevens, J. P. (1975). A Monte Carlo Study of the Stability of Canonical Correlations, Canonical Weights and Canonical Variate-Variable Correlations. Multivariate Behavioral Research, 10(3), 353–364. https://doi.org/10.1207/s15327906mbr1003_8 </p><p class=\\\"paragraph\\\">Bhagwat, N., Pipitone, J., Voineskos, A. N., & Chakravarty, M. M. (2019). An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures. Journal of Psychiatry & Neuroscience : JPN, 44(4), 246. https://doi.org/10.1503/JPN.180016 </p><p class=\\\"paragraph\\\">Dattalo, P. V. (2014). A Demonstration of Canonical Correlation Analysis with Orthogonal Rotation to Facilitate Interpretation. Retrieved from http://scholarscompass.vcu.edu/socialwork_pubs </p><p class=\\\"paragraph\\\">Hamming, R. W. (1950). Error Detecting and Error Correcting Codes. The Bell System Technical Journal, 196(2), 519–520. https://doi.org/10.1016/s0016-0032(23)90506-5 </p><p class=\\\"paragraph\\\">Helmer, M., Warrington, S., Lisa, J., Howell, A., & Rosand, B. (2020). Reliable estimation of Canonical Correlation Analysis ( CCA ) and Partial Least Squares ( PLS ) with application to brain-behavior associations. 2020. </p><p class=\\\"paragraph\\\">Hotelling, H. (1936). Relations Between Two Sets of Variates. Biometrika, 28(3–4), 321–377. https://doi.org/10.1093/biomet/28.3-4.321 </p><p class=\\\"paragraph\\\">Kernbach, J. M., Thomas Yeo, B. T., Smallwood, J., Margulies, D. S., De Schotten, M. T., Walter, H., … Bzdok, D. (2018). Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants. Proceedings of the National Academy of Sciences of the United States of America, 115(48), 12295–12300. https://doi.org/10.1073/pnas.1804876115 </p><p class=\\\"paragraph\\\">Khundrakpam, B. S., Tohka, J., Evans, A. C., Ball, W. S., Byars, A. W., Schapiro, M., … O’Neill, J. (2015). Prediction of brain maturity based on cortical thickness at different spatial resolutions. NeuroImage, 111, 350–359. https://doi.org/10.1016/J.NEUROIMAGE.2015.02.046 </p><p class=\\\"paragraph\\\">Lambert, Z. V., & Durand, R. M. (1975). Some Precautions in Using Canonical Analysis: Https://Doi.Org/10.1177/002224377501200411, 12(4), 468–475. https://doi.org/10.1177/002224377501200411 </p><p class=\\\"paragraph\\\">Leach, L. F., & Henson, R. K. (2014). Bias and precision of the squared canonical correlation coefficient under nonnormal data conditions. Journal of Modern Applied Statistical Methods, 13(1), 110–139. https://doi.org/10.22237/jmasm/1398917220 </p><p class=\\\"paragraph\\\">Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., … Mukadam, N. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 396(10248), 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6/ATTACHMENT/CEE43A30-904B-4A45-A4E5-AFE48804398D/MMC1.PDF </p><p class=\\\"paragraph\\\">Long, Z., Huang, J., Li, B., Li, Z., Li, Z., Chen, H., & Jing, B. (2018). A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry. Frontiers in Neuroscience, 12, 916. https://doi.org/10.3389/FNINS.2018.00916 </p><p class=\\\"paragraph\\\">Lyttelton, O., Boucher, M., Robbins, S., & Evans, A. (2007). An unbiased iterative group registration template for cortical surface analysis. NeuroImage, 34(4), 1535–1544. https://doi.org/10.1016/J.NEUROIMAGE.2006.10.041 </p><p class=\\\"paragraph\\\">Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine Learning in {P}ython. Journal of Machine Learning Research, 12, 2825–2830. </p><p class=\\\"paragraph\\\">Rolls, E. T., Huang, C. C., Lin, C. P., Feng, J., & Joliot, M. (2020). Automated anatomical labelling atlas 3. NeuroImage, 206(September 2019), 116189. https://doi.org/10.1016/j.neuroimage.2019.116189 </p><p class=\\\"paragraph\\\">Smith, S. M., Alfaro-Almagro, F., & Miller, K. L. (n.d.). UK Biobank Brain Imaging Documentation UK Biobank Brain Imaging Documentation Contributors to UK Biobank Brain Imaging. Retrieved from http://www.ukbiobank.ac.uk </p><p class=\\\"paragraph\\\">Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., … Miller, K. L. (2015, November 1). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, Vol. 18, pp. 1565–1567. https://doi.org/10.1038/nn.4125 </p><p class=\\\"paragraph\\\">Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., … Collins, R. (2015). UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Medicine, 12(3), e1001779. https://doi.org/10.1371/JOURNAL.PMED.1001779 </p><p class=\\\"paragraph\\\">Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. https://doi.org/10.1006/NIMG.2001.0978 </p><p class=\\\"paragraph\\\">Wang, H. T., Smallwood, J., Mourao-Miranda, J., Xia, C. H., Satterthwaite, T. D., Bassett, D. S., & Bzdok, D. (2020). Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists. NeuroImage, 216, 116745. https://doi.org/10.1016/J.NEUROIMAGE.2020.116745 </p><p class=\\\"paragraph\\\">Yang, Q., Zhang, X., Song, Y., Liu, F., Qin, W., Yu, C., & Liang, M. (2021). Stability test of canonical correlation analysis for studying brain-behavior relationships: The effects of subject-to-variable ratios and correlation strengths. Human Brain Mapping, 42(8), 2374–2392. https://doi.org/10.1002/HBM.25373 </p><p class=\\\"paragraph\\\">Zhuang, X., Yang, Z., & Cordes, D. (2020). A technical review of canonical correlation analysis for neuroscience applications. Human Brain Mapping, 41(13), 3807–3833. https://doi.org/10.1002/HBM.25090 </p><p class=\\\"paragraph\\\">Zijdenbos, A. P., Forghani, R., & Evans, A. C. (2002). Automatic “pipeline” analysis of 3-D MRI data for clinical trials: Application to multiple sclerosis. IEEE Transactions on Medical Imaging, 21(10), 1280–1291. https://doi.org/10.1109/TMI.2002.806283 </p>\",\"DateAccepted\":\"\",\"DateReceived\":\"2023.01.19\",\"otherMethods\":\"\",\"volumeNumber\":\"\",\"codeAvailability\":[],\"dataCodeContinued\":\"<p class=\\\"paragraph\\\"><em>No new research data were acquired or generated for this Research Object.</em></p>\",\"conflictOfInterest\":\"Agree. The author(s) declare that they have no known competing financial interests or personal relationships that may have influenced their research.\",\"researchObjectLinks\":\"<p class=\\\"paragraph\\\">https://github.com/CoBrALab/design-choices-cca-neuroimaging</p>\",\"AuthorCorrespondence\":\"\",\"similarityReportScore\":\"0\"}",
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