{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:34:32Z","timestamp":1770456872621,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006312","name":"BrightFocus Foundation","doi-asserted-by":"publisher","award":["A2020118F (PI: Miller)"],"award-info":[{"award-number":["A2020118F (PI: Miller)"]}],"id":[{"id":"10.13039\/100006312","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG046171, RF1AG051550 and 3U01AG024904-09S4"],"award-info":[{"award-number":["R01AG046171, RF1AG051550 and 3U01AG024904-09S4"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer\u2019s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected \u03b1 (p-value \u2264 1.40754 \u00d7 10\u221213). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.<\/jats:p>","DOI":"10.3390\/genes12111661","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:22:02Z","timestamp":1634862122000},"page":"1661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Pairwise Correlation Analysis of the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation"],"prefix":"10.3390","volume":"12","author":[{"given":"Erik D.","family":"Huckvale","sequence":"first","affiliation":[{"name":"Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew W.","family":"Hodgman","sequence":"additional","affiliation":[{"name":"Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brianna B.","family":"Greenwood","sequence":"additional","affiliation":[{"name":"Department of Biology, Brigham Young University, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Devorah O.","family":"Stucki","sequence":"additional","affiliation":[{"name":"Department of Biology, Brigham Young University, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katrisa M.","family":"Ward","sequence":"additional","affiliation":[{"name":"Department of Biology, Brigham Young University, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark T. 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K.","family":"Kauwe","sequence":"additional","affiliation":[{"name":"Department of Biology, Brigham Young University, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The Alzheimer\u2019s Disease Metabolomics Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5309-1570","authenticated-orcid":false,"given":"Justin B.","family":"Miller","sequence":"additional","affiliation":[{"name":"Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ijmedinf.2017.07.002","article-title":"Advancing Alzheimer\u2019s Research: A Review of Big Data Promises","volume":"106","author":"Zhang","year":"2017","journal-title":"Int. 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