{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kornak J"],"funding":["CDC","Bluefield Project to Cure Frontotemporal Dementia","NIA NIH HHS","Larry H. Hillblom Foundation","NHLBI NIH HHS","Mayo Clinic Center for Regenerative Medicine","National Institutes of Health","Alzheimer&apos;s Association","National Institute on Aging","Forum","Eli Lilly and Co.","Roche","DOD","NINDS NIH HHS","AstraZeneca","Jane Tanger Back","International Parkinson and Movement Disorder Society","Janssen Pharmaceuticals","Bristol-Myers Squibb","TauRx","Axovant","Corticobasal Degeneration Solutions","Association for Frontotemporal Dementia","Avid","Genentech","Nancy H. Hall Memorial","AVID Pharmaceuticals","SNIFF","Avid Radiopharmaceuticals","CDC HHS","Association for Frontotemporal Degeneration","Navidea Biopharmaceuticals","Minnesota Partnership for Biotechnology and Medical Genomics","Piramal","NIBIB NIH HHS","Eli Lilly","Allon Therapeutics","Bristol Myers Squibb","Parkinson Study Group","BrightFocus Foundation","Eisai Inc.","Parkinson Foundation","GE Healthcare","Canadian Institutes of Health Research","Merck","Allergan, Inc","Janssen Immunotherapy","Wyeth","Cortice","Alzheimer&apos;s Drug Discovery Foundation","Janssen","Lilly","Michael J Fox Foundation","C2N","Medivation","Alzheimer Society of British Columbia","Pfizer","NIA","New York State Department of Health","BMS","Penn Institute on Aging","University of Southern California","HDSA","NIH","C2N Diagnostics","AbbVie","Novartis","CIHR","Alector","Tau Research Consortium","Tau Consortium","Biogen","NCI NIH HHS","Biogen Pharmaceuticals"],"pagination":["797-808"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6911910"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11"],"pubmed_abstract":["<h4>Introduction</h4>Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these \"adjusted\" Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.<h4>Methods</h4>In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).<h4>Results</h4>Corrected Z-scores based on nonlinea"],"journal":["Alzheimer's & dementia (Amsterdam, Netherlands)"],"pubmed_title":["Nonlinear Z-score modeling for improved detection of cognitive abnormality."],"pmcid":["PMC6911910"],"funding_grant_id":["R01 CA132870","NU38 CK000480","P30 AG053760","P50 AG005146","U54 NS092089","P50 AG005142","1510130358","P30 AG066518","P30 AG062715","P50 AG047266","1U01NS086659","T35 HL007491","P30 AG019610","K23 AG061253","P50 AG008702","P30 AG010124","P30 AG012300","P50 AG047270","P30 AG010161","K23 AG059891","P30 AG049638","P01 AG066597","5P50 AG005131-31","P30 AG013846","P30 AG028383","P30 AG008017","P30 AG010133","P50 AG033514","U01AG045390","U01 AG006786","P01 AG019724","P50 AG005681","P50 AG047366","R01 AG041797","P50 AG023501","R01 EB022055","P30 AG008051","2018-A-025-FEL","P30 AG010129","P30 AG013854","P50 AG005138","1U54NS092089-01","P50 AG005134","U01 AG016976","P50 AG005136","P50 AG025688","P30 AG035982","P50 AG005131","5T35HL007491","U01 NS086659","P50 AG005133","U54NS092089","P50 AG016574","R01CA132870","U01 AG045390","P50 AG016573","R01EB022055","NU38CK000480"],"pubmed_authors":["McKinley E","Kornak J","Hsiao J","Rademakers R","Ramos EM","Grant IM","Coppola G","Brannelly P","Ghoshal N","Onyike C","Taylor J","Litvan I","Wang P","Goldman J","Karydas A","Dheel C","Wszolek Z","Domoto-Reilly K","Caso C","Petrucelli L","Boeve B","Haley D","Multani N","Fishman A","Foroud T","Ferrall J","Trojanowski J","Maldonado M","Kaufer D","McGinnis S","Grossman M","Tatton N","Toga A","Boxer A","Jones L","Knopman D","Fields J","Besser L","Kraft R","Lapid M","Lucente D","Irwin D","Rindels A","Dever R","Forsberg L","Kramer J","Mackenzie I","Rogalski-Miller E","Kukull W","Ghazanfari B","Ljubenkov P","Lungu C","Roberson ED","Dickinson S","Gavrilova R","Fong J","Jones D","Potter M","Hsiung R","Huey ED","Tartaglia C","Bordelon Y","Appleby B","Sengdy P","Wong B","Rascovsky K","ARTFL/LEFFTDS Consortium","Dodge H","Kerwin D","Weintraub S","Miller B","Shaw L","Sutherland M","Dickerson B","Manoochehri M","Kremers W","Pearlman R","Graff-Radford J","Mendez M","Graff-Radford N","Rosen H","Farmer S","Faber K","Bove J","Gearhart D","Brushaber D","Syrjanen J","Dominguez S","Staffaroni AM","Rankin K","Pantelyat A","Padmanabhan J","Heuer HW","Kantarci K"],"additional_accession":[]},"is_claimable":false,"name":"Nonlinear Z-score modeling for improved detection of cognitive abnormality.","description":"<h4>Introduction</h4>Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these \"adjusted\" Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.<h4>Methods</h4>In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).<h4>Results</h4>Corrected Z-scores based on nonlinea","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Dec","modification":"2025-05-29T21:07:03.241Z","creation":"2025-05-29T21:07:03.241Z"},"accession":"S-EPMC6911910","cross_references":{"pubmed":["31872042"],"doi":["10.1016/j.dadm.2019.08.003"]}}