A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease
Marianna Inglese, Neva Patel, Kristofer Linton‐Reid, Flavia Loreto, Zarni Win, Richard Perry, Christopher Carswell, Matthew Grech‐Sollars, William R. Crum, Haonan Lu, Paresh Malhotra, the Alzheimer’s Disease Neuroimaging Initiative, Lisa C. Silbert, Betty Lind, Rachel Crissey, Jeffrey Kaye, Raina Carter, Sara Dolen, Joseph F. Quinn, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Karen Dagerman, Bryan M. Spann, James B. Brewer, Helen Vanderswag, Adam Fleisher, Jaimie Ziolkowski, Judith L. Heidebrink, Zbizek-Nulph, Joanne Lord, Lisa Zbizek-Nulph, Ronald Petersen, Sara S. Mason, Colleen S. Albers, David S. Knopman, Kris Johnson, Javier Villanueva-Meyer, Valory Pavlik, Nathaniel Pacini, Ashley Lamb, Joseph S. Kass, Rachelle S. Doody, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Akiva Mintz, Beau M. Ances, John C. Morris, David Winkfield, Maria Carroll, Georgia Stobbs-Cucchi, Angela Oliver, Mary L. Creech, Mark A. Mintun, Stacy Schneider, David Geldmacher, Marissa Natelson Love, Randall Griffith, David G. Clark, John Brockington, Daniel Marson, Hillel Grossman, Martin Goldstein, Jonathan Greenberg, Effie Mitsis, Raj C. Shah, Melissa Lamar, Ajay Sood, Kimberly S. Blanchard, Debra Fleischman, Konstantinos Arfanakis, Patricia Samuels, Ranjan Duara, Maria T. Greig‐Custo, Rosemarie Rodriguez, Marilyn Albert, Daniel Varón, Chiadi U. Onyike, Leonie Farrington, Scott Rudow, Rottislav Brichko, Maria T. Greig, Stephanie Kielb, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Martin Sadowski, Thomas Wısnıewskı, Melanie Shulman, Arline Faustin, Julia Rao, Karen M. Castro, Anaztasia Ulysse, Shannon Chen, Mohammed O. Sheikh
Abstract
Abstract Background Alzheimer’s disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called “Alzheimer’s Predictive Vector” (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer’s related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.