A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease

Authors

Marianna Inglese, Imperial College London
Neva Patel, Imperial College Healthcare NHS Trust
Kristofer Linton-Reid, Imperial College London
Flavia Loreto, Imperial College London
Zarni Win, Imperial College Healthcare NHS Trust
Richard J. Perry, Imperial College London
Christopher Carswell, Imperial College Healthcare NHS Trust
Matthew Grech-Sollars, Imperial College London
William R. Crum, Imperial College London
Haonan Lu, Imperial College London
Paresh A. Malhotra, Imperial College London
Lisa C. Silbert, Oregon Health & Science University
Betty Lind, Oregon Health & Science University
Rachel Crissey, Oregon Health & Science University
Jeffrey A. Kaye, Oregon Health & Science University
Raina Carter, Oregon Health & Science University
Sara Dolen, Oregon Health & Science University
Joseph Quinn, Oregon Health & Science University
Lon S. Schneider, University of Southern California
Sonia Pawluczyk, University of Southern California
Mauricio Becerra, University of Southern California
Liberty Teodoro, University of Southern California
Karen Dagerman, University of Southern California
Bryan M. Spann, University of Southern California
James Brewer, University of California, San Diego
Helen Vanderswag, University of California, San Diego
Adam Fleisher, University of California, San Diego
Jaimie Ziolkowski, University of Michigan, Ann Arbor
Judith L. Heidebrink, University of Michigan, Ann Arbor
Zbizek-Nulph, University of Michigan, Ann Arbor
Joanne L. Lord, University of Michigan, Ann Arbor
Lisa Zbizek-Nulph, University of Michigan, Ann Arbor
Ronald Petersen, Mayo Clinic
Sara S. Mason, Mayo Clinic

Document Type

Article

Publication Date

12-1-2022

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.

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