18
IRUS TotalDownloads
Altmetric
A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease
File | Description | Size | Format | |
---|---|---|---|---|
s43856-022-00133-4.pdf | Published version | 2.38 MB | Adobe PDF | View/Open |
Title: | A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease |
Authors: | Inglese, M Patel, N Linton-Reid, K Loreto, F Win, Z Perry, R Carswell, C Grech-Sollars, M Crum, WR Lu, H Malhotra, PA Aboagye, E |
Item Type: | Journal Article |
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. |
Issue Date: | 20-Jun-2022 |
Date of Acceptance: | 24-May-2022 |
URI: | http://hdl.handle.net/10044/1/97726 |
DOI: | 10.1038/s43856-022-00133-4 |
ISSN: | 2730-664X |
Journal / Book Title: | Communications Medicine |
Volume: | 2 |
Copyright Statement: | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Sponsor/Funder: | Medical Research Council |
Publication Status: | Published |
Article Number: | ARTN 70 |
Appears in Collections: | Department of Surgery and Cancer Department of Brain Sciences |
This item is licensed under a Creative Commons License