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@article{Inglese:2022:10.1038/s43856-022-00133-4,
author = {Inglese, M and Patel, N and Linton-Reid, K and Loreto, F and Win, Z and Perry, R and Carswell, C and Grech-Sollars, M and Crum, WR and Lu, H and Malhotra, PA and Aboagye, E},
doi = {10.1038/s43856-022-00133-4},
journal = {Communications Medicine},
title = {A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease},
url = {http://dx.doi.org/10.1038/s43856-022-00133-4},
volume = {2},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Inglese,M
AU  - Patel,N
AU  - Linton-Reid,K
AU  - Loreto,F
AU  - Win,Z
AU  - Perry,R
AU  - Carswell,C
AU  - Grech-Sollars,M
AU  - Crum,WR
AU  - Lu,H
AU  - Malhotra,PA
AU  - Aboagye,E
DO  - 10.1038/s43856-022-00133-4
PY  - 2022///
SN  - 2730-664X
TI  - A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease
T2  - Communications Medicine
UR  - http://dx.doi.org/10.1038/s43856-022-00133-4
UR  - http://hdl.handle.net/10044/1/97726
VL  - 2
ER  -

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