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

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish (US)
Article number70
JournalCommunications Medicine
Volume2
Issue number1
DOIs
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Internal Medicine
  • Epidemiology
  • Medicine (miscellaneous)
  • Assessment and Diagnosis

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