The Use of Event Related Potentials to Predict Amyloid PET Status Among Patients from a Memory Disorders Clinic.

Alzheimer’s disease amyloid PET biomarkers event-related potentials

Journal

Journal of Alzheimer's disease : JAD
ISSN: 1875-8908
Titre abrégé: J Alzheimers Dis
Pays: Netherlands
ID NLM: 9814863

Informations de publication

Date de publication:
06 Jul 2024
Historique:
medline: 12 7 2024
pubmed: 12 7 2024
entrez: 12 7 2024
Statut: aheadofprint

Résumé

Amyloid positron emission tomography (PET) scans provide in vivo evidence of Alzheimer's disease (AD); however, their high cost limits their use in standard clinical care. Event related potentials (ERPs) may represent an inexpensive and non-invasive additional method for detecting AD pathology. We investigated whether ERPs, along with neuropsychological data, serve as predictors of amyloid PET status in patients with memory complaints. Veterans aged 50-100 were recruited from a memory disorders clinic. Participants underwent a neuropsychological battery and an ERP auditory oddball protocol. Twenty-eight patients had a positive amyloid PET scan, and thirty-nine patients had a negative scan. ERP-P200 target amplitude and P200 standard latency were predictors of amyloid PET status. When submitting to ROC analysis, P200 standard latency exhibited the highest specificity and sensitivity in predicting amyloid PET positivity, correctly classifying the amyloid PET status for 86% of patients. ERP-P200 measures are strong indicators of amyloid-β presence in patients from a memory disorder clinic. Increased P200 amplitude and decreased P200 latency in patients with a positive amyloid PET scan may be attributed to hyperactivation of perceptual bottom-up processes compensating for AD-related synaptic loss in the fronto-parietal networks.

Sections du résumé

Background UNASSIGNED
Amyloid positron emission tomography (PET) scans provide in vivo evidence of Alzheimer's disease (AD); however, their high cost limits their use in standard clinical care. Event related potentials (ERPs) may represent an inexpensive and non-invasive additional method for detecting AD pathology.
Objective UNASSIGNED
We investigated whether ERPs, along with neuropsychological data, serve as predictors of amyloid PET status in patients with memory complaints.
Methods UNASSIGNED
Veterans aged 50-100 were recruited from a memory disorders clinic. Participants underwent a neuropsychological battery and an ERP auditory oddball protocol. Twenty-eight patients had a positive amyloid PET scan, and thirty-nine patients had a negative scan.
Results UNASSIGNED
ERP-P200 target amplitude and P200 standard latency were predictors of amyloid PET status. When submitting to ROC analysis, P200 standard latency exhibited the highest specificity and sensitivity in predicting amyloid PET positivity, correctly classifying the amyloid PET status for 86% of patients.
Conclusions UNASSIGNED
ERP-P200 measures are strong indicators of amyloid-β presence in patients from a memory disorder clinic. Increased P200 amplitude and decreased P200 latency in patients with a positive amyloid PET scan may be attributed to hyperactivation of perceptual bottom-up processes compensating for AD-related synaptic loss in the fronto-parietal networks.

Identifiants

pubmed: 38995774
pii: JAD231038
doi: 10.3233/JAD-231038
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Anna Marin (A)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.
Department of Behavioral Neuroscience, Boston University Aram V. Chobanian & Edward Avedisian School of Medicine, Boston, MA, USA.

Katherine W Turk (KW)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.
Alzheimer's Disease Research Center, Boston University, Boston, MA, USA.

Kylie Schiloski (K)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.

Ana Vives-Rodriguez (A)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.

Cheongmin Suh (C)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.

Prayerna Uppal (P)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.

Brigid Dwyer (B)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.
Alzheimer's Disease Research Center, Boston University, Boston, MA, USA.

Rocco Palumbo (R)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.

Andrew E Budson (AE)

Center for Translational Cognitive Neuroscience, VA Boston Healthcare System, Boston, MA, USA.
Alzheimer's Disease Research Center, Boston University, Boston, MA, USA.

Classifications MeSH