Differentiation of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using qEEG/ERP-Based Cognitive Testing and Volumetric MRI in an Outpatient Specialty Memory Clinic

Author:

Ganapathi Aarthi S.1,Glatt Ryan M.12,Bookheimer Tess H.1,Popa Emily S.1,Ingemanson Morgan L.3,Richards Casey J.1,Hodes John F.1,Pierce Kyron P.1,Slyapich Colby B.1,Iqbal Fatima1,Mattinson Jenna1,Lampa Melanie G.1,Gill Jaya M.14,Tongson Ynez M.2,Wong Claudia L.12,Kim Mihae12,Porter Verna R.12,Kesari Santosh124,Meysami Somayeh14,Miller Karen J.15,Bramen Jennifer E.14,Merrill David A.1245,Siddarth Prabha15

Affiliation:

1. Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA

2. Providence Saint John’s Health Center, Santa Monica, CA, USA

3. Evoke Neuroscience, New York, NY, USA

4. Providence Saint John’s Cancer Institute, Santa Monica, CA, USA

5. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA

Abstract

Background: Distinguishing between subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia in a scalable, accessible way is important to promote earlier detection and intervention. Objective: We investigated diagnostic categorization using an FDA-cleared quantitative electroencephalographic/event-related potential (qEEG/ERP)-based cognitive testing system (eVox® by Evoke Neuroscience) combined with an automated volumetric magnetic resonance imaging (vMRI) tool (Neuroreader® by Brainreader). Methods: Patients who self-presented with memory complaints were assigned to a diagnostic category by dementia specialists based on clinical history, neurologic exam, neuropsychological testing, and laboratory results. In addition, qEEG/ERP (n = 161) and quantitative vMRI (n = 111) data were obtained. A multinomial logistic regression model was used to determine significant predictors of cognitive diagnostic category (SCD, MCI, or dementia) using all available qEEG/ERP features and MRI volumes as the independent variables and controlling for demographic variables. Area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the prediction models. Results: The qEEG/ERP measures of Reaction Time, Commission Errors, and P300b Amplitude were significant predictors (AUC = 0.79) of cognitive category. Diagnostic accuracy increased when volumetric MRI measures, specifically left temporal lobe volume, were added to the model (AUC = 0.87). Conclusion: This study demonstrates the potential of a primarily physiological diagnostic model for differentiating SCD, MCI, and dementia using qEEG/ERP-based cognitive testing, especially when combined with volumetric brain MRI. The accessibility of qEEG/ERP and vMRI means that these tools can be used as adjuncts to clinical assessments to help increase the diagnostic certainty of SCD, MCI, and dementia.

Publisher

IOS Press

Subject

Psychiatry and Mental health,Geriatrics and Gerontology,Clinical Psychology,General Medicine,General Neuroscience

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