Wolfgang Fruehwirt1, Georg Dorffner2, Stephen Roberts3, Matthias Gerstgrasser4, Dieter Grossegger5, Reinhold Schmidt6, Peter Dal-Bianco7, Gerhard Ransmayr8, Heinrich Garn9, Markus Waser9, Thomas Benke10. 1. Medical University of Vienna, Institute of Artificial Intelligence and Decision Support, Vienna, Austria; University of Oxford, Department of Engineering Science, Oxford, UK. Electronic address: wolfgang.fruehwirt@meduniwien.ac.at. 2. Medical University of Vienna, Institute of Artificial Intelligence and Decision Support, Vienna, Austria. 3. University of Oxford, Department of Engineering Science, Oxford, UK. 4. University of Oxford, Department of Computer Science, Oxford, UK. 5. Dr. Grossegger & Drbal GmbH, Vienna, Austria. 6. Medical University of Graz, Department of Neurology, Graz, Austria. 7. Medical University of Vienna, Department of Neurology, Vienna, Austria. 8. Kepler University Hospital, Department of Neurology 2, Linz, Austria. 9. AIT Austrian Institute of Technology GmbH, Vienna, Austria. 10. Medical University of Innsbruck, Department of Neurology, Innsbruck, Austria.
Abstract
BACKGROUND: So far, no cost-efficient, widely-used biomarkers have been established to facilitate the objectivization of Alzheimer's disease (AD) diagnosis and monitoring. Research suggests that event-related potentials (ERPs) reflect neurodegenerative processes in AD and might qualify as neurophysiological AD markers. OBJECTIVES: First, to examine which ERP component correlates the most with AD severity, as measured by the Mini-Mental State Examination (MMSE). Then, to analyze the temporal change of this component as AD progresses. METHODS: Sixty-three subjects (31 with possible, 32 with probable AD diagnosis) were recruited as part of the cohort study Prospective Dementia Registry Austria (PRODEM). For a maximum of 18 months patients revisited every 6 months for follow-up assessments. ERPs were elicited using an auditory oddball paradigm. P300 and N200 latency was determined with regard to target as well as difference wave ERPs, whereas P50 amplitude was measured from standard stimuli waveforms. RESULTS: P300 latency exhibited the strongest association with AD severity (e.g., r = -0.512, p < 0.01 at Pz for target stimuli in probable AD subjects). Further, there were significant Pearson correlations for N200 latency (e.g., r = -0.407, p = 0.026 at Cz for difference waves in probable AD subjects). P50 amplitude, as measured by different detection methods and at various scalp sites, did not significantly correlate with disease severity - neither in probable AD, possible AD, nor in both subgroups of patients combined. ERP markers for the group of possible AD patients did not show any significant correlations with MMSE scores. Post-hoc pairwise comparisons between baseline and 18-months follow-up assessment revealed significant P300 latency differences (e.g., p < 0.001 at Cz for difference waves in probable AD subjects). However, there were no significant correlations between the change rates of P300 latency and MMSE score. CONCLUSIONS: P300 and N200 latency significantly correlated with disease severity in probable AD, whereas P50 amplitude did not. P300 latency, which showed the highest correlation coefficients with MMSE, significantly increased over the course of the 18 months study period in probable AD patients. The magnitude of the observed prolongation is in line with other longitudinal AD studies and substantially higher than in normal ageing, as reported in previous trials (no healthy controls were included in our study).
BACKGROUND: So far, no cost-efficient, widely-used biomarkers have been established to facilitate the objectivization of Alzheimer's disease (AD) diagnosis and monitoring. Research suggests that event-related potentials (ERPs) reflect neurodegenerative processes in AD and might qualify as neurophysiological AD markers. OBJECTIVES: First, to examine which ERP component correlates the most with AD severity, as measured by the Mini-Mental State Examination (MMSE). Then, to analyze the temporal change of this component as AD progresses. METHODS: Sixty-three subjects (31 with possible, 32 with probable AD diagnosis) were recruited as part of the cohort study Prospective Dementia Registry Austria (PRODEM). For a maximum of 18 months patients revisited every 6 months for follow-up assessments. ERPs were elicited using an auditory oddball paradigm. P300 and N200 latency was determined with regard to target as well as difference wave ERPs, whereas P50 amplitude was measured from standard stimuli waveforms. RESULTS: P300 latency exhibited the strongest association with AD severity (e.g., r = -0.512, p < 0.01 at Pz for target stimuli in probable AD subjects). Further, there were significant Pearson correlations for N200 latency (e.g., r = -0.407, p = 0.026 at Cz for difference waves in probable AD subjects). P50 amplitude, as measured by different detection methods and at various scalp sites, did not significantly correlate with disease severity - neither in probable AD, possible AD, nor in both subgroups of patients combined. ERP markers for the group of possible ADpatients did not show any significant correlations with MMSE scores. Post-hoc pairwise comparisons between baseline and 18-months follow-up assessment revealed significant P300 latency differences (e.g., p < 0.001 at Cz for difference waves in probable AD subjects). However, there were no significant correlations between the change rates of P300 latency and MMSE score. CONCLUSIONS: P300 and N200 latency significantly correlated with disease severity in probable AD, whereas P50 amplitude did not. P300 latency, which showed the highest correlation coefficients with MMSE, significantly increased over the course of the 18 months study period in probable ADpatients. The magnitude of the observed prolongation is in line with other longitudinal AD studies and substantially higher than in normal ageing, as reported in previous trials (no healthy controls were included in our study).
Authors: Shimaa A Heikal; Mohamed Salama; Yuliya Richard; Ahmed A Moustafa; Brian Lawlor Journal: Front Aging Neurosci Date: 2022-02-07 Impact factor: 5.750
Authors: Florentine M Barbey; Francesca R Farina; Alison R Buick; Lena Danyeli; John F Dyer; Md Nurul Islam; Marina Krylova; Brian Murphy; Hugh Nolan; Laura M Rueda-Delgado; Martin Walter; Robert Whelan Journal: Front Digit Health Date: 2022-07-29