BACKGROUND: The utility of recognition memory for identifying persons with biomarker evidence of Alzheimer's disease (AD) is unclear since prior studies of mild cognitive impairment (MCI) relied only on clinical diagnosis and did not include simultaneous measures of central amyloidosis and tauopathy. OBJECTIVE: We evaluated whether recognition memory and associated indices, including discriminability and response bias from signal detection theory, differentiate persons with amnestic MCI (aMCI) due to prodromal AD from non-prodromal AD. METHOD: Sixty older adults with aMCI were classified as prodromal AD (n = 28) or non-prodromal AD (n = 32) based upon cerebrospinal fluid levels of amyloid-β and tau. Memory was assessed using the Hopkins Verbal Learning Test-Revised which includes free recall and recognition. RESULTS: ANCOVAs adjusting for age indicated comparable (all p > 0.05) performances between prodromal and non-prodromal MCI groups respectively on traditional HVLT-R recognition measures of hits (mean±SD: 9.5±3.0 versus 10.9±1.7), false alarms (1.8±1.8 versus 1.5±1.5), and hits minus false alarms (7.7±3.0 versus 9.2±2.6). In contrast, discriminability (d'), which reflects how easily targets and distractors are distinguished, was significantly (p = 0.009) poorer in the prodromal versus non-prodromal groups (3.1±1.9 versus 4.8±2.0, effect size = 0.87). In addition, only d' significantly predicted group membership (OR = 0.66, CI = 0.48-0.92, p = 0.04). Response bias, the tendency to report that a target did or did not appear, was comparable between groups (0.08±1.1 versus -0.04±1.3). CONCLUSION: Recognition discriminability is significantly poorer in aMCI with biomarker evidence of prodromal AD. In contrast to traditional recognition indices, discriminability from signal detection theory may be superior in identifying aMCI due to AD versus non-AD etiologies.
BACKGROUND: The utility of recognition memory for identifying persons with biomarker evidence of Alzheimer's disease (AD) is unclear since prior studies of mild cognitive impairment (MCI) relied only on clinical diagnosis and did not include simultaneous measures of central amyloidosis and tauopathy. OBJECTIVE: We evaluated whether recognition memory and associated indices, including discriminability and response bias from signal detection theory, differentiate persons with amnestic MCI (aMCI) due to prodromal AD from non-prodromal AD. METHOD: Sixty older adults with aMCI were classified as prodromal AD (n = 28) or non-prodromal AD (n = 32) based upon cerebrospinal fluid levels of amyloid-β and tau. Memory was assessed using the Hopkins Verbal Learning Test-Revised which includes free recall and recognition. RESULTS: ANCOVAs adjusting for age indicated comparable (all p > 0.05) performances between prodromal and non-prodromal MCI groups respectively on traditional HVLT-R recognition measures of hits (mean±SD: 9.5±3.0 versus 10.9±1.7), false alarms (1.8±1.8 versus 1.5±1.5), and hits minus false alarms (7.7±3.0 versus 9.2±2.6). In contrast, discriminability (d'), which reflects how easily targets and distractors are distinguished, was significantly (p = 0.009) poorer in the prodromal versus non-prodromal groups (3.1±1.9 versus 4.8±2.0, effect size = 0.87). In addition, only d' significantly predicted group membership (OR = 0.66, CI = 0.48-0.92, p = 0.04). Response bias, the tendency to report that a target did or did not appear, was comparable between groups (0.08±1.1 versus -0.04±1.3). CONCLUSION: Recognition discriminability is significantly poorer in aMCI with biomarker evidence of prodromal AD. In contrast to traditional recognition indices, discriminability from signal detection theory may be superior in identifying aMCI due to AD versus non-AD etiologies.
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Keywords:
Alzheimer’s disease; amyloid-βzzm321990; amyloidosis; biomarkers; mild cognitive impairment; prodromal Alzheimer’s disease; recognition memory; signal detection; tau
Authors: Ana Martín-Sánchez; Janet Piñero; Lara Nonell; Magdalena Arnal; Elena M Ribe; Alejo Nevado-Holgado; Simon Lovestone; Ferran Sanz; Laura I Furlong; Olga Valverde Journal: Alzheimers Res Ther Date: 2021-04-02 Impact factor: 6.982
Authors: Ivanna M Pavisic; Yoni Pertzov; Jennifer M Nicholas; Antoinette O'Connor; Kirsty Lu; Keir X X Yong; Masud Husain; Nick C Fox; Sebastian J Crutch Journal: Sci Rep Date: 2021-04-22 Impact factor: 4.379
Authors: Ivanna M Pavisic; Jennifer M Nicholas; Yoni Pertzov; Antoinette O'Connor; Yuying Liang; Jessica D Collins; Kirsty Lu; Philip S J Weston; Natalie S Ryan; Masud Husain; Nick C Fox; Sebastian J Crutch Journal: Neuropsychologia Date: 2021-09-22 Impact factor: 3.139