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BackgroundSemantic intrusion errors (SIEs) are associated with mild cognitive impairment (MCI) due to Alzheimer's disease (AD). It is unknown whether accounting for maximum learning capacity still leads to an increase in SIEs when elevated plasma p-tau₂₁₇, a biological indicator of underlying AD, is present. MethodsOne hundred fifty-eight older adult participants completed the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), a sensitive cognitive challenge test designed to elicit SIEs. Of these, 108 were clinically diagnosed with amnestic MCI (aMCI). Fifty-eight individuals met or exceeded a plasma p-tau₂₁₇positivity of >0.55 pg/ml, while 50 individuals scored below this threshold. ResultsAfter adjusting for demographic covariates and maximum learning capacity, the aMCI p-tau₂₁₇+ group evidenced more SIEs compared to aMCI p-tau₂₁₇- on the first (list B1;p= 0.035) and second trials of the competing list (list B2;p= 0.006). Biological predictors such asApoEε4 status, higher p-tau₂₁₇, and older age were predictors of an elevated number of SIEs [list B2:F(3,104) = 10.92;p= 0.001;R= 0.489)]. ConclusionsUnlike previous studies that used amyloid PET or other plasma biomarkers, individuals with aMCI p-tau₂₁₇+ evidenced more SIEs, even after adjusting for their initial learning capacity, a covariate that has not been studied previously. These findings support that SIEs are more prevalent in the presence of underlying AD pathology and occur independent of learning deficits. 

Neuroimaging and biofluid biomarkers provide a proxy of pathological changes for Alzheimer’s disease (AD) and are useful in improving diagnosis and assessing disease progression. However, it is not clear how race/ethnicity and different prevalence of AD risks impact biomarker levels. In this narrative review, we survey studies focusing on comparing biomarker differences between non-Hispanic White American(s) (NHW), African American(s) (AA), Hispanic/Latino American(s) (HLA), and Asian American(s) with normal cognition, mild cognitive impairment, and dementia. We found no strong evidence of racial and ethnic differences in imaging biomarkers after controlling for cognitive status and cardiovascular risks. For biofluid biomarkers, in AA, higher levels of plasma Aβ42/Aβ40, and lower levels of CSF total tau and p-tau 181, were observed after controlling for APOE status and comorbidities compared to NHW. Examining the impact of AD risks and comorbidities on biomarkers and their contributions to racial/ethnic differences in cognitive impairment are critical to interpreting biomarkers, understanding their generalizability, and eliminating racial/ethnic health disparities. 

IntroductionThis study investigated the role of proactive semantic interference (frPSI) in predicting the progression of amnestic Mild Cognitive Impairment (aMCI) to dementia, taking into account various cognitive and biological factors. MethodsThe research involved 89 older adults with aMCI who underwent baseline assessments, including amyloid PET and MRI scans, and were followed longitudinally over a period ranging from 12 to 55 months (average 26.05 months). ResultsThe findings revealed that more than 30% of the participants diagnosed with aMCI progressed to dementia during the observation period. Using Cox Proportional Hazards modeling and adjusting for demographic factors, global cognitive function, hippocampal volume, and amyloid positivity, two distinct aspects of frPSI were identified as significant predictors of a faster decline to dementia. These aspects were fewer correct responses on a frPSI trial and a higher number of semantic intrusion errors on the same trial, with 29.5% and 31.6 % increases in the likelihood of more rapid progression to dementia, respectively. DiscussionThese findings after adjustment for demographic and biological markers of Alzheimer’s Disease, suggest that assessing frPSI may offer valuable insights into the risk of dementia progression in individuals with aMCI. 

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to (1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and (2) generate a map of the brain regions implicated in longitudinal decline to dementia. Methods: Participants meeting criteria for aMCI at baseline ( N = 55) were classified at follow-up as remaining stable/improved in their diagnosis ( N = 41) or declined to dementia ( N = 14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups. Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p < 0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system. Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer’s disease.