To explore the relationship between age, education, sex, and ApoE4 (+) status to brain volume among a cohort with amnestic mild cognitive impairment (aMCI).

One hundred and twenty-three participants were stratified into Hispanic (n = 75) and White non-Hispanic (WNH, N = 48). Multiple linear regression analyses were conducted with age, education, sex, and ApoE4 status as predictor variables and left and right combined MRI volumes of the hippocampus, parahippocampus, and entorhinal cortex as dependent variables. Variations in head sizes were corrected by normalization with a total intracranial volume measurement.

Bonferroni-corrected results indicated that when controlling for ApoE4 status, education, and age, sex was a significant predictor of hippocampal volume among the Hispanic group (β = .000464, R2 = .196, p < .01) and the WNH group (β = .000455, R2 = .195, p < .05). Education (β = .000028, R2 = .168, p < .01) and sex (β = .000261, R2 = .168, p < .01) were significant predictors of parahippocampal volume among the Hispanic MCI group when controlling for the effects of ApoE4 status and age. One-way ANCOVAs comparing hippocampal and parahippocampal volume between males and females within groups revealed that females had significantly larger hippocampal volumes (p < .05). Hispanic females had significantly larger hippocampal (p < .001) and parahippocampal (p < .05) volume compared to males. No sex differences in parahippocampal volume were noted among WNHs.

Biological sex, rather than ApoE4 status, was a greater predictor of hippocampal volume among Hispanic and WNH females. These findings add to the mixed literature on sex differences in dementia research and highlight continued emphasis on ethnic populations to elucidate on neurodegenerative disparities.

To examine the direct and indirect effects of age, APOE ϵ4 genotype, amyloid positivity, and volumetric reductions in AD-prone brain regions as it relates to semantic intrusion errors reflecting proactive semantic interference (PSI) and the failure to recover from proactive semantic interference (frPSI) on the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L), a cognitive stress test that has been consistently more predictive of preclinical and prodromal Alzheimer’s disease (AD) than traditional list-learning tests.

Cross-sectional study.

1Florida Alzheimer’s Disease Research Center baseline study.

Two-hundred and twelve participants with Mini-Mental State Examination (MMSE) score above 16 and a broad array of cognitive diagnoses ranging from cognitively normal (CN) to dementia, of whom 58% were female, mean age of 72.1 (SD 7.9).

Participants underwent extensive clinical and neuropsychological evaluations, MR and amyloid Positron Emission Tomography/Computer/Computer Tomography (PET/CT) imaging, and analyses of APOE ϵ4 genotype. Confirmatory path analyses were conducted in the structural equation modeling framework that estimated multiple equations simultaneously while controlling for important covariates such as sex, education, language of evaluation, and global cognitive impairment.

Both amyloid positivity and decreased brain volumes in AD-prone regions were directly related to LASSI-L Cued B1 and Cued B2 intrusions (sensitive to PSI and frPSI effects) even after controlling for covariates. APOE ϵ4 status did not evidence direct effects on these LASSI-L cognitive markers, but rather exerted their effects on amyloid positivity, which in turn related to PSI and frPSI. Similarly, age did not have a direct relationship with LASSI-L scores, but exerted its effects indirectly through amyloid positivity and volumes of AD-prone brain regions.

Our study provides insight into the relationships among age, APOE ϵ4, amyloid, and brain volumetric reductions as it relates to semantic intrusion errors. The investigation expands our understanding of the underpinnings of PSI and frPSI intrusions in a large cohort.

To assess the influence of mild behavioral impairment (MBI) on the cognitive performance of older adults who are cognitively healthy or have mild cognitive impairment (MCI).

Secondary data analysis of a sample (n = 497) of older adults from the Florida Alzheimer’s Disease Research Center who were either cognitively healthy (n = 285) or diagnosed with MCI (n = 212). Over half of the sample (n = 255) met the operationalized diagnostic criteria for MBI. Cognitive domains of executive function, attention, short-term memory, and episodic memory were assessed using a battery of neuropsychological tests.

Older adults with MBI performed worse on tasks of executive function, attention, and episodic memory compared to those without MBI. A significant interaction revealed that persons with MBI and MCI performed worse on tasks of episodic memory compared to individuals with only MCI, but no significant differences were found in performance in cognitively healthy older adults with or without MBI on this cognitive domain. As expected, cognitively healthy older adults performed better than individuals with MCI on every domain of cognition.

The present study found evidence that independent of cognitive status, individuals with MBI performed worse on tests of executive function, attention, and episodic memory than individuals without MBI. Additionally, those with MCI and MBI perform significantly worse on episodic memory tasks than individuals with only MCI. These results provide support for a unique cognitive phenotype associated with MBI and highlight the necessity for assessing both cognitive and behavioral symptoms.

In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer’s disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.

We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.

Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705–0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).

These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

Evidence suggests that semantic interference may be a sensitive indicator of early dementia. We examined the utility of the Semantic Interference Test (SIT), a cognitive stress memory paradigm which taps proactive and retroactive semantic interference, for predicting progression from mild cognitive impairment (MCI) to dementia in both a clinical and a population-based sample.

Participants with MCI in the clinical (n = 184) and population-based (n = 435) samples were followed for up to four years. We employed receiver operating characteristic (ROC) methods to establish optimal thresholds for four different SIT indices. Threshold performance was compared in the two samples using logistic and Cox proportional hazard regression models.

Within four years, 42 (22.8%) MCI individuals in the clinical sample and 45 (10.3%) individuals in the population-based sample progressed to dementia. Overall classification accuracy of SIT thresholds ranged from 61.4% to 84.8%. Different subtests of the SIT had slightly different performance characteristics in the two samples. However, regression models showed that thresholds established in the clinical sample performed similarly in the population sample before and after adjusting for demographics and other baseline neuropsychological test scores.

Despite differences in demographic composition and progression rates, baseline SIT scores predicted progression from MCI to dementia similarly in both samples. Thresholds that best predicted progression were slightly below thresholds established for distinguishing between amnestic MCI and cognitively normal subjects in clinical practice. This confirms the utility of the SIT in both clinical and population-based samples and establishes thresholds most predictive of progression of individuals with MCI.