Brain Atrophy and Clinical Characterization of Adults With Mild Cognitive Impairment and Different Cerebrospinal Fluid Biomarker Profiles According to the AT(N) Research Framework of Alzheimer's Disease.

Introduction: This study aimed to evaluate, in adults with mild cognitive impairment (MCI), the brain atrophy that may distinguish between three AT(N) biomarker-based profiles, and to determine its clinical value. Methods: Structural MRI (sMRI) was employed to evaluate the volume and cortical thickness differences in MCI patients with different AT(N) profiles, namely, A-T-(N)-: normal AD biomarkers; A+T-(N)-: AD pathologic change; and A+T+(N)+: prodromal AD. Sensitivity and specificity of these changes were also estimated. Results: An initial atrophy in medial temporal lobe (MTL) areas was found in the A+T-(N)- and A+T+(N)+ groups, spreading toward the parietal and frontal regions in A+T+(N)+ patients. These structural changes allowed distinguishing AT(N) profiles within the AD continuum; however, the profiles and their pattern of neurodegeneration were unsuccessful to determine the current clinical status. Conclusion: sMRI is useful in the determination of the specific brain structural changes of AT(N) profiles along the AD continuum, allowing differentiation between MCI adults with or without pathological AD biomarkers.

Relevance of Complaint Severity in Predicting the Progression of Subjective Cognitive Decline and Mild Cognitive Impairment: A Machine Learning Approach.

BACKGROUND: The presence of subjective cognitive complaints (SCCs) is a core criterion for diagnosis of subjective cognitive decline (SCD); however, no standard procedure for distinguishing normative and non-normative SCCs has yet been established. OBJECTIVE: To determine whether differentiation of participants with SCD according to SCC severity improves the validity of the prediction of progression in SCD and MCI and to explore validity metrics for two extreme thresholds of the distribution in scores in a questionnaire on SCCs. METHODS: Two hundred and fifty-three older adults with SCCs participating in the Compostela Aging Study (CompAS) were classified as MCI or SCD at baseline. The participants underwent two follow-up assessments and were classified as cognitively stable or worsened. Severity of SCCs (low and high) in SCD was established by using two different percentiles of the questionnaire score distribution as cut-off points. The validity of these cut-off points for predicting progression using socio-demographic, health, and neuropsychological variables was tested by machine learning (ML) analysis. RESULTS: Severity of SCCs in SCD established considering the 5th percentile as a cut-off point proved to be the best metric for predicting progression. The variables with the main role in conforming the predictive algorithm were those related to memory, cognitive reserve, general health, and the stability of diagnosis over time. CONCLUSION: Moderate to high complainers showed an increased probability of progression in cognitive decline, suggesting the clinical relevance of standard procedures to determine SCC severity. Our findings highlight the important role of the multimodal ML approach in predicting progression.

Comparison Between an Empirically Derived and a Standard Classification of Amnestic Mild Cognitive Impairment From a Sample Of Adults With Subjective Cognitive Complaints.

OBJECTIVE: The aim of this study is to compare an empirically derived classification of amnestic mild cognitive impairment (aMCI) from a sample of adults with subjective cognitive complaints by using cluster analysis of their performance on the California Verbal Learning Test (CVLT) with a classification of aMCI based on standard clinical criteria. METHOD: Three hundred ninety-one individuals aged 48 years and older were diagnosed as aMCI or healthy controls. Cluster analysis of the CVLT performance was conducted, followed by logistic regression analysis. RESULTS: A two-cluster solution performed on the CVLT measures correctly classified 98.0% of the aMCI patients and 73.4% of the healthy controls diagnosed by using standard aMCI criteria. DISCUSSION: The empirically derived classification of aMCI is consistent with the classification based on standard criteria; however, standard criteria should also be considered to prevent false positives.

Cluster subtypes of the Spanish version of the California Verbal Learning Test in a sample of adults with subjective memory complaints.

We examined subtypes of learning and memory by administering the California Verbal Learning Test (CVLT) to a sample of adults with memory complaints that included a subsample of healthy controls and another of participants with amnesic mild cognitive impairment. We performed two-stage cluster analyses for CVLT variables representing three main factors-General Verbal Learning, Inaccurate Memory, and Serial Effect. Four, three, and two reliable subtypes were differentiated in the total sample and in the two subsamples, respectively. Gender, age, education, reading habits, vocabulary, memory complaints, and general cognitive performance were meaningfully related to variability in the performance of the subtypes.