Mild behavioral impairment (MBI) is a validated diagnostic entity, that describes the emergence of later life neuropsychiatric symptoms (NPS) in pre-dementia states. The aim of this study was to estimate the prevalence of MBI in people with subjective cognitive complains (SCCs) in primary care centers and observe the evolution in a longitudinal study.

Three hundred twenty-three participants belonging to the CompAS longitudinal study who attended primary care centers with SCCs and without previous diagnosis of dementia and other neurological or psychiatric disturbances underwent clinical, neurological, and neuropsychological examinations at baseline and at two follow-up times (around 24 and 60 moths). At each evaluation point participants were diagnosed as Cognitively Unimpaired (UC), Mild Cognitive Impairment (MCI) and SDC; at the follow-up assessments dementia was diagnosed as well. Diagnosis of MBI was made via a series of semistructured independent interviews with patients and relatives in accordance with the ISTAART-AA criteria and using the Spanish MBI-C cut-off point (Mallo et al, 2019). Frequencies of participants diagnosed in each category were obtained and evolution of the MBI along the follow-up evaluations was studied.

MBI diagnosis prevalence was 22.9% at baseline corresponding to 74 individuals of which at 24 months follow-up were MBI stable 31.1%, 14.9% evolved to MCI, 1.3% to dementia and 40% to CU, (attrition 12.2%). At 60 months follow-up, from the 23 individuals with MBI, 6 remained stable (26.1%), 6 (21.1%) evolved to MCI, 2 (8.7%) to dementia, 3 (13%) to CU, (attrition 26.1%) (Figure 1).

Results indicated that almost a quarter of individuals attending primary care centers with SCCs without previous diagnosis of dementia or psychiatric disorders are MBI. An important part of them evolve to UC or MCI, and only a minority progress to dementia. More studies are needed to analyze the cognitive, personal, and biological factors that determine this evolution.

The assessment of MBI involves two important issues: 1) to know the underlying structure of the Mild Behavioral Impairment Checklist (MBI-C) a questionnaire designed to evaluates Neuropsychiatric Symptoms (NPS) in pre-dementia states; and 2) to consider self and proxy (i.e., study partner) symptom ratings that may not capture comparable samples. Our objective is to give some answer to these questions: first, to analyze the underlying structure of the MBI-C at baseline and follow-up using Multidimensional Scaling (MDS) and two, to determine how self and proxy ratings and the choice of rating type impact in the results of the MBI-C.

To analyze MBI-C structure, 200 Subjective Cognitive Decline and Mild Cognitive Impairment patients from the CompAS longitudinal study completed baseline and follow-up assessments. Two-step bidimensional weighted dichotomous MDS were performed. All items were included in the first step. Items closely associated with each dimension (1 SD above or below the mean) were selected in a second step to obtain the final models solution.

We will also present a review of the literature on the importance of self and proxy MBI-C ratings. We will also present new empirical evidence based on data from over 10,000 cognitively normal.

Results from baseline and follow-up showed two dimensions: Dimension I (right-left) differentiate high and low emotional activation and Dimension II (top-down) high and low behavioral activation. The combination of both generates 4 quadrants: resistance, restlessness, flattening and desolation. The final models were built considering the most relevant items, with little differences between baseline and follow-up. The good fit of the models, type of two-dimensional solution and group weights were similar in baseline and follow-up.

Regarding our second objective, the results suggest that self and proxy ratings may not capture comparable samples and that the choice of rating type can indeed impact the conclusions drawn from analysis.

The 4 quadrants identified could be the most useful NPS to determine risk factors for predementia patients. Also, the findings suggest that the way of applying the MBI-C has relevant implications.

Mild cognitive impairment (MCI), as a stage in the cognitive continuum between normal ageing and dementia, is mainly characterized by memory impairment. The aims of this study were to examine CANTAB measures of temporal changes of visual memory in MCI and to evaluate the usefulness of the baseline scores for predicting changes in cognitive status.

The study included 201 participants aged over 50 years with subjective cognitive complaints. Visual memory was assessed with four CANTAB tests [paired associates learning (PAL), delayed matching to sample (DMS), pattern recognition memory (PRM) and spatial span (SSP)] administered at baseline and on two further occasions, with a follow-up interval of 18–24 months. Participants were divided into three groups according to the change in their cognitive status: participants with subjective cognitive complaints who remained stable, MCI participants who remained stable (MCI-Stable) and MCI participants whose cognitive deterioration continued (MCI-Worsened). Linear mixed models were used to model longitudinal changes, with evaluation time as a fixed variable, and multinomial regression models were used to predict changes in cognitive status.

Isolated significant effects were obtained for age and group with all CANTAB tests used. Interactions between evaluation time and group were identified in the PAL and DMS tests, indicating different temporal patterns depending on the changes in cognitive status. Regression models also indicated that CANTAB scores were good predictors of changes in cognitive status.

Decline in visual memory measured by PAL and DMS tests can successfully distinguish different types of MCI, and considered together PAL, DMS, PRM and SSP can predict changes in cognitive status.

To study the influence of cognitive reserve (CR) on cognitive performance of individuals with subjective cognitive complaints (SCCs) within a period of 36 months.

We used a general linear model repeated measures procedure to analyze the differences in performance between three assessments. We used a longitudinal structural equation modeling to analyze the relationship between CR and cognitive performance at baseline and at two follow-up assessments.

Participants with SCCs were recruited and assessed in primary care health centers.

A total of 212 participants older than 50 years with SCCs.

Cognitive reserve data were collected with an ad hoc questionnaire administered to the subjects in an interview. General cognitive performance (GCP), episodic memory (EM), and working memory (WM) have been evaluated. The Mini-Mental State Examination and the total score of Spanish version of the Cambridge Cognitive Examination evaluated the GCP. Episodic memory was assessed with the Spanish version of the California Verbal Learning. Working memory was evaluated by the counting span task and the listening span task.

The satisfactory fit of the proposed model confirmed the direct effects of CR on WM and GCP at baseline, as well as indirect effects on EM and WM at first and second follow-up. Indirect effects of CR on other cognitive constructs via WM were observed over time.

The proposed model is useful for measuring the influence of CR on cognitive performance over time. Cognitive response acquired throughout life may influence cognitive performance in old age and prevent cognitive deterioration, thus increasing processing resources via WM.

To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.

Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen’s kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm.

Primary care health centers.

128 participants: 78 cognitively unimpaired and 50 with MCI.

Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score.

30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen’s kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score.

ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.

To estimate the prevalence of Mild Behavioral Impairment (MBI) in people with Subjective Cognitive Decline (SCD), and validate the Mild Behavioral Impairment Checklist (MBI-C) with respect to score distribution, sensitivity, specificity, and utility for MBI diagnosis, as well as correlation with other neuropsychological tests.

Correlational study with a convenience sampling. Descriptive, logistic regression, ROC curve, and bivariate correlations analyses were performed.

Primary care health centers.

127 patients with SCD.

An extensive evaluation, including Questionnaire for Subjective Memory Complaints, Mini-Mental State Examination, Cambridge Cognitive Assessment-Revised, Neuropsychiatric Inventory-Questionnaire (NPI-Q), the Geriatric Depression Scale-15 items (GDS-15), the Lawton and Brody Index and the MBI-C, which was administered by phone to participants’ informants.

MBI prevalence was 5.8% in those with SCD. The total MBI-C scoring was low and differentiated people with MBI at a cut-off point of 8.5 (optimizing sensitivity and specificity). MBI-C total scoring correlated positively with NPI-Q, Questionnaire for Subjective Cognitive Complaints (QSCC) from the informant and GDS-15.

The phone administration of the MBI-C is useful for detecting MBI in people with SCD. The prevalence of MBI in SCD was low. The MBI-C detected subtle Neuropsychiatric symptoms (NPS) that were correlated with scores on the NPI-Q, depressive symptomatology (GDS-15), and memory performance perceived by their relatives (QSCC). Next steps are to determine the predictive utility of MBI in SCD, and its relation to incident cognitive decline over time.