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The presence of missing data and small sample sizes are very common in social and health sciences. Concurrently to present a methodology to solve the small sample size and missing data, we aim to present a definition of Cognitive Reserve for people with Down Syndrome. This population has become an appealing focus to study this concept because of the high incidence of dementia. The accidental sample comprised 35 persons with DS (16–35 years). A total of 12 variables were acquired, four of them had missing data. Two types of multiple imputation were made. Confirmatory factor analysis with Bayesian estimations was performed on the final database with non-informative priors. However, to solve the sample size problem, two additional corrections were made: first, we followed the Jiang and Yuan (2017) schema, and second, we made a Jackknife correlation correction. The estimations of the confirmatory factor analysis, as well as the global fit, are adequate. As an applied perspective, the acceptable fit of our model suggests the possibility of operationalizing the latent factor Cognitive Reserve in a simple way to measure it in the Down Syndrome population.

Emerging evidence suggests that an effective or functional connectivity network does not use a static process over time but incorporates dynamic connectivity that shows changes in neuronal activity patterns. Using structural equation models (SEMs), we estimated a dynamic component of the effective network through the effects (recursive and nonrecursive) between regions of interest (ROIs), taking into account the lag 1 effect. The aim of the paper was to find the best structural equation model (SEM) to represent dynamic effective connectivity in people with Down syndrome (DS) in comparison with healthy controls. Twenty-two people with DS were registered in a functional magnetic resonance imaging (fMRI) resting-state paradigm for a period of six minutes. In addition, 22 controls, matched by age and sex, were analyzed with the same statistical approach. In both groups, we found the best global model, which included 6 ROIs within the default mode network (DMN). Connectivity patterns appeared to be different in both groups, and networks in people with DS showed more complexity and had more significant effects than networks in control participants. However, both groups had synchronous and dynamic effects associated with ROIs 3 and 4 related to the upper parietal areas in both brain hemispheres as axes of association and functional integration. It is evident that the correct classification of these groups, especially in cognitive competence, is a good initial step to propose a biomarker in network complexity studies.

Background Down syndrome (DS) is a chromosomal disorder that causes intellectual disability. Few studies have been conducted on functional connectivity using resting‐state fMRI (functional magnetic resonance imaging) signals or more specifically, on the relevant structure and density of the default mode network (DMN). Although data on this issue have been reported in adult DS individuals (age: >45 years), the DMN properties in young DS individuals have not been studied. The aim of this study was to describe the density and structure of the DMN network from fMRI signals in young DS (age, DMN in Down syndrome. Network indicators in DS. Functional connectivity.

Background: The study of the Default Mode Network (DMN) has been shown to be sensitive for the recognition of connectivity patterns between the brain areas involved in this network. It has been hypothesized that the connectivity patterns in this network are related to different cognitive states. Purpose: In this study, we explored the relationship that can be estimated between these functional connectivity patterns of the DMN with the Quality-of-Life levels in people with Down syndrome, since no relevant data has been provided for this population. Methods: 22 young people with Down syndrome were evaluated, they were given a large evaluation battery that included the Spanish adaptation of the Personal Outcome Scale (POS). Likewise, fMRI sequences were obtained on a 3T resonator. For each subject, the DMN functional connectivity network was studied by estimating the indicators of complexity networks. The variability obtained in the Down syndrome group was studied by taking into account the Quality-of-Life distribution. Results: There is a negative correlation between the complexity of the connectivity networks and the Quality-of-Life values. Conclusions: The results are interpreted as evidence that, even at rest, connectivity levels are detected as already shown in the community population and that less intense connectivity levels correlate with higher levels of Quality of Life in people with Down syndrome.