Spanish Validation of the Music in Dementia Assessment Scales

Music therapy has been described as a beneficial intervention for people with dementia. To provide a music therapy outcome measure, McDermott et al. (2015) developed the Music in Dementia Assessment Scales (MiDAS). The preliminary original validation indicated that MiDAS had acceptable to good psychometric properties. This study aimed to present the translation and cross-cultural adaptation of the MIDAS to the Spanish language and to show some validity sources using the Spanish version of the scale. MiDAS was adapted following the guidelines of Beaton et al. (2000), Muñiz et al. (2013) and Ridder et al. (2015), A psychometric validation study was conducted with a sample of 80 care home residents with moderate-severe dementia. Acceptable reliability values according to Cronbach's alpha were obtained, as was good interobserver reliability at one rating time point, based on Kendall's W test. The concurrent criterion validity values were positive, particularly in terms of the correlation coefficients of the criterion measure (specifically, the QoL-AD measures) and the item analysis, as shown by the correlation matrices. A one-factor confirmatory factor analysis (CFA) did not indicate a good fit for the obtained models, but acceptable and optimal values were observed for various parameters. The results indicate the usefulness of this tool, with sources of evidence of validity and reliability, although it is necessary to note the limitations of some of the results, as in the construct validity analysis. The MiDAS-ESP is a useful tool in clinical practice that can be used for measuring the effect of music therapy.

Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients

Background/Objective: This study aims to characterize the differences on the short-term temporal network dynamics of the undirected and weighted whole-brain functional connectivity between healthy aging individuals and people with mild cognitive impairment (MCI). The Network Change Point Detection algorithm was applied to identify the significant change points in the resting-state fMRI register, and we analyzed the fluctuations in the topological properties of the sub-networks between significant change points. Method: Ten MCI patients matched by gender and age in 1:1 ratio to healthy controls screened during patient recruitment. A neuropsychological evaluation was done to both groups as well as functional magnetic images were obtained with a Philips 3.0T. All the images were preprocessed and statistically analyzed through dynamic point estimation tools. Results: No statistically significant differences were found between groups in the number of significant change points in the functional connectivity networks. However, an interaction effect of age and state was detected on the intra-participant variability of the network strength. Conclusions: The progression of states was associated to higher variability in the patient's group. Additionally, higher performance in the prospective and retrospective memory scale was associated with higher median network strength.

Resting-state functional dynamic connectivity and healthy aging: A sliding-window network analysis

BACKGROUND

Graph theory has been widely used to study structural and functional brain connectivity changes in healthy aging, and occasionally with clinical samples; in both cases, during task-related and resting-state experiments. Recent studies have focused their interest on dynamic changes during a resting-state fMRI register in order to identify differences in non-stationary patterns associated with the aging process. The objective of this study was to characterize resting-state fMRI network dynamics in order to study the healthy aging process.

METHOD

114 healthy older adults were measured in a resting-state paradigm using fMRI. A sliding-window approach to graph theory was used to measure the mean degree, average path length, clustering coefficient, and small-worldness of each subnetwork, and the impact of age and time in each graph measure was assessed.

RESULTS

A combined effect of age and time was detected in mean degree, average path length, and small-worldness, where participants aged 75 to 79 showed a curvilinear trend with reduced network density and increased small-world coefficient in the middle of the register.

CONCLUSION

An effect of age was observed on average path length, with younger participants showing slightly lower scores.

Resting-State Functional Connectivity Dynamics in Healthy Aging: An Approach Through Network Change Point Detection

This study aims at assessing the impact of age on the short-term temporal dynamics of the topological properties of the undirected and weighted whole-brain functional connectivity (FC) networks. We studied the association between the participant's age and the number of significant change points detected through the Network Change Point Detection algorithm. Secondary, we defined state as the resting-state functional magnetic resonance imaging (rs-fMRI) subsequence between two significant change points, obtaining the FC network in each state and participant and characterized their network topological properties. The data comprise the rs-fMRI sequences of 114 healthy individuals combined from 3 different studies conducted at the Department of Medicine, School of Medicine and Health Sciences, University of Barcelona. Participants were healthy people in the absence of any pathology that could interfere with the scanning procedures, as well as any chronic illness that implied a short-lived situation. Topological properties of everyone's FC networks were characterized by their network strength, transitivity, characteristic path length, and small-worldness, analyzing the effect of age in those observed distributions. To that effect, we constructed a mixed linear model for each network topological property with age, state, and state duration as the linear predictors. Several statistically significant relationships have been estimated between the indicators of the FC networks that show a certain regular pattern of change in the networks during the time of registration at the resting fMRI paradigm. These dynamic changes seem to be related to the age of each group studied. Healthy aging could be characterized by FC dynamics patterns.

The Effects of Exposure to Recent Autobiographical Events on Declarative Memory in Amnestic Mild Cognitive Impairment: A Preliminary Pilot Study

BACKGROUND

Individuals with amnestic Mild Cognitive Impairment (aMCI) are at heightened risk of developing Alzheimer's dementia. In recent years, much attention has been given to the search for new interventions to slow down the progression of cognitive decline of these patients. Wearable digital camera devices are one form of new technology that captures images of one's life events, so they constitute a promising method to be used as a means to stimulate recent autobiographical memory.

OBJECTIVE

This preliminary study investigates the ability of a new cognitive intervention based on exposure to recent autobiographical memory captured by wearable cameras to improve episodic memory in patients with aMCI.

METHODS

Seventeen subjects wore a wearable camera while they went about their daily activities. The images captured were converted into eight different 3-minute films containing the most relevant information of each event. The intervention involved eight individualized weekly sessions during which patients were exposed to a different autobiographical event each week. Besides, several specific questions were formulated within each session. Clinical questionnaires assessing cognitive reserve, premorbid intelligence, depression, and anxiety were administered at baseline. Measures of objective episodic memory were applied at baseline and at post-treatment.

RESULTS

Significant improvements were observed at post-treatment in memory measures, and significant associations were found between memory change scores and age and cognitive reserve. Anyway, these associations did not reach statistical significance after adjusting for multiple comparisons.

CONCLUSION

The present study provides preliminary evidence that aMCI patients may benefit from a cognitive intervention program based on re-experiencing recent autobiographical events. However, future studies incorporating a control group will be needed to confirm these preliminary findings.

Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment

Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer's disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results.