Abstract
Objective
Semantic verbal fluency constitutes a good candidate for identifying cognitive impairment. This paper offers normative data of different semantic verbal fluency tests for ...middle-aged and older adults natives from Spain considering sociodemographic factors, and different measures for each specific category (number of words produced, errors, and words evoked every 15 s).
Method
Two thousand and eighty-eight cognitively unimpaired subjects aged between 50 and 89 years old, community dwelling, participated in the study. The statistical procedure includes the conversion of percentile ranges into scalar scores. Secondly, the effects of age, education and gender were verified. Linear regressions are used to calculate the scalar adjusted scores.
Results
Scalar scores and percentiles corresponding to all semantic verbal fluency tests across different measures are shown. Additional tables, which show the points that must be added or subtracted from direct scores, are provided for Education regarding the total number of “animals” and “clothes” evoked by participants, as well as for Age and Education in case of the total number of “clothes”. Gender affects the number of “clothes” produced by participants in the first two 15-second segments.
Conclusions
The current norms should provide clinically useful data for evaluating Spanish-speaking natives from Spain aged from 50 to 89 years.
Background
Cognitive training has been found to be effective in preventing and delaying cognitive decline in MCI and early dementia, and gains could be enhanced with transcranial electrical ...stimulation (tDCS). Cognitive‐training applications (app) allow remote interventions, optimize the cost‐benefit ratio, and a continuous monitoring. Most of apps are web‐based applications but specific narrative cognitive‐training video‐games are scarce. Our RCT aims to compare memory changes after training using a web‐based app (i.e., NeuronUp) and a narrative video‐game (i.e., ‘Following the traces of time’) combining with tDCS or Sham (placebo).
Method
Fifty‐three participants with SCDs and MCI were randomized assigned to the five experimental groups (i.e., Active‐control=11; NeuronUP‐tDCS=14; NeuronUP‐Sham=9; Videogame‐tDCS=9; Videogame‐Sham=10). NeuronUP groups sequentially performed 24 computerized activities (in 8 activities/session cycles) with increasing in complexity according actual performance. Videogame groups resolved puzzles with three difficulty levels integrated into a meaningful narrative plot allowing them to advance in the story. NeuronUP and Videogame mainly implemented memory and executive function training. The active‐control group attended specific classes for older people (i.e., computing and mildfulness/philosophy). All interventions extended along 20 hours (5 weekly sessions of 120 min for 2 weeks) and participants simultaneously received 20’ of tDCS/Sham in the last 6 sessions.
Pre‐Post assessments were accomplished to tests changes in measures of immediate verbal recall (Lists A and B of the RAVLT), and prospective memory (Forms 1 and 2 of the Event‐Related Task; MPMT).
Results
Repeat measures ANOVA showed that Immediate recall (Figure 1) significantly improved in post‐intervention, F(1,48)=12.56, p=.001, ηp2
=.207, but Group*Measurement interaction was not significant. Group factor differences only pointed out significant improvement in the tDCS‐NeuronUP group compared to the Sham‐Videogame group.
Prospective memory (Figure 2) also showed improvement in post‐intervention, F(1,48)=12.79, p=.001, ηp2
=.210, but neither main Group effect or Group*Measurement interaction achieved significance.
Conclusions
Trends showed significant memory improvements particularly in NeuronUP‐groups. tDCS seems to take some responsibility in the improvement mainly in the videogame‐group. The intensity of training and automatic difficulty adjustment associated with the NeuronUP app, along with the enhancing effect of tDCS, appear to be responsible for further improving memory.
(1) Background: Early identification of mild cognitive impairment (MCI) in people reporting subjective cognitive complaints (SCC) and the study of progression of cognitive decline are important ...issues in dementia research. This paper examines whether empirically derived procedures predict progression from MCI to dementia. (2) Methods: At baseline, 192 participants with SCC were diagnosed according to clinical criteria as cognitively unimpaired (70), single-domain amnestic MCI (65), multiple-domain amnestic MCI (33) and multiple-domain non-amnestic MCI (24). A two-stage hierarchical cluster analysis was performed for empirical classification. Categorical regression analysis was then used to assess the predictive value of the clusters obtained. Participants were re-assessed after 36 months. (3) Results: Participants were grouped into four empirically derived clusters: Cluster 1, similar to multiple-domain amnestic MCI; Cluster 2, characterized by subjective cognitive decline (SCD) but with low scores in language and working memory; Cluster 3, with specific deterioration in episodic memory, similar to single-domain amnestic MCI; and Cluster 4, with SCD but with scores above the mean in all domains. The majority of participants who progressed to dementia were included in Cluster 1. (4) Conclusions: Cluster analysis differentiated between MCI and SCD in a sample of people with SCC and empirical criteria were more closely associated with progression to dementia than standard criteria.
Background
The Mild Behavioral Impairment Checklist (MBI‐C) (Ismail et al., 2017) is a 34‐item scale that evaluates Neuropsychiatric Symptoms (NPS) in pre‐dementia states. Its underlying structure ...has been little studied and remains largely unknown. Factor analysis‐based approaches may have difficulty finding stable relationships between NPS collected on a checklist to account for the large heterogeneity of manifestations. Thus, our objective was to analyze the underlying structure of the MBI‐C using an alternative approach.
Method
Eighty‐two MCI and 117 SCD older adults of the CompAS were recruited in primary care health centers. Asymptomatic participants were excluded from the analyses. A weighted model of multidimensional scaling (MDS) will be used. A two‐step bidimensional weighted dichotomous MDS was 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 model solution.
Results
The results obtained in the two analyzes were similar in terms of the good fit of the models, the type of two‐dimensional solution and the group weights. Model weights were also similar for the three diagnostic groups.
The final model was built considering the 12 most relevant items selected in the first step analysis and showed optimal fit indices (stress‐II = .59; D.A.F. = .94).
Figure 1 shows the coordinates for the 12 selected items in a bidimensional solution. Dimension I (right‐left) differentiate high and low emotional activation of NPS. Dimension II (top‐down) distinguishes between high and low behavioral activation. The combination of both generates four quadrants, indicating symptoms of resistance (Q‐I: low emotional and high behavioral activation), restlessness (Q‐II: high emotional and behavioral activation), flattening (Q‐III: low emotional and behavioral activation) and desolation (Q‐IV: high emotional and low behavioral activation) (see Figure 1).
Conclusions
The results suggest that two dimensions underlie the most discriminant NPS included in scale (i.e., emotional and behavioral activation). These two dimensions seem to differentiate between four NPS states (resistance, restlessness, flattening, and desolation), which could be the most useful NPS in the determination of risk factors for predementia patients.
Background
The validity of Subjective Cognitive Complaints (SCCs) from dyadic patterns to predict progression to dementia is not yet clear. Some studies suggest that the validity of informant report ...in predicting dementia increases as cognitive function and awareness of symptoms decline. Our aim was to compare validity of informant and participant reports, and its agreement, to predict progression to MCI and conversion to dementia.
Method
A total of 226 participants from the CompAS study were longitudinally assessed (intervals 18–24 months). The sample consisted of SCD (198) and MCI (28) participants. SCCs from participants and informants were assessed at baseline, 1st and 2nd follow‐ups using the QAM questionnaire. Informant‐participant total score agreement (disagreement, over‐ and under‐estimation, were identically considered) were calculated. Logistic regressions were separately performed to distinguish SCD stables and participants progressing either from SCD to MCI/dementia or from MCI to dementia using SCCs reports from participants, informants and agreement as predictive variables.
Results
Informant report at baseline significantly predicted conversion to dementia from baseline to the 3rd follow‐up (β = .173; SE = .048; p<.001; OR = 1.189, CI = 1.082‐1.306), similarly to that observed for informant reports at 1st follow‐up to predict conversion from 1st to the 3rd follow‐up (β = .225; SE = .050; p = .001; OR = 1.252; CI = 1.135‐1.381), and at 2nd follow‐up to predict conversion from 2nd to the 3rd follow‐up (β = .271; SE = .083; p<.001; OR = 1.313; CI = 1.110–1.552) (see Table 1).
Informant report at baseline also successfully predicted the progression to MCI from baseline to the 3rd follow‐up (β = .139; SE = .057; p = .017; OR = 1.49; CI = 1.025‐1.288), similarly to that observed for informant reports at 1st (β = .124; SE = .056; p = .026; OR = 1.132; CI = 1.015‐1.264), and the 2nd follow‐ups (β = .195; SE = .073; p = .008; OR = 1.216; CI = 1.053‐1.404) (see Table 2).
Conversion to dementia (β = .113; SE = .048; p = .018; OR = 1.119; CI = 1.020‐1.229) and progression to MCI (β = .138; SE = .054; p = .011; OR = 1.148; CI = 1.032‐1.277) using Self‐report were only significantly predicted using 1st follow‐up reports.
Agreement did not significantly predict the progression to MCI or dementia at any of measurement points.
Conclusions
Informant report successfully predicted progression and conversion at any transition point. Self‐reports were only predictive at 1st follow‐up. Agreement did not significantly predict progression and conversion.
Background
Severity of the neuropsychiatric symptoms (NPS) is expected to have a negative impact on IADL, adding to the effect that cognitive decline can have and could lead to further impairment of ...functionality. Our objective was to analyze differences in IADL between two longitudinal evaluations in SCC participants with null, intermediate, or high levels of NPS at baseline considering the influence of the cognitive status.
Method
The sample consisted of 242 older adults with SCCs from the CompAS (Compostela Aging Study) and followed up 18‐24 months. Participants were classified according to absence (score ≤ 33th; MBI‐C total score=0), intermediate (score between 33th and 66th; MBI‐C total score ≥0 and ≤5; M=2.18, SD=1.10), and high level of NPS (score ≥ 66th; MBI‐C total score >5; M=10.83, SD=5.32) measured at the baseline by the Mild Behavioral Impairment‐Checklist (MBI‐C). Functionality was assessed by the short version of the Amsterdam‐IADL (A‐IADL‐Q‐SV). A repeated measures mixed‐ANOVA was performed to test intergroup (two groups: null‐NPS=92; Intermediate‐NPS=70; High‐NPS=80) and intra‐subject (two measurements: A‐IADL‐Q‐SV scores at baseline and at follow‐up) differences. In order to test the between‐group differences subtracting the effect of the cognitive objective decline, CAMCOG‐R total score was subsequently introduced as covariate.
Results
Mixed‐ANOVA of repeated measures showed significant differences between groups in all the pairwise comparisons (Null‐NPS<Intermediate‐NPS<High‐NPS), indicating lower A‐IADL‐Q‐SV scores associated to the groups with higher MBI‐C scores, F(2,239)=22.41, p<.001, ηp2
=.158, observed power=1.0 (Figure 1). Measurement main effect and Group*Measurement interaction were not significant. Inclusion of CAMCOG‐R total score as covariate did not alter the results but size effect for the Group differences slightly decreased, F(2,238)=18.28, p<.001, ηp2
=.133, observed power=1.0.
Conclusions
Functionality decline was significantly associated to the severity level of NPS. The consideration of cognitive status as covariate only slightly reduced the size effect observed in between‐group differences, which suggests a certain independence of the NPS in the deterioration of functionality. Longitudinal differences in IADL were not observed in the 18‐24 months follow‐up interval.
Background
Subjective cognitive complaints (SCCs) are a risk factor to dementia. Self‐ and informant‐reports may have different predictive validity along the cognitive decline continuum. We aimed to ...analyze longitudinal differences between self‐ and informant‐reports in stable(‐s) and worsening(‐w) SCD and MCI participants, and the predictive value of these reports on cognitive worsening at different stages of cognitive decline.
Method
A total of 216 participants with SCCs from the CompAS study were longitudinally assessed three times (intervals 18‐24 months). SCD (190) and MCI (26) participants were classified as stable or worsening at the last follow‐up.
SCCs from participants and informants were evaluated through the QAM, and depressive symptoms through the GDS‐15. SCD participants were classified as Low (L‐SCD=119) and High (H‐SCD=71) complainers (below and above 5%ile in self QAM scores).
Mixed‐ANOVAs tested differences between the stable and worsening groups (L‐SCD‐s; H‐SCD‐s; L‐SCD‐w; H‐SCD‐w; MCI‐s; MCI‐w) in the three QAM measurements for participants and informant. Logistic regressions were performed to analyze if QAM scoring of participants and informants at each assessment time predicts worsening.
Results
Significant main effect of Group F(1,5)=40.15, p< .001, ηp2
=.489 showed QAM self‐reports (Figure 1A) were lower for L‐SCD‐s than for H‐SCD‐s, H‐SCD‐w, MCI‐s, MCI‐w; and higher for H‐SCD‐s than for L‐SCD‐s H‐SCD‐w, MCI‐s, MCI‐w. For QAM informant‐reports (Figure 1B) significant Group*Measurement interaction was found, F(10,294)=2.91, p=.002, ηp2
=.090 indicating (Bonferroni tests) that QAM‐3 was higher than QAM‐1 and QAM‐2 for MCI‐w; and only QAM‐3 was higher for MCI‐w than for L‐SCD‐s, H‐SCD‐s, and MCI‐s. Covariate GDS‐15 score did not alter the results.
Neither self‐ nor the informant‐reports predicted L‐SCD‐w. Self‐report‐QAM‐2 β=.300; SE=.088; p=.001; 95%CI=1.35(1.13–1.60) and informant‐report‐QAM‐3 β= .314; SE=.119; p=.008; 95%CI=1.36(1.08–1.73) significantly predicted H‐SCD‐w and MCI‐w self‐report‐QAM‐2: β=.197; SE=.087; p=.023; 95%CI=1.21(1.02–1.44); informant‐report‐QAM‐3: β=.301; SE=.109; p=.006; 95%CI=1.35(1.09–1.67).
Conclusions
Cognitive complains from informants increased at the end of follow‐up for SCD and MCI participants who worsened compared to those who were stable, although significance was only achieved for the MCI‐w. Neither the self‐ nor the informant‐report showed predictive validity at baseline assessment. Self‐reports predicted progression earlier (QAM‐2) and informant‐reports later (QAM‐3).
Background
Presence of significant subjective complaints about cognition (SCD) is considered the first behavioral manifestation of Alzheimer disease (AD). However, SCD has not yet overcome the ...challenge of becoming a reliable preclinical AD marker. Severity indices were proposed to improve the accuracy of complaints when predicting the risk of AD (Jessen et al., 2010).
Our aim was to compare the predictive accuracy of ML algorithms using a more (95%ile) or less (5%ile) restrictive cut‐off point in severity of complaints for classification in Low (LSC) and High (HSC) subjective complaints groups.
Method
One hundred and ninety‐nine participants from the Compostela Aging Study (ComPAS) were identified at baseline as SCDs and completed three follow‐up measurements (54‐72 months). Considering their total scores in the Subjective Memory Questionnaire, participants were classified as LSC and HSC following using two distinct cut‐off points: complaint scores above or below than 5%ile vs 95%ile. Participants were labeled as ‘worsening’ or ‘stable’ based on their progression or not to MCI or AD. ML classifier algorithms (Random Forest, Support Vector Machine, Extra Tree) were applied to forty‐one measures (socio‐demographic, time, health, cognitive, behavioral, cognitive reserve) collected at baseline.
Results
The best performing model was the Random Forest (95%ile: PPV =.87; Sensibility =.92; Specificity =.38; 5%ile: PPV =.66; Sensibility=.61; Specificity =.51). The confusion matrix (Figure 1) showed that: a) both, the more (95%ile) and the less (5%ile) restrictive criteria mostly classified the HSC‐stable participants as LSC‐stable; and b) criterion 5%ile, but not 95%ile, was able to differentially identify progressors based on their complaints. Episodic memory, executive functions, depression, cognitive reserve and progression timing measures assume the highest levels of importance in the ML algorithm that differentially predicts the progression of SCD to MCI and AD according to the level of complaints.
Conclusions
For both the criteria, algorithms failed to successfully identify stable SCD participants. The less restrictive criterion resulted in a better classification than the more lenient one to differentially identify LSC and HSC participants who get worse. Cognitive, affective, cognitive reserve and progression timing were the more prominent variables in conforming the predictive algorithm.
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