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PRELIMINARY RESULTS FROM THE SYNERGIC TRIAL: A MULTIMODAL INTERVENTION FOR MILD COGNITIVE IMPAIRMENT

ABSTRACT: Abstract Mild Cognitive Impairment (MCI) is a prevalent pre-dementia state. Emerging evidence supports that physical exercise and computerized cognitive training can independently improve cognitive outcomes. Recently, vitamin D deficiency has been associated with increased risk of progression to dementia. To date, a multimodal intervention based on physical exercises, combined with computerized cognitive training and vitamin D supplementation has not yet been tested in MCI. The SYNERGIC Trial (SYNchronizing Exercises, Remedies in Gait and Cognition) is a multisite clinical-trial aimed to improve cognition and delay progression from MCI to dementia, using the aforementioned multimodal intervention. 200 participants with MCI are being assigned to active or sham interventions. Active interventions (3 days/week) include combined aerobic and progressive resistance training exercise, progressive cognitive training, and vitamin D supplementation (4000 IU/daily) for 6 months. Control interventions consist of balance and toning exercises, control cognitive training, and placebo vitamin D. Primary outcome is improvement in ADAS-Cog 13 (range 0–84; lower scores indicate better cognitive function). ANCOVA adjusting for baseline values was the primary analysis. ClinicalTrials.gov Identifier: NCT02808676. Preliminary results show that combining exercise and cognitive training showed the best improvements on ADAS-Cog 13 after 6 months, followed by combined exercise (combined exercise and cognitive training: 11.67 ± 4.58; combined exercise: 15.29 ± 6.64; balance and toning: 20.77 ± 9.67; p= 0.010). The control group did not improve ADAS-Cog 13 scores. Our preliminary results show that a multimodal intervention with physical and cognitive training is feasible and have a positive effect in improving cognition in MCI.

SUBMITTER: Montero Odasso M

PROVIDER: S-EPMC6227110 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Project description:Functional brain connectivity (FBC), or areas that are anatomically separate but temporally synchronized in their activation, represent a sensitive biomarker for monitoring dementia progression. It is unclear whether frailty is associated with FBC in those at higher risk of progression to dementia (e.g., mild cognitive impairment -MCI-) and if sex plays a role. We used baseline data from the SYNERGIC trial, including participants with MCI that received brain MRI. In this cross-sectional analyses (n = 100), we measured frailty using a deficit accumulation frailty index. Using the CONN toolbox, we assessed FBC of networks and regions of interest across the entire connectome. We used Pearson's correlation to investigate the relationship between FBC and frailty index in the full sample and by sex. We also divided the full sample and each sex into tertiles based upon their frailty index score and then assessed between-tertile differences in FBC. The full sample (cluster: size = 291 p-FDR < 0.05) and males (cluster: size = 993 and 451 p-FDR < 0.01) demonstrated that increasing (stronger) connectivity between the right hippocampus and clusters in the temporal gyrus was positively correlated with increasing (worse) frailty. Males also demonstrated between-tertile differences in right hippocampus connectivity to clusters in the lateral occipital cortex (cluster: size = 289 p-FDR < 0.05). Regardless of frailty status, females demonstrated stronger within-network connectivity of the Default-Mode (p = 0.024). Our results suggest that increasing (worse) frailty was associated with increasing (stronger) connectivity between regions not typically linked, which may reflect a compensation tactic by the plastic brain. Furthermore, the relationship between the two variables appears to differ by sex. Our results may help elucidate why specific individuals progress to a dementia syndrome. NCT02808676. https://www.clinicaltrials.gov/ct2/show/NCT02808676.

Project description:Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.

Project description:IntroductionThe diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI.Methods and analysisThis study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline.Ethics and disseminationEthics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.Trial registration numberClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.

Project description:Dementia affects 35 million people worldwide and is currently incurable. Many cases may be preventable because regular participation in physical, mental and social leisure activities during middle age is associated with up to 47% dementia risk reduction. However, the majority of middle-aged adults are not active enough. MCI is therefore a clear target for activity interventions aimed at reducing dementia risk. An active lifestyle during middle age reduces dementia risk but it remains to be determined if increased activity reduces dementia risk when MCI is already evident. Before this can be investigated conclusively, complex multimodal activity programmes are required that (1) combine multiple health promoting activities, (2) engage people with MCI, and (3) result in sufficient adherence rates.We designed the ThinkingFit programme to engage people with MCI in a complex intervention comprised of three activity components: physical activity, group-based cognitive stimulation (GCST) and individual cognitive stimulation (ICST). Engagement and adherence was promoted by applying specific psychological techniques to enhance behavioural flexibility in an early pre-phase and during the course of the intervention. To pilot the intervention, participants served as their own controls during a 6- to 12-week run-in period, which was followed by 12 weeks of activity intervention.Out of 212 MCI patients screened, 163 were eligible, 70 consented and 67 completed the intervention (mean age 74 years). Activity adherence rates were high: physical activity?=?71%; GCST?=?83%; ICST?=?67%. Significant treatment effects (p?<?.05) were evident on physical health outcomes (decreased BMI and systolic blood pressure, [pre/post values of 26.3/25.9 kg/m2 and 145/136 mmHg respectively]), fitness (decreased resting and recovery heart rate [68/65 bpm and 75/69 bpm]), and cognition (improved working memory [5.3/6.3 items]).We found satisfactory recruitment, retention and engagement rates, coupled with significant treatment effects in elderly MCI patients. It appears feasible to conduct randomized controlled trials of the dementia prevention potential of complex multimodal activity programmes like ThinkingFit.ClinicalTrials.gov registration nr: NCT01603862; date: 17/5/2012.

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PRELIMINARY RESULTS FROM THE SYNERGIC TRIAL: A MULTIMODAL INTERVENTION FOR MILD COGNITIVE IMPAIRMENT

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