The Latest Articles on Mild Cognitive Impairment

Our staff editors continue to share exciting, interesting, and thought-provoking reading material in the recommended articles series.

This week, we would like to share several latest articles on Mild Cognitive Impairment.

Title: The Effectiveness of Dance Movement Interventions for Older Adults with Mild Cognitive Impairment, Alzheimer’s Disease, and Dementia: A Systematic Scoping Review and Meta-analysis
Authors: Dan Tao, Roger Awan-Scully, Garrett I Ash, Zhong Pei, Yaodong Gu, Yang Gao, Alistair Cole, Julien S Baker
Type: Review
Abstract:

Objectives
To synthesize evidence and summarize research findings related to the effectiveness and feasibility of dance movement intervention (DMI) in older adults with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and dementia; to systemically map existing research gaps and research directions for future practice.

Methods
A systematic search was conducted using six electronic databases: Web of Science, PubMed, PsycINFO, MEDLINE, ScienceDirect, and Cochrane Central Register of Controlled Trials. The methodological quality of included studies was assessed using the Cochrane Risk of Bias Tool for Randomized Trials (RoB 2) and The Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I).

Results
29 dance intervention studies (13 RCT studies) were included in the scoping review: 62% of MCI, 10% of AD, and 28% of dementia; a total of 1708 participants (Female=1247; Male=461) aged from 63.8 (± 5.24) to 85.8 (±5.27) years old. Eight RCT studies were included in the meta-analysis; results indicated that dance interventions had a significant effect on global cognition, memory, balance, and significantly decreased depression. No significant effects were found for executive function.

Conclusion
Dance is a non-pharmacological, effective, affordable, and engaging intervention that can be used as a complementary treatment for older adults with MCI, AD, and dementia.
Access this article: https://doi.org/10.1016/j.arr.2023.102120

Title: A fixel-based analysis of white matter reductions early detects Parkinson disease with mild cognitive impairment
Authors: Ting-Wei Liao, Jiun-Jie Wang, Chih-Chien Tsai, Pei-Ning Wang, Yao-Liang Chen, Yi-Ming Wu, Yih-Ru Wu
Type: Research Article
Abstract:

Background
White matter (WM) tract alterations are early signs of cognitive impairment in Parkinson disease (PD) patients. Fixel-based analysis (FBA) has advantages over traditional diffusion tensor imaging in managing complex and crossing fibers. We used FBA to measure fiber-specific changes in patients with PD mild cognitive impairment (PD-MCI) and PD normal cognition (PD-NC).

Methods
Seventy-one patients with PD without dementia were included: 39 PD-MCI and 32 PD-NC. All underwent diffusion-weighted imaging, clinical examinations, and tests to evaluate their cognitive function globally and in five cognitive domains. FBA was used to investigate fiber-tract alterations and compare PD-MCI with PD-NC subjects. Correlations with each cognitive test were analyzed.

Results
Patients with PD-MCI were significantly older (P = 0.044), had a higher male-to-female ratio (P = 0.006) and total Unified Parkinson's Disease Rating Scale score (P = 0.001). All fixel-based metrics were significantly reduced within the body of the corpus callosum and superior corona radiata in PD-MCI patients (family-wise error-corrected P value < 0.05) compared with PD-NC patients. The cingulum, superior longitudinal fasciculi, and thalamocortical circuit exhibited predominantly fiber-bundle cross-section (FC) changes. In regression analysis, reduced FC values in cerebellar circuits were associated with poor motor function in PD-MCI patients and poor picture-naming ability in PD-NC patients.

Conclusions
PD-MCI patients have significant WM alterations compared with PD-NC patients. FBA revealed these changes in various bundle tracts, helping us to better understand specific WM changes that are functionally implicated in PD cognitive decline. FBA is potentially useful in detecting early cognitive decline in PD.
Access this article: https://doi.org/10.1016/j.bj.2023.100678

Title: Detecting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease using FLAIR MRI Biomarkers
Authors: Owen Crystal, Pejman J. Maralani, Sandra Black, Corinne Fischer, Alan R. Moody, April Khademi
Type: Research Article
Abstract:

Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer’s disease (AD) and while it presents as an imperative intervention window, it is difficult to detect which subjects convert to AD (cMCI) and which ones remain stable (sMCI). The objective of this work was to investigate fluid-attenuated inversion recovery (FLAIR) MRI biomarkers and their ability to differentiate between sMCI and cMCI subjects in cross-sectional and longitudinal data. Three types of biomarkers were investigated: volume, intensity and texture. Volume biomarkers included total brain volume, cerebrospinal fluid volume (CSF), lateral ventricular volume, white matter lesion volume, subarachnoid CSF, and grey matter (GM) and white matter (WM), all normalized to intracranial volume. The mean intensity, kurtosis, and skewness of the GM and WM made up the intensity features. Texture features quantified homogeneity and microstructural tissue changes of GM and WM regions. Composite indices were also considered, which are biomarkers that represent an aggregate sum (z-score normalization and summation) of all biomarkers. The FLAIR MRI biomarkers successfully identified high-risk subjects as significant differences (p < 0.05) were found between the means of the sMCI and cMCI groups and the rate of change over time for several individual biomarkers as well as the composite indices for both cross-sectional and longitudinal analyses. Classification accuracy and feature importance analysis showed volume biomarkers to be most predictive, however, best performance was obtained when complimenting the volume biomarkers with the intensity and texture features. Using all the biomarkers, accuracy of 86.2% and 69.2% was achieved for normal control-AD and sMCI-cMCI classification respectively. Survival analysis demonstrated that the majority of the biomarkers showed a noticeable impact on the AD conversion probability 4 years prior to conversion. Composite indices were the top performers for all analyses including feature importance, classification, and survival analysis. This demonstrated their ability to summarize various dimensions of disease into single-valued metrics. Significant correlation (p < 0.05) with phosphorylated-tau and amyloid-beta CSF biomarkers was found with all the FLAIR biomarkers. The proposed biomarker system is easily attained as FLAIR is routinely acquired, models are not computationally intensive and the results are explainable, thus making this pipeline easily integrated into clinical workflow.

Access this article: https://doi.org/10.1016/j.nicl.2023.103533

Title: Functional connectivity changes in mild cognitive impairment: a meta-analysis of M/EEG studies
Authors: Giulia Buzi, Chiara Fornari, Alessio Perinelli, Veronica Mazza
Type: Research Article
Abstract:

Objective
Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer’s disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD.

Methods
We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes (“MetaNSUE”) of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023.

Results
Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25).

Conclusions
Alterations of alpha synchrony are present even at MCI stage.

Significance
Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.
Access this article: https://doi.org/10.1016/j.clinph.2023.10.011

Title: Computerized Cognitive and Skills Training in Older People with Mild Cognitive Impairment: Using Ecological Momentary Assessment to Index Treatment Related Changes in Real-World Performance of Technology-Dependent Functional Tasks
Authors: Courtney Dowell-Esquivell, Sara J. Czaja, Peter Kallestrup, Colin A. Depp, John N. Saber, Philip D. Harvey
Type: Research Article
Abstract:

Objectives
Cognitive and functional skills training improves skills and cognitive test performance, but the true test of efficacy is real-world transfer. We trained participants with mild cognitive impairment (MCI) or normal cognition (NC) for up to 12 weeks on six technology-related skills using remote computerized functional skills assessment and training (FUNSAT™) software. Using ecological momentary assessment (EMA), we measured real-world performance of the technology-related skills over 6 months and related EMA-identified changes in performance to training gains.

Design
Randomized clinical trial with post-training follow-up.

Setting
14 Community centers in New York City and Miami.

Participants
Older adults with normal cognition (n=72) or well-defined MCI (n = 92), ranging in age from 60-90, primarily female, and racially and ethnically diverse.

Intervention
Computerized cognitive and skills training.

Measurements
EMA surveys measuring trained and untrained functional skills 3 or more days per week for 6 months and training gains from baseline to end of training.

Results
Training gains in completion times across all 6 tasks were significant (p<.001) for both samples, with effect sizes more than 1.0 SD for all tasks. EMA surveys detected increases in performance for both trained (p<.03) and untrained (p<.001) technology-related skills for both samples. Training gains in completion times predicted increases in performance of both trained and untrained technology-related skills (all p<.001).

Conclusions
Computerized training produces increases in real-world performance of important technology-related skills. These gains continued after the end of training, with greater gains in MCI participants.
Access this article: https://doi.org/10.1016/j.jagp.2023.10.014