Functional parameters indicative of mild cognitive impairment: a systematic review using instrumented kinematic assessment

Motor-Cognitive Dual-Task Cost and Associated Micro Lesions of Cerebellum and Brainstem in Multiple System Atrophy (Parkinsonian Type)

In multiple system atrophy with parkinsonian type (MSA-P), the dual-task cost and the underlying neurological mechanisms remain under-researched. We included 20 early-stage MSA-P patients and 10 matched healthy controls (HC). Using a video-based gait analysis machine, we explored gait characteristics under three conditions: single-task gait (STG), dual-task gait with backward counting (DTG-BC), and dual-task gait with spontaneous animal naming (DTG-SAN). Neuroimaging scans were collected to analyze the gray matter and white matter structures related to the dual-task cost in MSA-P. Our neuroimaging analysis focused on the infratentorial structures, as previous studies have indicated that these regions are closely related to dual-task cost. There were no differences in gait metrics between MSA-P and HC in STG. In the DTG-BC, patients with MSA-P exhibited a higher dual-task cost burden, as indicated by longer turning durations and shorter swing cycles compared to HC. MSA-P patients had decreased gray matter volume in the right culmen and increased radial diffusivity in the left declive compared to HC. Diffusion tensor imaging analysis showed that the higher dual-task cost of the right swing cycle in DTG-BC was related to the higher mean diffusivity of the left mesencephalic locomotor region (MLR). Additionally, a higher dual-task cost of turning duration in DTG-BC was related to increased axial diffusivity and radial diffusivity in the white matter of the bilateral culmen. Patients with MSA-P exhibited a higher dual-task burden compared to HC, and WM deficit in MLR and culmen were related to the disease's specific dual-task cost in MSA-P.

Cognitive profile in burning mouth syndrome versus mild cognitive impairment: A comparative study

OBJECTIVES

This study aims to assess and contrast cognitive and psychological aspects of patients with burning mouth syndrome (BMS-MCI) and geriatric patients (G-MCI) with mild cognitive impairment, focusing on potential predictors like pain, mood disorders, blood biomarkers, and age-related white matter changes (ARWMCs).

METHODS

The study enrolled 40 BMS-MCI and 40 geriatric G-MCI, matching them by age, gender, and educational background. Participants underwent psychological, sleepiness, and cognitive assessment including the Mini-Mental State Exam (MMSE), Trail Making Test (TMT), Corsi Block-Tapping Task, Rey Auditory Verbal Learning Test, Copying Geometric Drawings Test, Frontal Assessment Battery, and Digit Cancellation Test.

RESULTS

G-MCI patients exhibited higher ARWMCs scores in right (p = 0.005**) and left (p < 0.001**) temporal regions, which may relate to specific neurodegenerative processes. Conversely, BMS-MCI patients showed higher levels of depression and anxiety and lower MMSE scores(p < 0.001**), also struggling more with tasks requiring processing speed and executive function, as evidenced by their higher TMT-A scores (p < 0.001**).

CONCLUSIONS

The study highlights particular deficits in global cognition and processing speed for BMS-MCI. The influence of educational background, pain levels, cholesterol, sleep disturbances, and anxiety on these cognitive assessments underscores the need for personalized therapeutic strategies addressing both cognitive and emotional aspects of MCI.

3D-optical motion capturing examination of sensori- and psychomotor abnormalities in mental disorders: Progress and perspectives

Sensori-/psychomotor abnormalities refer to a wide range of disturbances in individual motor, affective and behavioral functions that are often observed in mental disorders. However, many of these studies have mainly used clinical rating scales, which can be potentially confounded by observer bias and are not able to detect subtle sensori-/psychomotor abnormalities. Yet, an innovative three-dimensional (3D) optical motion capturing technology (MoCap) can provide more objective and quantifiable data about movements and posture in psychiatric patients. To draw attention to recent rapid progress in the field, we performed a systematic review using PubMed, Medline, Embase, and Web of Science until May 01st 2024. We included 55 studies in the qualitative analysis and gait was the most examined movement. The identified studies suggested that sensori-/psychomotor abnormalities in neurodevelopmental, mood, schizophrenia spectrum and neurocognitive disorders are associated with alterations in spatiotemporal parameters (speed, step width, length and height; stance time, swing time, double limb support time, phases duration, adjusting sway, acceleration, etc.) during various movements such as walking, running, upper body, hand and head movements. Some studies highlighted the advantages of 3D optical MoCap systems over traditional rating scales and measurements such as actigraphy and ultrasound gait analyses. 3D optical MoCap systems are susceptible to detecting differences not only between patients with mental disorders and healthy persons but also among at-risk individuals exhibiting subtle sensori-/psychomotor abnormalities. Overall, 3D optical MoCap systems hold promise for objectively examining sensori-/psychomotor abnormalities, making them valuable tools for use in future clinical trials.

Dual-task turn velocity - a novel digital biomarker for mild cognitive impairment and dementia

Background

Disorders associated with cognitive impairment impose a significant burden on both families and society. Previous studies have indicated that gait characteristics under dual-task as reliable markers of early cognitive impairment. Therefore, digital gait detection has great potential for future cognitive screening. However, research on digital biomarkers based on smart devices to identify cognitive impairment remains limited. The aim of this study is to explore digital gait biomarkers by utilizing intelligent wearable devices for discriminating mild cognitive impairment and dementia.

Methods

This study included 122 subjects (age: 74.7 ± 7.7 years) diagnosed with normal cognition (NC, n = 38), mild cognitive impairment (MCI, n = 42), or dementia (n = 42). All subjects underwent comprehensive neuropsychological assessments and cranial Magnetic Resonance Imaging (MRI). Gait parameters were collected using validated wearable devices in both single-task and dual-task (DT). We analyzed the ability of gait variables to predict MCI and dementia, and examined the correlations between specific DT-gait parameters and sub-cognitive functions as well as hippocampal atrophy.

Results

Our results demonstrated that dual-task could significantly improve the ability to predict cognitive impairment based on gait parameters such as gait speed (GS) and stride length (SL). Additionally, we discovered that turn velocity (TV and DT-TV) can be a valuable novel digital marker for predicting MCI and dementia, for identifying MCI (DT-TV: AUC = 0.801, sensitivity 0.738, specificity 0.842), and dementia (DT-TV: AUC = 0.923, sensitivity 0.857, specificity 0.842). The correlation analysis and linear regression analysis revealed a robust association between DT-TV and memory function, as well as the hippocampus atrophy.

Conclusion

This study presents a novel finding that DT-TV could accurately identify varying degrees of cognitive impairment. DT-TV is strongly correlated with memory function and hippocampus shrinkage, suggests that it can accurately reflect changes in cognitive function. Therefore, DT-TV could serve as a novel and effective digital biomarker for discriminating cognitive impairment.

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Background

Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.

Objective

Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.

Methods

A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.

Results

The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.

Conclusions

The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

Classification of Parkinson's disease stages with a two-stage deep neural network

Introduction

Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test.

Methods

The proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the activities included in the test and obtains biomechanical variables that are considered clinically relevant parameters for functional assessment. The second stage is a neural network with three input branches: one with the biomechanical variables, one with the spectrogram image of the sensor signals, and the third with the raw sensor signals.

Results

This stage employs convolutional layers and long short-term memory. The results show a mean accuracy of 99.64% for the stratified k-fold training/validation process and 100% success rate of participants in the test phase.

Discussion

The proposed model is capable of identifying the three initial stages of Parkinson's disease using a 2-min functional test. The test easy instrumentation requirements and short duration make it feasible for use feasible in the clinical context.

Relationship between Cognitive Decline and Daily Life Gait among Elderly People Living in the Community: A Preliminary Report

Introduction

Early detection and intervention are important to prevent dementia. Gait parameters have been recognized as a potentially easy screening tool for mild cognitive impairment (MCI); however, differences in gait parameters between cognitive healthy individuals (CHI) and MCI are small. Daily life gait change may be used to detect cognitive decline earlier. In the present study, we aimed to clarify the relationship between cognitive decline and daily life gait.

Methods

We performed 5-Cog function tests and daily life and laboratory-based gait assessments on 155 community-dwelling elderly people (75.5 ± 5.4 years old). Daily life gait was measured for 6 days using an iPod-touch with an accelerometer. Laboratory-based 10-m gait (fast pace) was measured using an electronic portable walkway.

Results

The subjects consisted of 98 CHI (63.2%) and 57 cognitive decline individuals (CDI; 36.8%). Daily life maximum gait velocity in the CDI group (113.7 [97.0-128.5] cm/s) was significantly slower than that in the CHI group (121.2 [105.8-134.3] cm/s) (p = 0.032). In the laboratory-based gait, the stride length variability in the CDI group (2.6 [1.8-4.1]) was significantly higher than that in the CHI group (1.8 [1.2-2.7]) (p < 0.001). The maximum gait velocity in daily life gait was weakly but significantly correlated with stride length variability in laboratory-based gait (ρ = -0.260, p = 0.001).

Conclusion

We found an association between cognitive decline and slower daily life gait velocity among community-dwelling elderly people.

A novel dual-task paradigm with story recall shows significant differences in the gait kinematics in older adults with cognitive impairment: A cross-sectional study

Objective

Cognitive and motor dysfunctions in older people become more evident while dual-tasking. Several dual-task paradigms have been used to identify older individuals at the risk of developing Alzheimer's disease and dementia. This study evaluated gait kinematic parameters for dual-task (DT) conditions in older adults with mild cognitive impairment (MCI), subjective cognitive decline (SCD), and normal cognition (NC).

Method

This is a cross-sectional, clinical-based study carried out at the Zhongshan Rehabilitation Branch of First Affiliated Hospital of Nanjing Medical University, China.

Participants

We recruited 83 community-dwelling participants and sorted them into MCI (n = 24), SCD (n = 33), and NC (n = 26) groups based on neuropsychological tests. Their mean age was 72.0 (5.55) years, and male-female ratio was 42/41 (p = 0.112). Each participant performed one single-task walk and four DT walks: DT calculation with subtracting serial sevens; DT naming animals; DT story recall; and DT words recall.

Outcome and measures

Kinematic gait parameters of speed, knee peak extension angle, and dual-task cost (DTC) were obtained using the Vicon Nexus motion capture system and calculated by Visual 3D software. A mixed-effect linear regression model was used to analyze the data.

Results

The difference in gait speed under DT story recall and DT calculation was -0.099 m/s and - 0.119 m/s (p = 0.04, p = 0.013) between MCI and SCD, respectively. Knee peak extension angle under DT story recall, words recall, and single task was bigger in the MCI group compared to the NC group, respectively (p = 0.001, p = 0.001, p = 0.004). DTC was higher in the DT story recall test than all other DT conditions (p < 0.001).

Conclusion

Kinematic gait parameters of knee peak extension angle for the DT story recall were found to be sensitive enough to discriminate MCI individuals from NC group. DTC under DT story recall was higher than the other DT conditions.

Gait variability during abrupt slow and fast speed transitions in older adults with mild cognitive impairment

Gait speed modulation, including abruptly decreasing or increasing gait speed, is a challenging task and prerequisite for safe mobility in the community. Older adults with Mild Cognitive Impairment (MCI) exhibit gait deficits under challenging walking conditions which may increase their risk of falls. The purpose of this study was to investigate spatiotemporal variability during slow and fast speed transitions in older adults with and without MCI. Twenty-five older adults with MCI (mean age = 68.56 ± 3.79 years) and 25 cognitively intact controls (mean age = 68.72 ± 4.67 years) participated. Gait performance during gait speed transitions was measured in two walking conditions: 1) a slow to fast speed transition in response to a randomly presented cue, and 2) a fast to slow speed condition in response to a randomly presented cue. Means and variability of spatiotemporal parameters during the transitions were measured and mixed model repeated measures ANOVAs were used to assess interaction and main effects. The older adults with MCI exhibited greater variability of step length (MCI = 13.93 ± 5.38, Control = 11.12 ± 3.15, p = 0.03) and swing time (MCI = 13.35 ± 6.01, Control = 10.43 ± 2.87, p = 0.03) than the controls during the fast to slow speed transitions. No other between-group differences were evident for the gait parameters across the two walking conditions. The findings suggest that older adults with MCI have reduced ability to adapt their gait during transitions from fast to slow walking speeds. This impairment may indicate a decline in automated regular rhythmic gait control and explain in part why this group is at increased risk of falls. Slow speed transition task might be incorporated as a fall risk screening in older adults with MCI.

Assessment Tools to Evaluate Motor Function in People with Dementia: A Systematic Review

Background: In addition to cognitive changes, motor impairments have been observed in patients with dementia and are present early in the disease, even at the preclinical stage. Although it is difficult to assess motor function in this population, it is critical for monitoring disease progression and determining the efficacy of therapeutic interventions. However, the best measurement tools for assessing motor function in dementia patients have yet to be determined. Objective: We aimed to summarize and critically evaluate the measurement tools used to assess motor function indementia. Methods: A systematic review was conducted using the databases CENTRAL, MEDLINE, Embase, and PEDro from their inception to June 2021 to identify all experimental studies conducted in patients with dementia and that included an assessment of motor function. Two reviewers independently screened citations, extracted data, and assessed clinimetric properties. Results: We included 200 studies that assess motor function in dementia patients. Motor function was assessed using a total of 84 different measurement tools. Only nine (12% ) were used in over ten studies. The Timed-Up-and-Go test, 6MWT, Berg Balance Scale, and the Short Physical Performance Battery are all suggested. Conclusion: Currently, a wide variety of measurement instruments are used to assess motor performance in people with dementia, most instruments were not designed for this population and have not been validated for this use. We propose the development of an assessment protocol tailored to the different disease stages. We also recommend that future research continues to develop technological devices that can assist with this task.

Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis

Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.

Gait Kinematic and Kinetic Characteristics of Older Adults With Mild Cognitive Impairment and Subjective Cognitive Decline: A Cross-Sectional Study

Background

Older adults with mild cognitive impairment (MCI) have slower gait speed and poor gait performance under dual-task conditions. However, gait kinematic and kinetic characteristics in older adults with MCI or subjective cognitive decline (SCD) remain unknown. This study was designed to explore the difference in gait kinematics and kinetics during level walking among older people with MCI, SCD, and normal cognition (NC).

Methods

This cross-sectional study recruited 181 participants from July to December 2019; only 82 met the inclusion criteria and consented to participate and only 79 completed gait analysis. Kinematic and kinetic data were obtained using three-dimensional motion capture system during level walking, and joint movements of the lower limbs in the sagittal plane were analyzed by Visual 3D software. Differences in gait kinematics and kinetics among the groups were analyzed using multivariate analysis of covariance (MANCOVA) with Bonferroni post-hoc analysis. After adjusting for multiple comparisons, the significance level was p < 0.002 for MANCOVA and p < 0.0008 for post-hoc analysis.

Results

Twenty-two participants were MCI [mean ± standard deviation (SD) age, 71.23 ± 6.65 years], 33 were SCD (age, 72.73 ± 5.25 years), and 24 were NC (age, 71.96 ± 5.30 years). MANCOVA adjusted for age, gender, body mass index (BMI), gait speed, years of education, diabetes mellitus, and Geriatric Depression Scale (GDS) revealed a significant multivariate effect of group in knee peak extension angle (F = 8.77, p < 0.0001) and knee heel strike angle (F = 8.07, p = 0.001) on the right side. Post-hoc comparisons with Bonferroni correction showed a significant increase of 5.91° in knee peak extension angle (p < 0.0001) and a noticeable decrease of 6.21°in knee heel strike angle (p = 0.001) in MCI compared with NC on the right side. However, no significant intergroup difference was found in gait kinetics, including dorsiflexion, plantar flexion, knee flexion, knee extension, hip flexion, and hip extension(p > 0.002).

Conclusion

An increase of right knee peak extension angle and a decrease of right knee heel strike angle during level walking were found among older adults with MCI compared to those with NC.