CCCDTD5 recommendations on early non cognitive markers of dementia: A Canadian consensus

Balance, Strength, and Risk of Dementia: Findings From the Health and Retirement Study and the English Longitudinal Study of Ageing

BACKGROUND

Slow gait speed has been consistently associated with an increased risk of dementia. This study examined whether measures of balance and lower limb strength are similarly related to the risk of developing dementia.

METHODS

Participants from the Health and Retirement Study (HRS, N = 5 658, mean age = 73.23, standard deviation [SD] = 6.22) and the English Longitudinal Study of Ageing (ELSA, N = 3667, mean age = 69.90, SD = 7.02) completed measures of gait speed, semi-tandem balance, chair stand (ELSA only), and cognitive status at baseline. Cognitive status was assessed over up to 15 years.

RESULTS

Baseline slower gait speed (hazard ratio [HR]HRS = 1.52, 95% confidence interval [CI] = 1.32-1.75, p < .001; HRELSA = 1.73, 95% CI = 1.37-2.18, p < .001); and balance impairment (HRHRS = 1.58, 95% CI = 1.26-1.96, p < .001; HRELSA = 1.97, 95% CI = 1.24-3.14, p < .01) were related to a higher risk of incident dementia, adjusting for demographic factors. The combination of slower gait and impaired balance was associated with a two-to-three times higher risk of dementia in HRS and ELSA. Worse performance on the chair stand at baseline was associated with a higher risk of dementia in ELSA (HR = 1.56, 95% CI = 1.23-1.99, p < .001). All performance measures remained significant when entered simultaneously and accounted for obesity, diabetes, blood pressure, physical activity, smoking, and depressive symptoms. There was little evidence that age, sex, or APOE ε4 moderated the association.

CONCLUSIONS

Similar to gait speed, measures of balance and strength are associated with a higher risk of incident dementia. The findings have implications for clinical practice, given that these routinely used geriatric assessment tools are similarly related to dementia risk.

Neuropsychiatric symptoms as a prodromal factor in Alzheimer's type neurodegenerative disease: A scoping review

Objective: Identifying neuropsychiatric symptoms (NPS) can aid in the early detection of Alzheimer's disease (AD); however, there is still a need for a greater consensus. This review aims to delineate the predominant NPS, compile a comprehensive list of the most commonly employed NPS assessment tools, and corroborate the principal findings regarding the link between NPS and neuropsychological assessment and neurobiological substrates. Methods: To conduct this scoping review, we followed the Preferred Reporting Items for Systematic Reviews guidelines and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched for relevant articles published between 2017 and 2023 in MEDLINE, PsycINFO, PubMed, Web of Science, and Cochrane Library. Results: Of the 61 eligible articles, depression, anxiety, and apathy were the main NPSs. The Neuropsychiatric Inventory Questionnaire and Neuropsychiatric Inventory were the primary assessment tools used to evaluate NPS. Correlations between NPS severity and neurobiological markers were considered clinically significant. Furthermore, clinical procedures prioritized the use of global cognitive screening tools, assessments of executive functions, and functionality evaluations. Conclusion: Standardization of procedures is necessary because of the diversity of methods. The data show that NPS can predict the etiology, severity, form, and type of disease progression, serving as a precursor sign of AD. The results of the most common cognitive screening tools and NPS instruments provided an interesting overview of future clinical approaches.

Progress on early diagnosing Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions. The disease follows a continuum, starting with preclinical stages, progressing to mild cognitive and behavioral impairment, ultimately leading to dementia. Early detection of AD is crucial for better diagnosis and more effective treatment. However, the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive, and mostly are still not able to detect early disease state. Consequently, there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD. Various non-cognitive manifestations, including behavioral abnormalities, sleep disturbances, sensory dysfunctions, and physical changes, have been observed in the preclinical AD stage before occurrence of notable cognitive decline. Recent research advances have identified several biofluid biomarkers as early indicators of AD. This review focuses on these non-cognitive changes and newly discovered biomarkers in AD, specifically addressing the preclinical stages of the disease. Furthermore, it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.

Gait Parameters Change can be an Early Marker of Cognitive Impairment

BACKGROUND AND OBJECTIVE

Gait impairment leads to increased dependence, morbidity, institutionalization, and mortality in older people. We intended to assess gait parameters with the continuum of cognitive impairment and observe variation with the severity of cognitive impairment.

MATERIALS AND METHODS

This cross-sectional, observational study was conducted at the memory clinic of a tertiary care center. One hundred and twelve subjects were recruited, and cognition was assessed by the Clinical Dementia Rating scale. Usual gait was assessed by a 6-m walk test, and the dynamic gait was assessed using Biodex Gait Trainer™. Apart from crude analysis, adjusted linear regression was used to find the association of spatiotemporal gait parameters with cognitive decline.

RESULTS

Subjects were divided into subjective cognitive decline (SCD; n = 38), mild cognitive impairment (MCI; n = 40), and major neurocognitive disorder (MNCD; n = 34) groups. History of falls (23.7% vs. 30.0% vs. 67.7%, P < 0.001) and impaired activities of daily living (ADLs) (5.3% vs. 15.0% vs. 100%, P < 0.001) were significantly higher with cognitive decline. Age- and gender-adjusted regression analysis revealed that usual gait speed (0.8 vs. 0.6 vs. 0.5, P < 0.001) (m/s), total time (3.9 vs. 2.9 vs. 2.6, P = 0.022) (min), total distance (65.6 vs. 55.8 vs. 46.6, P = 0.025) (m), step cycle time (0.6 vs. 0.8 vs. 0.8, P = 0.020) (cycles/s), and step lengths were significant.

CONCLUSION

Gait speed and other parameters worsened with increasing cognitive impairment. Changes in gait parameters might be a useful marker of declining cognition, though a long-term follow-up study is required to establish this association. Early intervention could be beneficial in preserving autonomy in patients with cognitive impairment.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

The Australian Traumatic Brain Injury Initiative: Statement of Working Principles and Rapid Review of Methods to Define Data Dictionaries for Neurological Conditions

The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to develop a health informatics approach to collect data predictive of outcomes for persons with moderate-severe TBI across Australia. Central to this approach is a data dictionary; however, no systematic reviews of methods to define and develop data dictionaries exist to-date. This rapid systematic review aimed to identify and characterize methods for designing data dictionaries to collect outcomes or variables in persons with neurological conditions. Database searches were conducted from inception through October 2021. Records were screened in two stages against set criteria to identify methods to define data dictionaries for neurological conditions (International Classification of Diseases, 11th Revision: 08, 22, and 23). Standardized data were extracted. Processes were checked at each stage by independent review of a random 25% of records. Consensus was reached through discussion where necessary. Thirty-nine initiatives were identified across 29 neurological conditions. No single established or recommended method for defining a data dictionary was identified. Nine initiatives conducted systematic reviews to collate information before implementing a consensus process. Thirty-seven initiatives consulted with end-users. Methods of consultation were "roundtable" discussion (n = 30); with facilitation (n = 16); that was iterative (n = 27); and frequently conducted in-person (n = 27). Researcher stakeholders were involved in all initiatives and clinicians in 25. Importantly, only six initiatives involved persons with lived experience of TBI and four involved carers. Methods for defining data dictionaries were variable and reporting is sparse. Our findings are instructive for AUS-TBI and can be used to further development of methods for defining data dictionaries.

Postural Control Characteristics in Alzheimer's Disease, Dementia With Lewy Bodies, and Vascular Dementia

BACKGROUND

Dementia often results in postural control impairment, which could signify central nervous system dysfunction. However, no studies have compared postural control characteristics among various types of dementia. This study aimed to compare static postural control in patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and vascular dementia (VaD).

METHODS

Cross-sectional relationship between the clinical diagnoses (AD, DLB, VaD, or normal cognition [NC]) of outpatients at a memory clinic and their upright postural control characteristics were examined. In the postural control test, participants were instructed to maintain a static upright standing on a stabilometer for 60 seconds under the eyes-open and eyes-closed conditions. Forty postural control parameters, including distance, position, and velocity in the anterior-posterior and medio-lateral directions, derived from the trajectory of the center of mass sway, were calculated. The characteristics of each type of dementia were compared to those of NC, and the differences among the 3 types of dementia were evaluated using linear regression models.

RESULTS

The study included 1 789 participants (1 206 with AD, 111 with DLB, 49 with VaD, and 423 with NC). Patients with AD exhibited distinct postural control characteristics, particularly in some distance and velocity parameters, only in the eyes-closed condition. Those with DLB exhibited features in the mean position in the anterior-posterior direction. In patients with VaD, significant differences were observed in most parameters, except the power spectrum.

CONCLUSIONS

Patients with AD, DLB, and VaD display disease-specific postural control characteristics when compared to cognitively normal individuals.

Link among apolipoprotein E E4, gait, and cognition in neurodegenerative diseases: ONDRI study

INTRODUCTION

Apolipoprotein E E4 allele (APOE E4) and slow gait are independently associated with cognitive impairment and dementia. However, it is unknown whether their coexistence is associated with poorer cognitive performance and its underlying mechanism in neurodegenerative diseases.

METHODS

Gait speed, APOE E4, cognition, and neuroimaging were assessed in 480 older adults with neurodegeneration. Participants were grouped by APOE E4 presence and slow gait. Mediation analyses were conducted to determine if brain structures could explain the link between these factors and cognitive performance.

RESULTS

APOE E4 carriers with slow gait had the lowest global cognitive performance and smaller gray matter volumes compared to non-APOE E4 carriers with normal gait. Coexistence of APOE E4 and slow gait best predicted global and domain-specific poorer cognitive performances, mediated by smaller gray matter volume.

DISCUSSION

Gait slowness in APOE E4 carriers with neurodegenerative diseases may indicate extensive gray matter changes associated with poor cognition.

HIGHLIGHTS

APOE E4 and slow gait are risk factors for cognitive decline in neurodegenerative diseases. Slow gait and smaller gray matter volumes are associated, independently of APOE E4. Worse cognition in APOE E4 carriers with slow gait is explained by smaller GM volume. Gait slowness in APOE E4 carriers indicates poorer cognition-related brain changes.

PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use

PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), assessing glucose metabolism, provides a measure of neurodegeneration and allows a precise differential diagnosis among the most common neurodegenerative diseases, such as Alzheimer's disease, frontotemporal dementia or dementia with Lewy bodies. PET tracers specific for the pathological deposits characteristic of different neurodegenerative processes, namely amyloid and tau deposits typical of Alzheimer's Disease, allow the visualization of these aggregates in vivo. [18F]FDG and amyloid PET imaging have reached a high level of clinical validity and are since 2022 investigations that can be offered to patients in standard clinical care in most of Canada.This article will briefly review and summarize the current knowledge on these diagnostic tools, their integration into diagnostic algorithms as well as perspectives for future developments.

Adjunct Methods for Alzheimer's Disease Detection: A Review of Auditory Evoked Potentials

The auditory afferent pathway as a clinical marker of Alzheimer's disease (AD) has sparked interest in investigating the relationship between age-related hearing loss (ARHL) and AD. Given the earlier onset of ARHL compared to cognitive impairment caused by AD, there is a growing emphasis on early diagnosis and intervention to postpone or prevent the progression from ARHL to AD. In this context, auditory evoked potentials (AEPs) have emerged as a widely used objective auditory electrophysiological technique for both the clinical diagnosis and animal experimentation in ARHL due to their non-invasive and repeatable nature. This review focuses on the application of AEPs in AD detection and the auditory nerve system corresponding to different latencies of AEPs. Our objective was to establish AEPs as a systematic and non-invasive adjunct method for enhancing the diagnostic accuracy of AD. The success of AEPs in the early detection and prediction of AD in research settings underscores the need for further clinical application and study.

Behavioral marker-based predictive modeling of functional status for older adults with subjective cognitive decline and mild cognitive impairment: Study protocol

Objective

This study describes a research protocol for a behavioral marker-based predictive model that examines the functional status of older adults with subjective cognitive decline and mild cognitive impairment.

Methods

A total of 130 older adults aged ≥65 years with subjective cognitive decline or mild cognitive impairment will be recruited from the Dementia Relief Centers or the Community Service Centers. Data on behavioral and psychosocial markers (e.g. physical activity, mobility, sleep/wake patterns, social interaction, and mild behavioral impairment) will be collected using passive wearable actigraphy, in-person questionnaires, and smartphone-based ecological momentary assessments. Two follow-up assessments will be performed at 12 and 24 months after baseline. Mixed-effect machine learning models: MErf, MEgbm, MEmod, and MEctree, and standard machine learning models without random effects [random forest, gradient boosting machine] will be employed in our analyses to predict functional status over time.

Results

The results of this study will be fundamental for developing tailored digital interventions that apply deep learning techniques to behavioral data to predict, identify, and aid in the management of functional decline in older adults with subjective cognitive decline and mild cognitive impairment. These older adults are considered the optimal target population for preventive interventions and will benefit from such tailored strategies.

Conclusions

Our study will contribute to the development of self-care interventions that utilize behavioral data and machine learning techniques to provide automated analyses of the functional decline of older adults who are at risk for dementia.

Using dual-task gait to recognize Alzheimer's disease and mild cognitive impairment: a cross-sectional study

Background

Gait is a potential diagnostic tool for detecting mild cognitive impairment (MCI) and Alzheimer's disease (AD). Nevertheless, little attention has been paid to arm movements during walking, and there is currently no consensus on gait asymmetry. Therefore, in this study, we aimed to determine whether arm motion and gait asymmetry could be utilized for identifying MCI and AD.

Methods

In total, 102 middle-aged and elderly individuals were included in the final analysis and were assigned to the following three groups: AD (n = 27), MCI (n = 35), and a normal control group (n = 40). Gait and cognitive assessments were conducted for all participants. Gait detection included a single-task gait with free-speed walking and a dual-task gait with adding a cognitive task of successive minus seven to walking. Original gait parameters were collected using a wearable device featuring the MATRIX system 2.0. Gait parameters were shortened to several main gait domains through factor analysis using principal component extraction with varimax rotation. Subsequently, the extracted gait domains were used to differentiate the three groups, and the area under the receiver operating characteristic curve was calculated.

Results

Factor analysis of single-task gait identified five independent gait domains: rhythm symmetry, rhythm, pace asymmetry, arm motion, and variability. Factor analysis of the dual-task gait identified four gait domains: rhythm, variability, symmetry, and arm motion. During single-task walking, pace asymmetry was negatively correlated with MoCA scores and could distinguish between the AD group and the other two groups. Arm motion was not associated with MoCA scores, and did not exhibit adequate discrimination in either task.

Conclusion

Currently, there is no reliable evidence suggesting that arm motion can be used to recognize AD or MCI. Gait asymmetry can serve as a potential gait marker for the auxiliary diagnosis of AD but not for MCI.

Dementia and comorbidities in primary care: a scoping review

BACKGROUND

People with dementia (PwD) are known to have more chronic conditions compared to those without dementia, which can impact the clinical presentation of dementia, complicate clinical management and reduce overall quality of life. While primary care providers (PCPs) are integral to dementia care, it is currently unclear how PCPs adapt dementia care practices to account for comorbidities. This scoping review maps recent literature that describes the role for PCPs in the prevention, detection/diagnosis and management of dementia in the context of comorbidities, identifies critical knowledge gaps and proposes potential avenues for future research.

METHODS

We searched for peer-reviewed literature published between 2017-2022 in MEDLINE, Cochrane Library, and Scopus using key terms related to dementia, primary care, and comorbidity. The literature was screened for relevance by title-abstract screening and subsequent full-text screening. The prioritized papers were categorized as either 'Risk Assessment and Prevention', 'Screening, Detection, and Diagnosis' or 'Management' and were further labelled as either 'Tools and Technologies', 'Recommendations for Clinical Practice' or 'Programs and Initiatives'.

RESULTS

We identified 1,058 unique records in our search and respectively excluded 800 and 230 publications during title-abstract and full-text screening. Twenty-eight articles were included in our review, where ~ 50% describe the development and testing of tools and technologies that use pre-existing conditions to assess dementia risk. Only one publication provides official dementia screening guidelines for PCPs in people with pre-existing conditions. About 30% of the articles discuss managing the care of PwD, where most were anchored around models of multidisciplinary care and mitigating potentially inappropriate prescribing.

CONCLUSION

To our knowledge, this is the first scoping review that examines the role for PCPs in the prevention, detection/diagnosis and management of dementia in the context of comorbidities. Given our findings, we recommend that future studies: 1) further validate tools for risk assessment, timely detection and diagnosis that incorporate other health conditions; 2) provide additional guidance into how comorbidities could impact dementia care (including prescribing medication) in primary care settings; 3) incorporate comorbidities into primary care quality indicators for dementia; and 4) explore how to best incorporate dementia and comorbidities into models/frameworks of holistic, person-centred care.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Longitudinal Associations Between Mild Behavioral Impairment, Sleep Disturbance, and Progression to Dementia

Background

Clinical guidelines recommend incorporating non-cognitive markers like mild behavioral impairment (MBI) and sleep disturbance (SD) into dementia screening to improve detection.

Objective

We investigated the longitudinal associations between MBI, SD, and incident dementia.

Methods

Participant data were from the National Alzheimer's Coordinating Center in the United States. MBI was derived from the Neuropsychiatric Inventory Questionnaire (NPI-Q) using a published algorithm. SD was determined using the NPI-Q nighttime behaviors item. Cox proportional hazard regressions with time-dependant variables for MBI, SD, and cognitive diagnosis were used to model associations between baseline 1) MBI and incident SD (n = 11,277); 2) SD and incident MBI (n = 10,535); 3) MBI with concurrent SD and incident dementia (n = 13,544); and 4) MBI without concurrent SD and incident dementia (n = 11,921). Models were adjusted for first-visit age, sex, education, cognitive diagnosis, race, and for multiple comparisons using the Benjamini-Hochberg method.

Results

The rate of developing SD was 3.1-fold higher in older adults with MBI at baseline compared to those without MBI (95% CI: 2.8-3.3). The rate of developing MBI was 1.5-fold higher in older adults with baseline SD than those without SD (95% CI: 1.3-1.8). The rate of developing dementia was 2.2-fold greater in older adults with both MBI and SD, as opposed to SD alone (95% CI:1.9-2.6).

Conclusions

There is a bidirectional relationship between MBI and SD. Older adults with SD develop dementia at higher rates when co-occurring with MBI. Future studies should explore the mechanisms underlying these relationships, and dementia screening may be improved by assessing for both MBI and SD.

Investigating the associations between upper limb motor function and cognitive impairment: a scoping review

Upper limb motor function is a potential new biomarker of cognitive impairment and may aid discrimination from healthy ageing. However, it remains unclear which assessments to use. This study aimed to explore what methods have been used and to describe associations between upper limb function and cognitive impairment. A scoping review was conducted using PubMed, CINAHL and Web of Science. A systematic search was undertaken, including synonyms for key concepts 'upper limb', 'motor function' and 'cognitive impairment'. Selection criteria included tests of upper limb motor function and impaired cognition in adults. Analysis was by narrative synthesis. Sixty papers published between 1998 and 2022, comprising 41,800 participants, were included. The most common assessment tasks were finger tapping, Purdue Pegboard Test and functional tasks such as writing. Protocols were diverse in terms of equipment used and recording duration. Most participants were recruited from clinical settings. Alzheimer's Disease was the most common cause of cognitive impairment. Results were mixed but, generally, slower speed, more errors, and greater variability in upper limb movement variables was associated with cognitive impairment. This review maps the upper limb motor function assessments used and summarises the available evidence on how these associate with cognitive impairment. It identifies research gaps and may help guide protocols for future research. There is potential for upper limb motor function to be used in assessments of cognitive impairment.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning

BACKGROUND

The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.

METHODS

The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.

RESULTS

Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.

CONCLUSIONS

Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Persistence of neuropsychiatric symptoms and dementia prognostication: A comparison of three operational case definitions of mild behavioral impairment

INTRODUCTION

We compared three operational case definitions of mild behavioral impairment (MBI) in the context of MBI prevalence estimates and dementia risk modeling.

METHODS

Participants were dementia-free older adults (n = 13701) from the National Alzheimer's Coordinating Center. Operational case definitions of MBI were generated based on neuropsychiatric symptoms at one (OV), two-consecutive (TCV), or more than two-thirds (TTV) of dementia-free study visits. Definitions were compared in prevalence and in Cox regressions using MBI to predict incident dementia.

RESULTS

OV MBI was the most prevalent (54.4%), followed by TCV (32.3%) and TTV (26.7%) MBI. However, OV MBI had the lowest rate of incident dementia (hazard ratio [HR] = 2.54, 95% confidence interval [CI]: 2.33-2.78) and generated poorer model metrics than TCV MBI (HR = 4.06, 95% CI: 3.74-4.40) and TTV MBI (HR = 5.77, 95% CI: 5.32-6.26).

DISCUSSION

Case ascertainment with longer timeframe MBI operational case definitions may more accurately define groups at risk of dementia in datasets lacking tools designed to detect MBI.Highlights: Mild behavioral impairment (MBI) can identify older adults at risk of dementia.Neuropsychiatric symptom (NPS) assessment tools can be proxy measures for MBI.Hazard for dementia was highest for MBI defined by NPS presence at more than two-thirds of visits.

Intelligence, cognition, and major neurocognitive disorders: From constructs to measures

The study of intelligence's role in development of major neurocognitive disorders (MND) is influenced by the approaches used to conceptualize and measure these constructs. In the field of cognitive impairment, the use of single 'intelligence' tests is a common approach to estimate intelligence. Despite being a practical compromise between feasibility and constructs, variance of these tests is only partially explained by general intelligence, and some tools (e.g., lexical tasks for premorbid intelligence) presented inherent limitations. Alternatively, factorial models allow an actual measure of intelligence as a latent factor superintending all mental abilities. Royall and colleagues used structural equation modeling to decompose the Spearman's general intelligence factor g in δ (shared variance across cognitive and functional measures) and g' (shared variance across cognitive measures only). Authors defined δ as the 'cognitive correlates of functional status', and thus a 'phenotype for all cause dementia'. Compared to g', δ explained a little rate of cognitive measures' variance, but it demonstrated a higher accuracy in dementia case-finding. From the methodological perspective, given g 'indifference' to its indicators, further studies are needed to identify the minimal set of tools necessary to extract g, and to test also non-cognitive variables as measures of δ. From the clinical perspective, general intelligence seems to influence MND presence and severity more than domain specific cognitive abilities. Giving δ 'blindness' to etiology, its association with biomarkers and contribution to differential diagnosis might be limited. Classical neuropsychological approaches based on patterns of performances at cognitive tests remained fundamental for differential diagnosis.

A new one-stop interdisciplinary cognitive clinic model tackles rural health inequality and halves the time to diagnosis: Benchmarked against a national dementia registry

OBJECTIVES

Unequal access to cognitive assessments is a major barrier to timely diagnosis, especially for those living in rural or remote areas. 'One-stop' cognitive clinic models are a proposed solution, but few such clinics exist. We evaluate the implementation of a new one-stop State-wide clinic model in Tasmania, Australia, where 27% of people live in rural/remote areas.

METHODS

A novel single-visit protocol has been developed, comprising interdisciplinary medical and cognitive assessments, research participation, consensus diagnosis and management plan. A cross-sectional evaluation was undertaken using the RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework and results benchmarked against the national Australian Dementia Network Registry.

RESULTS

Over the first 52 consecutive weekly clinics: Reach: 130 adults were assessed (mean age [SD] 70.12 years [10.31]; 59.2% female) with 40 (36.8%) from rural/remote areas.

EFFECTIVENESS

98.5% (128/130) received a same-day diagnosis: 30.1% (n = 40) Subjective Cognitive Decline, 35.4% (46) Mild Cognitive Impairment, 33.1% (43) dementia and one case inconclusive. Adoption: 22.9% (156) of General Practitioners referred patients.

IMPLEMENTATION

Nearly all 'ideal' diagnostic clinical practices were met and >90% of surveyed patients reported 'good/very good' clinic experience. The wait from referral to diagnosis was 2 months shorter than other national Registry clinics (78 vs. 133 days).

CONCLUSIONS

This 'one-stop' model provides an interdisciplinary consensus cognitive diagnosis quickly and is well accepted; this may reduce health inequities especially for people living in rural/remote areas. This cognitive clinic model may be of relevance to other centres worldwide and also provides a rich data source for research studies.

Floor Maze Test is capable of differentiating spatial navigation between frail and pre-frail institutionalized older persons

Investigation of the association between physical frailty and cognitive performance through spatial navigation is important to enable the identification of individuals with cognitive impairment and physical comorbidity.

Objective

To analyze the association between spatial navigation and frailty in frail and pre-frail institutionalized older adults.

Methods

Forty older people of both sexes, aged 60 years or over, residing in four Brazilian Long-Term Care Facilities (LTCFs) participated in this study. The following tests were applied: Mini-Mental State Examination (MMSE), 2.44m Timed Up and Go, Floor Maze Test (FMT), and Fried's frailty criteria. For data analysis, the Mann-Whitney and independent t-tests were used to compare the groups (frail x pre-frail), principal component analysis was used to explore the main variables related to the data variance, and binary logistic regression to estimate associations.

Results

There was a significant difference in performance in the FMT immediate maze time (IMT) (p=0.02) and in the delayed maze time (DMT) (p=0.009) between the pre-frail and frail older adults. An association between FMT DMT performance and frailty was found, showing that older people with shorter times on the DMT (better performance) had approximately four times the chance of not being frail (odds ratio - OR=4.219, 95% confidence interval - 95%CI 1.084-16.426, p=0.038).

Conclusion

Frailty is associated with impaired spatial navigation ability in institutionalized older adults, regardless of gait speed performance.

The association between cognitive impairment, gait speed, and Walk ratio

Background

Gait speed has been found to be associated with cognitive function. However, gait speed is an unspecific measure that may not be informative about gait patterns. The Walk ratio (step length divided by step frequency) can be measured without specialized equipment, and has been suggested as an indicator of central gait control. However, the association with cognitive function is unknown.

Research question

Is there a relationship between Walk ratio and cognitive function, and gait speed and cognitive function?

Methods

This was a systematic literature review of studies where spatiotemporal gait parameters was reported in populations with cognitive impairment. The search was performed through PubMed, PEDro, AMED, Cochrane, Embase, MEDLINE, and PsycINFO. The studies had to contain either the Walk ratio, or report average step length and average step frequency. In the latter case, the average step length was divided by the average step frequency. The studies also had to report gait speed and the minimal mental state examination (MMSE). Studies testing patients on treadmills or that did not state the exclusion of patients with neurologic or orthopedic diseases, possible affecting gait ability, were excluded.

Results

A total of 24 studies were included, consisting of 909 patients with cognitive impairment and 4,108 healthy controls. The patient group had a lower Walk ratio (mean difference 0.07, p ≤ 0.001) and gait speed (mean difference 0.26, p ≤ 0.001) than the healthy controls. Using linear regression models, we found an association between the MMSE and the Walk ratio (R² = 0.29, p < 0.001) and gait speed (R² = 0.41, p < 0.001) in separate, unadjusted models. In a final model with Walk ratio, gait speed and age, Walk ratio was not significantly associated with MMSE, while gait speed was.

Significance

Our results suggest that preferred gait speed may be preferable to the Walk ratio when assessing older adults with cognitive impairment.

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning

Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a "digital-twin" of STRIDE, consisting of a human walking simulation model and a micro-Doppler radar simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, shoulder, etc.). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.

Decision making under uncertainty in the diagnosis and management of Alzheimer's Disease in primary care: A study protocol applying concepts from neuroeconomics

Background

The current management of patients with Dementia, primarily with Alzheimer's Disease (AD) is rapidly evolving. However, limited information is available about the current gaps and decision-making in primary care.

Objectives

To evaluate factors associated with gaps, risk preferences regarding diagnostic and therapeutic choices in the management of patients with AD by primary care physicians (PCP) from across Canada.

Methods

We propose a non-interventional, cross-sectional pilot study involving 120 primary care physicians referred from the College of Family Physicians of Canada to assess diagnostic and therapeutic decisions in the management of ten simulated AD-related case-scenarios commonly encountered in clinical practice. We initially describe the current landscape and gaps regarding diagnostic and therapeutic challenges in the management of patients with AD in primary care. Then, we provide concepts from behavioral economics and neuroeconomics applied to medical decision-making. Specifically, we include standardized tests to measure risk aversion, physicians' reactions to uncertainty, and questions related to risk preferences in different domains. Finally, we summarize the protocol to be implemented to address our goals. The primary study outcome is the proportion of participants that elect to defer initial investigations to the specialist and the associated factors. Secondary outcomes include the proportion of PCP willing to order cerebral spinal fluid studies, PET scans, or initiate treatment according to the simulated case-scenarios. The study will be conducted in English and French.

Conclusions

The study findings will contribute a better understanding of relevant factors associated with diagnostic and therapeutic decisions of PCP in the management of AD, identifying participant's preferences and evaluating the role of behavioral aspects such tolerance to uncertainty, aversion to ambiguity, and therapeutic inertia.

Dual-Task Gait as a Predictive Tool for Cognitive Impairment in Older Adults: A Systematic Review

The use of the dual-task model as a cognitive-motor interface has been extensively investigated in cross-sectional studies as a training task in cognitive impairment. However, few existing longitudinal studies prove the usefulness of this tool as a clinical marker of cognitive impairment in older people. What is the evidence in prospective studies about dual-task gait as a predictor of cognitive impairment in older adults? This study aims to review and discuss the current state of knowledge in prospective studies on the use of dual-task gait as a predictive tool for cognitive impairment in older adults. The methodology used was a systematic review, according to the PRISMA criteria for the search, summarize and report. A search in 3 databases (Pubmed, Web of Science, and Scopus) was carried out until April 2021. The search terms used were: "(gait OR walking) AND (cognitive decline) AND (dual-task) AND (follow-up OR longitudinal OR long-term OR prospective OR cohort OR predict)." We included prospective research articles with older people with cognitive evaluation at the beginning and the end of the follow-up and dual-task gait paradigm as initial evaluation associated with the presentation of cognitive impairment prediction using any dual-task gait parameters. After exclusion criteria, 12 studies were reviewed. The results indicate that eight studies consider dual-task gait parameters a useful cognitive-motor tool, finding that some of the evaluated parameters of dual-task gait significantly correlate with cognitive impairment over time. The most promising DT parameters associated with cognitive impairment prediction seem to be gait speed, speed cost, DT time, numbers of words during DT, among others. In sum, this study reviews the variety of dual-task gait parameters and their relevance as a simple tool for early cognitive impairment screening, opening a diagnostic window for the screening of cognitive impairment in older people.

The Performance of Dual-Task Tests Can Be a Combined Neuro-Psychological and Motor Marker of Mild Cognitive Impairment, Depression and Dementia in Geriatric Patients-A Cross-Sectional Study

The study aims to assess the performance of dual-task tests in the geriatric population and their association with the cognitive status of the patients.

METHODS

Patients admitted to the Department of Geriatrics, Hospital of the Ministry of Interior and Administration on Bialystok, Poland, in 2019 and 2020 were enrolled in the study. Data on the patients' clinical, functional, and cognitive status were collected based on the comprehensive geriatric assessment. Dual-task tests included Timed Up and Go (TUG) test while counting backward (CB7), enumerating animals (EA), and holding a cup (TUG M).

RESULTS

250 patients were included in the study, with a median age of 81.5 years (IQR 76-86) and most above 75 years of age (80.8%). Only 29 (11.6%) of study participants had no cognitive or mood disorders. Depression was diagnosed in 30.4%, MCI in 12%, and dementia in 38.4% of cases with median Mini-Mental Score Evaluation (MMSE) 17 (12-20) points. Dual-task TUG CB7 results did not differ between cognitive conditions of patients. TUG EA differed between healthy controls and other cognitive groups and TUG between healthy controls and depression and dementia, but not mild cognitive impairment (MCI). The performance of all dual-task tests differed in patients with and without dementia. Ability to finish TUG CB7 was low even in the group without dementia. There were statistically significant differences in median scores of MMSE and Clock Drawing Test (CDT) between patients who were able or not to finish single and dual-task gait tests.

CONCLUSION

Dual-task test results and the performance of these tasks can differentiate patients with depression, MCI and dementia compared to healthy controls in the geriatric population.

Gait variability across neurodegenerative and cognitive disorders: Results from the Canadian Consortium of Neurodegeneration in Aging (CCNA) and the Gait and Brain Study

Introduction

Gait impairment is common in neurodegenerative disorders. Specifically, gait variability—the stride‐to‐stride fluctuations in distance and time—has been associated with neurodegeneration and cognitive impairment. However, quantitative comparisons of gait impairments across the cognitive spectrum of dementias have not been systematically investigated.

Methods

Older adults (N = 500) with subjective cognitive impairment, Parkinson disease (PD), mild cognitive impairment (MCI), PD‐MCI, Alzheimer's disease (AD), PD‐dementia, Lewy body dementia, and frontotemporal dementia, as well cognitive normal controls, who were assessed for their gait and cognitive performance.

Results

Factor analyses grouped 11 quantitative gait parameters and identified four independent gait domains: rhythm, pace, variability, and postural control, for group comparisons and classification analysis. Among these domains, only high gait variability was associated with lower cognitive performance and accurately discriminated AD from other neurodegenerative and cognitive conditions.

Discussion

Our findings indicate that high gait variability is a marker of cognitive‐cortical dysfunction, which can help to identify Alzheimer's disease dementia.