Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior

Digital Clock Drawing as an Alzheimer's Disease Susceptibility Biomarker: Associations with Genetic Risk Score and APOE in Older Adults

BACKGROUND

Alzheimer's disease (AD) is the leading cause of dementia in older adults, but most people are not diagnosed until significant neuronal loss has likely occurred along with a decline in cognition. Non-invasive and cost-effective digital biomarkers for AD have the potential to improve early detection.

OBJECTIVE

We examined the validity of DCTclockTM (a digitized clock drawing task) as an AD susceptibility biomarker.

DESIGN

We used two primary independent variables, Apolipoprotein E (APOE) ε4 allele carrier status and polygenic risk score (PRS). We examined APOE and PRS associations with DCTclockTM composite scores as dependent measures.

SETTING

We used existing data from the Framingham Heart Study (FHS), a community-based study with the largest dataset of digital clock drawing data to date.

PARTICIPANTS

The sample consisted of 2,398 older adults ages 60-94 with DCTclockTM data (mean age of 72.3, 55% female and 92% White).

MEASUREMENTS

PRS was calculated using 38 variants identified in a recent large genome-wide association study (GWAS) and meta-analysis of late-onset AD (LOAD).

RESULTS

Results showed that DCTclockTM performance decreased with advancing age, lower education, and the presence of one or more copies of APOE ε4. Lower DCTclockTM Total Score as well as lower composite scores for Information Processing Speed (both command and copy conditions) and Drawing Efficiency (command condition) were significantly associated with higher PRS levels and more copies of APOE ε4. APOE and PRS associations displayed similar effect sizes in both men and women.

CONCLUSIONS

Our results indicate that higher AD genetic risk is associated with poorer DCTclockTM performance in older adults without dementia. This is the first study to demonstrate significant differences in clock drawing performance on the basis of APOE status or PRS.

Doll therapy for improving behavior, psychology and cognition among older nursing home residents with dementia: A systematic review and meta-analysis

OBJECTIVE

To explore the effectiveness of doll therapy (DT) on behavior, psychology and cognition among older nursing home residents with dementia.

METHODS

A systematic review and meta-analysis was conducted. Subgroup analyses were performed to determine whether the intervention characteristics influenced effect sizes.

RESULTS

Ten studies met the inclusion criteria and were selected for qualitative and quantitative synthesis. The overall methodological quality was relatively high. DT significantly improved all behaviors [SMD=-0.42, P=0.01], including agitation [SMD=-0.94, P<0.001], apathy, irritability and wandering, and psychological states (i.e., pleasure, anxiety and depression). However, there was no significant difference in the improvement of cognition. Subgroup analyses revealed that the DT process employing empathy dolls and coordinating with caregivers was more beneficial for improving all behaviors (P=0.01; P=0.02).

CONCLUSION

DT significantly reduced behavioral and psychological disturbances among older nursing home residents with dementia. Specifically, administering empathy dolls and coordinating with caregivers may be the most appropriate and effective option.

Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons

Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.

Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults

Hand drawing involves multiple neural systems for planning and precise control of sequential movements, making it a valuable cognitive test for older adults. However, conventional visual assessment of drawings may not capture intricate nuances that could help track cognitive states. To address this issue, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3,111 participants in three aging cohorts, explained 23.3% of the variance in global cognitive scores, a comprehensive hour-long cognitive battery. The model’s performance, which was 1.92 times more accurate than conventional visual assessment, significantly improved the detection of cognitive decline. The improvement in accuracy was due to capturing additional drawing features that we found to be associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that hand-drawn images can provide rich cognitive information, enabling rapid assessment of cognitive decline and suggesting potential clinical implications in dementia.

The Boston Process Approach and Digital Neuropsychological Assessment: Past Research and Future Directions

Neuropsychological assessment using the Boston Process Approach (BPA) suggests that an analysis of the strategy or the process by which tasks and neuropsychological tests are completed, and the errors made during test completion convey much information regarding underlying brain and cognition and are as important as overall summary scores. Research over the last several decades employing an analysis of process and errors has been able to dissociate between dementia patients diagnosed with Alzheimer's disease, vascular dementia associated with MRI-determined white matter alterations, and Parkinson's disease; and between mild cognitive impairment subtypes. Nonetheless, BPA methods can be labor intensive to deploy. However, the recent availability of digital platforms for neuropsychological test administration and scoring now enables reliable, rapid, and objective data collection. Further, digital technology can quantify highly nuanced data previously unobtainable to define neurocognitive constructs with high accuracy. In this paper, a brief review of the BPA is provided. Studies that demonstrate how digital technology translates BPA into specific neurocognitive constructs using the Clock Drawing Test, Backward Digit Span Test, and a Digital Pointing Span Test are described. Implications for using data driven artificial intelligence-supported analytic approaches enabling the creation of more sensitive and specific detection/diagnostic algorithms for putative neurodegenerative illness are also discussed.

Digitally generated Trail Making Test data: Analysis using hidden Markov modeling

The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT-A and -B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT-A and -B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT-B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance.

The Correlation between a New Online Cognitive Test (the Brain Assessment) and Widely Used In-Person Neuropsychological Tests

INTRODUCTION

There are several problems with standard in-person neuropsychological assessments, such as habituation, necessity of human resources, and difficulty of in-person assessment under societal conditions during the outbreak of coronavirus disease 2019. Thus, we developed an online cognitive test (the Brain Assessment [BA]). In this study, we investigated the correlation between the results of the BA and those of established neuropsychological tests.

PARTICIPANTS AND METHODS

Seventy-seven elderly persons (mean 71.3 ± 5.1 years old; range 65-86; male:female = 45:32) were recruited through the internet. Correlations were evaluated between the BA and the following widely used neuropsychological tests: the mini-mental state examination (MMSE), the Raven's colored progressive matrices (RCPM), the logical memory I and II of the Rivermead Behavioral Memory Test, the word fluency (WF) test, and the Trail-Making TestA/B.

RESULTS

We found moderate correlations between the total cognitive score of the BA and the total score of the MMSE (r = 0.433, p < 0.001), as well as between the total BA score and the total RCPM score (r = 0.582, p < 0.001) and time to complete the RCPM (r = 0.455, p < 0.001). Moderate correlations were also observed between the cognitive score of the memory of words BA subtest and the LM-I (r = 0.518, p < 0.001), the mental rotation subtest and figure drawing (r = 0.404, p < 0.001), the logical reasoning subtest and total RCPM score (r = 0.491, p < 0.001), and the memory of numbers and words subtests and WF (memory of numbers and total WF: r = 0.456, p < 0.001; memory of words and total WF: r = 0.571, p < 0.001).

DISCUSSION

We found that the BA showed moderate correlations between established neuropsychological tests for intellect, memory, visuospatial function, and frontal function. The MMSE and the RCPM reflect Spearman's s-factor and g-factor, respectively, and thus the BA also covered both factors.

CONCLUSION

The BA is a useful tool for assessing the cognitive function of generally healthy elderly persons.

Digital Neuropsychological Assessment: New Technology for Measuring Subtle Neuropsychological Behavior

Technology has transformed the science and practice of medicine. In this special mini-forum, data using digital neuropsychological technology are reported. All of these papers demonstrate how coupling digital technology with standard paper and pencil neuropsychological tests are able to extract behavior not otherwise obtainable. As digital assessment methods mature, early identification of persons with emergent neurodegenerative and other neurological illness may be possible.