The discriminant validity of Rey Complex Figure Test (RCFT) in subjective cognitive decline, mild cognitive impairment (multiple domain) and Alzheimer's disease dementia (ADD; mild stage) in Greek older adults

Non-equivalence of sub-tasks of the Rey-Osterrieth Complex Figure Test with convolutional neural networks to discriminate mild cognitive impairment

BACKGROUND

The Rey-Osterrieth Complex Figure Test (RCFT) is a tool to evaluate cognitive function. Despite its usefulness, its scoring criteria are as complicated as its figure, leading to a low reliability. Therefore, this study aimed to determine the feasibility of using the convolutional neural network (CNN) model based on the RCFT as a screening tool for mild cognitive impairment (MCI) and investigate the non-equivalence of sub-tasks of the RCFT.

METHODS

A total of 354 RCFT images (copy and recall conditions) were obtained from 103 healthy controls (HCs) and 74 patients with amnestic MCI (a-MCI). The CNN model was trained to predict MCI based on the RCFT-copy and RCFT-recall images. To evaluate the CNN model's performance, accuracy, sensitivity, specificity, and F1-score were measured. To compare discriminative power, the area under the curve (AUC) was calculated by the receiver operating characteristic (ROC) curve analysis.

RESULTS

The CNN model based on the RCFT-recall was the most accurate in discriminating a-MCI (accuracy: RCFT-copy = 0.846, RCFT-recall = 0.872, MoCA-K = 0.818). Furthermore, the CNN model based on the RCFT could better discriminate MCI than the MoCA-K (AUC: RCFT-copy = 0.851, RCFT-recall = 0.88, MoCA-K = 0.848). The CNN model based on the RCFT-recall was superior to the RCFT-copy.

CONCLUSION

These findings suggest the feasibility of using the CNN model based on the RCFT as a surrogate for a conventional screening tool for a-MCI and demonstrate the superiority of the CNN model based on the RCFT-recall to the RCFT-copy.

Cognition and Functionality Were Not Affected Due to the COVID-19 Pandemic in People with Mild Cognitive Impairment and AD Dementia Attending Digital Non-Pharmacologic Interventions

BACKGROUND

The majority of previous studies showed that older adults with mild cognitive impairment (MCI) as well as Alzheimer's disease dementia (ADD) had impaired cognition and mood status, as well as increased behavioral disturbances after the first wave of the COVID-19 pandemic. However, there are still controversial data as regards the multifactorial impact of the restrictive measures on cognition, mood and daily function in older adults with MCI and ADD.

AIM

In the current study, the scope is to identify possible deterioration by means of cognitive and functional level due to mood and behavioral alterations during the second quarantine imposed in Greece between November 2020 and May 2021, as well as one year after the second quarantine, in May 2022.

METHODS

Participants were recruited from the two day centers of the Greek Association of Alzheimer Disease and Related Disorders (GAADRD). They underwent three yearly follow up assessments from May 2020 to May 2022 and participated in cognitive training interventions (through digital online means) during the aforementioned period. Mixed measures analyses of variance as well as path models were used for the study's purposes.

RESULTS

The study sample comprised 210 participants (175 people with MCI and 35 people with ADD). The mean age was 71.59 and 77.94 for people with MCI and mild ADD, respectively, whereas the average number of years of education was 12.65 for those with MCI and 9.83 for people with mild ADD. The results show that participants' deterioration rate (D), calculated by means of their performance in neuropsychological and functional assessments between 2020 and 2021 (D1) and 2021 and 2022 (D2), did not change significantly, except for the Rey Auditory Verbal Learning Test (RAVLT), since both groups displayed a larger D2 across the test conditions (immediate recall, fifth trial and delayed recall). Trail Making Test-B (TMT-B) performance, applied only in the MCI group, decreased more in relation to the deterioration rate D2. Additionally, two path models were applied to measure the direct relationships between diagnosis, performance in tests measuring mood and neuropsychiatric disturbances (NPI) and cognition, as measured by the RAVLT, in the 2020-2022 assessments. TMT-B was administered only in the MCI population, and therefore was not included in path models. The results show that participants' scores in RAVLT conditions were related to diagnosis and NPI performance, which was positively affected by diagnosis. No other relationships between RAVLT with mood tests were observed.

CONCLUSIONS

Our results show that after the second lockdown period, the neuropsychological performance of people with MCI and ADD, calculated by means of their D2, did not change, except from their verbal memory, as well as visual scanning and information processing, measured using the TMT-B. Therefore, it can be assumed that those who were enrolled in digital non-pharmacological interventions during the COVID-19 pandemic home restrictions did not experience increased cognitive and functional deterioration due to mood and behavioral alterations after the pandemic.

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

BACKGROUND

Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method.

OBJECTIVE

This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices.

METHODS

This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22).

RESULTS

We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data.

CONCLUSIONS

The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' cognitive impairment compared to conventional paper-based feature assessment.