Quantitative digital clock drawing test as a sensitive tool to detect subtle cognitive impairments in early stage Parkinson's disease

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 Influence of GBA and LRRK2 on Mood Disorders in Parkinson's Disease

Background

Mood disorders have emerged as major non-motor comorbidities in Parkinson's disease (PD) even at the prodromal stage of the disease. Mutations in the LRRK2 and GBA genes are common among Ashkenazi Jews, with more severe phenotype reported for GBA-PD.

Objective

To explore the association between genetic status and mood related disorders before and after diagnosis of PD and the association between mood-related medications, phenotype, and genetic status.

Methods

Participants were genotyped for mutations in the LRRK2 and GBA genes. State of depression, anxiety and non-motor features were evaluated using validated questionnaires. History of mood disorders prior to diagnosis of PD and use of mood-related medications were assessed.

Results

The study included 105 idiopathic PD (iPD), 55 LRRK2-PD and 94 GBA-PD. Scores on mood related questionnaires and frequency of depression and anxiety before diagnosis were similar between the groups (p>0.05). However, more GBA-PD patients used mood related medications before PD diagnosis than LRRK2-PD and iPD (16.5% vs 7.1% and 8.2%, p=0.044). LRRK2-PD and GBA-PD receiving mood-related medications at time of assessment had worse motor and non-motor phenotype compared to those that did not (p<0.05). LRRK2-PD receiving mood related-medications at time of assessment, scored higher on mood-related questionnaires compared to LRRK2-PD not receiving such medications (p<0.04).

Conclusions

Prodromal GBA-PD are more frequently treated with mood related-medications despite equal rates of reported mood-related disorders, while LRRK2-PD with mood-related disorders experience high rates of anxiety and depression despite treatment, attesting to the need of more precise assessment and treatment of these genetic subgroups.

Dissociating Statistically Determined Normal Cognitive Abilities and Mild Cognitive Impairment Subtypes with DCTclock

OBJECTIVE

To determine whether the DCTclock can detect differences across groups of patients seen in the memory clinic for suspected dementia.

METHOD

Patients (n = 123) were classified into the following groups: cognitively normal (CN), subtle cognitive impairment (SbCI), amnestic cognitive impairment (aMCI), and mixed/dysexecutive cognitive impairment (mx/dysMCI). Nine outcome variables included a combined command/copy total score and four command and four copy indices measuring drawing efficiency, simple/complex motor operations, information processing speed, and spatial reasoning.

RESULTS

Total combined command/copy score distinguished between groups in all comparisons with medium to large effects. The mx/dysMCI group had the lowest total combined command/copy scores out of all groups. The mx/dysMCI group scored lower than the CN group on all command indices (p < .050, all analyses); and lower than the SbCI group on drawing efficiency (p = .011). The aMCI group scored lower than the CN group on spatial reasoning (p = .019). Smaller effect sizes were obtained for the four copy indices.

CONCLUSIONS

These results suggest that DCTclock command/copy parameters can dissociate CN, SbCI, and MCI subtypes. The larger effect sizes for command clock indices suggest these metrics are sensitive in detecting early cognitive decline. Additional research with a larger sample is warranted.

Neural activation in the prefrontal cortex during the digital clock drawing test measured with functional near-infrared spectroscopy in early stage Parkinson's disease

INTRODUCTION

The clock drawing test (CDT) is a neuropsychological test for the screening of global cognitive functioning. The test requires use of multiple cognitive domains including executive functions, visuospatial abilities and semantic memory and can be a suitable tool for screening cognitive decline in participants in the early stages of Parkinson's Disease (PD). Behavioral performance on the CDT has been studied in depth, however, neural activation during real-time performance has not been extensively investigated. In this study we explored changes in prefrontal cortex (PFC) activation during the performance of CDT in participants with PD compared to healthy controls (HC) and assessed the correlations between PFC activation and CDT performance.

METHODS

The study included 60 participants, 29 PD and 31 HC participants whom performed a digital CDT (DCTclock) in conjunction with a Functional Near-Infrared Spectroscopy (fNIRS) system measuring neural activation in the PFC.

RESULTS

HbO2 signals derived from the fNIRS during the CDT revealed that PD participants showed more moderate slopes than the HC in the right hemisphere in the command (p = 0.042) and copy task (p = 0.009). Better score on the measurement of information processing correlated with steeper right hemisphere HbO2 slope in the copy task in the PD group (p = 0.003).

CONCLUSION

Our results reflect slower PFC activation in participants with PD which correlates with behavioral measures. In addition, the findings of the study indicate the importance of performing the CDT copy task condition that detect early cognitive decline in participants with PD.

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.

Erythrocytes Are an Independent Protective Factor for Vascular Cognitive Impairment in Patients With Severe White Matter Hyperintensities

Background and Purpose: Hemoglobin is one of the main proteins in erythrocytes. There are significant correlations between low hemoglobin and white matter hyperintensities (WMH) and cognitive impairment. This study explored whether erythrocytopenia has predictive value for vascular cognitive impairment (VCI) in patients with WMH.

Method: We conducted a cross-sectional study of 302 patients, including 62 with cerebral small vessel disease and 240 with stroke. Basic demographic data and fasting blood were collected. First, all patients were divided into normal cognition (NC), mild VCI (mVCI), and severe VCI (sVCI) groups (subgroups later) based on cognitive behavior scores. Second, all patients were divided into mild WMH (mWMH) and severe WMH (sWMH) groups based on Fazekas scores. The differences in blood markers between different groups or subgroups with different cognitive levels were analyzed by univariate analysis. Then, binary logistic regression was used to analyze the diagnostic value of erythrocyte counts for VCI in the sWMH group, and ordinal logistic regression was used to analyze the predictive value of multiple variables for different cognitive levels.

Results: Univariate analysis showed that erythrocytes, hemoglobin, high-sensitivity C-reactive protein, retinol binding protein and prealbumin were potential blood markers for different cognitive levels in sWMH patients. Among them, erythrocytopenia has good predictive value for the diagnosis of mVCI (AUC = 0.685, P = 0.008) or sVCI (AUC = 0.699, P = 0.003) in patients with sWMH. Multivariate joint analysis showed that erythrocytes were an independent protective factor reducing the occurrence of VCI in patients with sWMH (OR = 0.633, P = 0.045). Even after adjusting for age, there was still a significant difference (P = 0.047).

Conclusion: Erythrocytes are an independent protective factor for VCI in patients with sWMH. Promoting hematopoietic function may have potential value for prevention of cognitive decline in patients with cerebrovascular disease.