Distinct Contributions of the Cerebellum and Basal Ganglia to Arithmetic Procedures

The neural basis of intuitive approximate number system in board game Go (Baduk) experts

Studies have shown that newborns and nonhuman animals innately estimate quantities using the approximate number system (ANS), raising questions about whether the ANS is a precursor to advanced computational abilities or an independent cognitive function. Professional board game Go players, who can quickly judge territory sizes without explicit calculations, provide a unique insight into the ANS. Using fMRI, we investigated the neural correlates of the approximate number system in professional Go players. Results showed that during the difficult task, professional Go players exhibited significantly increased activity in the right cerebellum compared to the controls, while several parts of the cerebrum were activated during the easy task. The observed activation in the right cerebellum was inversely correlated with the number of years of training required to become professional players. The findings indicate that the ANS is either facilitated by training or reflects an inherent, exceptional ability in certain individuals, suggesting a cerebellar-based alternative to the computational role of the cerebral cortex.

Large-scale evidence for the validity of remote MoCA administration among people with cerebellar ataxia

Objective: For over half a century, studies of rare diseases using in-person cognitive tools have faced challenges, such as long study periods and small sample sizes (e.g. n = 10). The Montreal Cognitive Assessment (MoCA) was widely employed to assess mild cognitive impairment (MCI). We aimed to validate a modified online version of the MoCA in a large sample of a rare disease (population prevalence < .01%). Method: First, we analyzed 20 previous findings (n = 1,377), comparing the MoCA scores between large groups of neurotypically healthy (NH; n = 837) and cerebellar ataxia (CA; n = 540), where studies were conducted in-person. Second, we administered the MoCA in-person to a group of NH (n = 41) and a large group of CA (n = 103). Third, we administered a video conferencing version of the MoCA to NH (n = 38) and a large group of CA (n = 83). Results: We observed no performance differences between online and in-person MoCA administration in the NH and CA groups (p > .05, η2 = 0.001), supporting reliability. Additionally, our online CA group had lower MoCA scores than the NH group (p < .001, Hedges' g = 0.68). This result is consistent with previous studies, as demonstrated by our forest plot across 20 previous in-person findings, supporting construct validity. Conclusion: The results indicate that an online screening tool is valid in a large sample of individuals with CA. Online testing is not only time and cost-effective, but facilitates disease management and monitoring, ultimately enabling early detection of MCI.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

Objective

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.

Method

This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

Results

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.

Conclusion

The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

Online cognitive testing in Parkinson's disease: advantages and challenges

Parkinson's disease (PD) is primarily characterized by motor symptoms. Yet, many people with PD experience cognitive decline, which is often unnoticed by clinicians, although it may have a significant impact on quality of life. For over half a century, traditional in-person PD cognitive assessment lacked accessibility, scalability, and specificity due to its inherent limitations. In this review, we propose that novel methods of online cognitive assessment could potentially address these limitations. We first outline the challenges of traditional in-person cognitive testing in PD. We then summarize the existing literature on online cognitive testing in PD. Finally, we explore the advantages, but also the limitations, of three major processes involved in online PD cognitive testing: recruitment and sampling methods, measurement and participation, and disease monitoring and management. Taking the limitations into account, we aim to highlight the potential of online cognitive testing as a more accessible and efficient approach to cognitive testing in PD.