Distinct cortical and subcortical predictors of Purdue Pegboard decline in Parkinson's disease and atypical parkinsonism

The lateralized effects of Parkinson's Disease on motor imagery: Evidence from mental chronometry

Alterations to the content of action representations may contribute to the movement challenges that characterize Parkinson's Disease (PD). One way to investigate action representations is through motor imagery. As PD motor symptoms typically have a unilateral onset, disease-related deficits related to action representations may follow a similarly lateralized pattern. The present study examined if temporal accuracy of motor imagery in individuals with PD differed according to the side of the body involved in the task. Thirty-eight participants with PD completed a mental chronometry task using their more affected and less affected side. Participants had significantly shorter mental versus physical movement times for the more affected. Higher imagery vividness in the kinaesthetic domain predicted shorter mental versus physical movement times for the more affected side, as did lower imagery vividness in the visual domain and poorer cognitive function. These results indicate that people with PD imagine movements differently when the target actions their more affected versus less affected side. It is additionally possible that side-specific deficits in the accurate processing of kinaesthetic information lead to an increased reliance on visual processes and cognitive resources to successfully execute motor imagery involving the more affected side.

Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease

Background

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.

Objective

The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.

Methods

Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.

Results

The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.

Conclusion

The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Cortico-basal ganglia white matter microstructure is linked to restricted repetitive behavior in autism spectrum disorder

BACKGROUND

Restricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB.

METHODS

We utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB.

RESULTS

Individuals with ASD had significantly lower free-water corrected fractional anisotropy (FAT) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FAT and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes.

LIMITATIONS

The basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated.

CONCLUSIONS

These new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FAT was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.

Cerebellar, Not Nigrostriatal Degeneration Impairs Dexterity in Multiple System Atrophy

BACKGROUND

Multiple system atrophy (MSA) clinically manifests with either predominant nigrostriatal or cerebellopontine degeneration. This corresponds to two different phenotypes, one with predominant Parkinson's symptoms (MSA-P [multiple system atrophy-parkinsonian subtype]) and one with predominant cerebellar deficits (MSA-C [multiple system atrophy-cerebellar subtype]). Both nigrostriatal and cerebellar degeneration can lead to impaired dexterity, which is a frequent cause of disability in MSA.

OBJECTIVE

The aim was to disentangle the contribution of nigrostriatal and cerebellar degeneration to impaired dexterity in both subtypes of MSA.

METHODS

We thus investigated nigrostriatal and cerebellopontine integrity using diffusion microstructure imaging in 47 patients with MSA-P and 17 patients with MSA-C compared to 31 healthy controls (HC). Dexterity was assessed using the 9-Hole Peg Board (9HPB) performance.

RESULTS

Nigrostriatal degeneration, represented by the loss of cells and neurites, leading to a larger free-fluid compartment, was present in MSA-P and MSA-C when compared to HCs. Whereas no intergroup differences were observed between the MSAs in the substantia nigra, MSA-P showed more pronounced putaminal degeneration than MSA-C. In contrast, a cerebellopontine axonal degeneration was observed in MSA-P and MSA-C, with stronger effects in MSA-C. Interestingly, the degeneration of cerebellopontine fibers is associated with impaired dexterity in both subtypes, whereas no association was observed with nigrostriatal degeneration.

CONCLUSION

Cerebellar dysfunction contributes to impaired dexterity not only in MSA-C but also in MSA-P and may be a promising biomarker for disease staging. In contrast, no significant association was observed with nigrostriatal dysfunction. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.