Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson's Disease

Identification of Parkinson's disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach

Objective

This study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson's disease (PD).

Methods

We leveraged a comprehensive dataset from the Parkinson's Progression Markers Initiative (PPMI), which includes high-resolution neuroimaging data, genetic single-nucleotide polymorphism (SNP) profiles, and detailed clinical information from individuals with early-stage PD and healthy controls. Two multi-modal fusion strategies were used: feature-level fusion, where we employed a hybrid feature selection algorithm combining Fisher discriminant analysis, an ensemble Lasso (EnLasso) method, and partial least squares (PLS) regression to identify and integrate the most informative features from neuroimaging and genetic data; and decision-level fusion, where we developed an adaptive ensemble stacking (AE_Stacking) model to synergistically integrate the predictions from multiple base classifiers trained on individual modalities.

Results

The AE_Stacking model achieving the highest average balanced accuracy of 95.36% and an area under the receiver operating characteristic curve (AUC) of 0.974, significantly outperforming feature-level fusion and single-modal models (p < 0.05). Furthermore, by analyzing the features selected across multiple iterations of our models, we identified stable brain region features [lh 6r (FD) and rh 46 (GI)] and key genetic markers (rs356181 and rs2736990 SNPs within the SNCA gene region; rs213202 SNP within the VPS52 gene region), highlighting their potential as reliable early diagnostic indicators for the disease.

Conclusion

The AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder.

Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years

Parkinson's disease (PD) increases fall risk, leading to injuries and reduced quality of life. Accurate fall risk assessment is crucial for effective care planning. Traditional assessments are subjective and time-consuming, while recent assessment methods based on wearable sensors have been limited to 1-year follow-ups. This study investigated whether a short sensor-based assessment could predict falls over up to 5 years. Data from 104 people with PD without prior falls were collected using six wearable sensors during a 2-min walk and a 30-s postural sway task. Five machine learning classifiers analysed the data. The Random Forest classifier performed best, achieving 78% accuracy (AUC = 0.85) at 60 months. Most models showed excellent performance at 24 months (AUC > 0.90, accuracy 84-92%). Walking and postural variability measures were key predictors. Adding clinicodemographic data, particularly age, improved model performance. Wearable sensors combined with machine learning can effectively predict fall risk, enhancing PD management and prevention strategies.

A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson's Disease Rehabilitation

BACKGROUND

Parkinson's disease is primarily characterized by the degeneration of motor neurons, leading to significant impairments in movement. Initially, physical therapy was predominantly employed to address these motor issues through targeted rehabilitation exercises. However, recent research has indicated that cognitive training can enhance the quality of life for patients with Parkinson's. Consequently, some researchers have posited that the simultaneous engagement in computer-assisted motor and cognitive dual-task (CADT) may yield superior therapeutic outcomes.

METHODS

A comprehensive literature search was performed across various databases, and studies were selected following PRISMA guidelines, focusing on CADT rehabilitation interventions.

RESULTS

Dual-task training enhances Parkinson's disease (PD) rehabilitation by automating movements and minimizing secondary task interference. The inclusion of a sensor system provides real-time feedback to help patients make immediate adjustments during training. Furthermore, CADT promotes more vigorous participation and commitment to training exercises, especially those that are repetitive and can lead to patient boredom and demotivation. Virtual reality-tailored tasks, closely mirroring everyday challenges, facilitate more efficient patient adaptation post-rehabilitation.

CONCLUSIONS

Although the current studies are limited by small sample sizes and low levels, CADT rehabilitation presents as a significant, effective, and potential strategy for PD.

The validation of a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q)

OBJECTIVE

This study aimed to develop a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q) and investigate its validity and reliability.

METHODS

After translating the NFOG-Q according to a standardised protocol, 56 patients with Parkinson's disease (PD) were administered it. Additionally, the MDS-UPDRS parts II and III, Hoehn and Yahr (H&Y) stage, and number of falls over 1 month were evaluated. Spearman's correlation coefficients (rho) were used to determine construct validity, and Cronbach's alpha (α) was used to examine reliability.

RESULTS

The interquartile range of the NFOG-Q scores was 10.0-25.3 (range 0-29). The NFOG-Q scores were strongly correlated with the MDS-UPDRS part II, items 2.12 (walking and balance), 2.13 (freezing), 3.11 (freezing of gait), and 3.12 (postural stability) and the postural instability and gait difficulty score (rho = 0.515-0.669), but only moderately related to the MDS-UPDRS item 3.10 (gait), number of falls, disease duration, H&Y stage, and time of the Timed Up-and-Go test (rho = 0.319-0.434). No significant correlations were observed between age and the time of the 10-m walk test. The internal consistency was excellent (α = 0.96).

CONCLUSIONS

The Japanese version of the NFOG-Q is a valid and reliable tool for assessing the severity of freezing in patients with PD.