Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease

Correlation of brain iron deposition and freezing of gait in Parkinson's disease: a cross-sectional study

Background

Quantitative susceptibility mapping (QSM) is a novel imaging method for detecting iron content in the brain. The study aimed determine whether the iron deposition in the brains of people with Parkinson's disease (PD) is correlated with freezing of gait (FOG).

Methods

We retrospectively collected the data of 24 patients with PD from the Movement Disorders Program and 36 healthy controls (HCs) from January 2021 to December 2021. Clinical assessments included mental intelligence scales, Parkinson rating scales, motor-related scales, and clinical gait assessments. All exercise scales and gait assessments were performed in the "ON" and "OFF" states. Magnetic resonance imaging (MRI) data were collected using 3-dimensional fast low-angle shot sequences. We chose the bilateral red nucleus, substantia nigra, thalamus, putamen, caudate nucleus, and globus pallidus as regions of interest for QSM analysis.

Results

The iron deposition in the substantia nigra of the PD group was significantly higher than that of the HC group (P<0.01). In the PD group, the iron deposition in the substantia nigra of patients with FOG was significantly higher than that in patients without FOG (P=0.04). The iron deposition in the substantia nigra was positively correlated with the New Freezing of Gait Questionnaire (P=0.03). The scores for depression and anxiety of the PD group were significantly higher than those of the HC group, while the Berg balance scale score was significantly lower (P<0.01).

Conclusions

The iron deposition in the substantia nigra of patients with PD is increased compared with that of controls and is associated with FOG. QSM can be used to detect brain iron deposition in patients with PD, which would help to explore the mechanism of abnormal neurobiological activity in FOG.

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer's disease (AD) and Parkinson's disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.

Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review

Background

The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way.

Objective

This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD.

Methods

Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance.

Results

A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection.

Conclusion

These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.