Journal: NPJ Parkinson's Disease

Article Title: Modeling and optimizing deep brain stimulation to enhance gait in Parkinson’s disease: personalized treatment with neurophysiological insights

doi: 10.1038/s41531-025-00990-5

Figure Lengend Snippet: A illustrates the DBS depth electrodes implanted along with the cortical paddles placed to stimulate electrical impulses and capture neural activity. B depicts a subject with Parkinson’s disease (PD) wearing a combination of sensors (i.e., Trigno system (Delsys) and MVN Analyze (Xsens)) to capture various gait kinematics. C shows a sample of data collected during overground walking. The first two subpanels display neural data from the Globus pallidus and motor cortex. Next are the gait kinematics, such as force sensors and ankle acceleration to capture gait events and spatiotemporal measurements to monitor body movements like arm swing amplitudes. D outlines the components of stimulation parameters altered in our experiments. E summarizes the goals of our analysis, which aim to understand the links between DBS settings, neurophysiological characteristics, and gait functions in PD. F Details the metrics employed to assess the impacts of DBS settings on walking performance, including variability in step length and step time, stride velocity, and arm swing amplitude. G presents sample results from our data-driven approach, mapping the relationship between DBS setting parameters and walking performance, where deeper red colors indicate higher walking performance. H exemplifies the outcome of our study in uncovering the neurophysiological bases of DBS settings associated with improved walking performance, where the walking performance is a function of the cortical-subcortical coherence during different phases of the gait cycle. I shows patient characteristics. MDS-UPDRS-III Movement Disorders Society Unified Parkinson’s Disease Rating Scale; Part III: motor domain, PIGD Posture Instability Gait Disorder (subscore from items 3.9: arising from the chair, 3.10: gait; 3.11: freezing, 3.12: postural instability, and 3.13: posture. Panels J and K display depth pallidal electrodes and cortical paddles, respectively.

Article Snippet: B depicts a subject with Parkinson’s disease (PD) wearing a combination of sensors (i.e., Trigno system (Delsys) and MVN Analyze (Xsens)) to capture various gait kinematics.

Techniques: Activity Assay

Journal: MethodsX

Article Title: Multimodal mobile brain and body imaging for quantification of dance motor sequence learning

doi: 10.1016/j.mex.2025.103324

Figure Lengend Snippet: Overview of equipment and respective connections. The basic setup (in green arrows) only requires a stimulus presentation PC to run E-Prime Ⓡ and record behavioral variables, including step-level accuracy and response times of participants. The bottom-right side of the diagram shows that Movella Xsens motion capture sensors that can be configured with up to 20 sensors in a full suit for recording biomechanical kinematics such as limb segment, sensor vector displacement, velocity, and acceleration. The sensors communicate through Wi-Fi with the MTw Awinda base station, which connects to the MVN Analyze recording software via a USB-A cable. Importantly, E-Prime Ⓡ sends event markers through line code via a Wi-Fi to MVN Analyze for indexing different movement moments (e.g. each step). In the top left and center of the diagram, EEG can be connected to the setup for neuroimaging. Lab streaming layer is required in our setup for E-Prime Ⓡ to send event markers to the ANT Neuro mylab EEG recording software. In this way, all stimuli and response events are synchronized and recorded in EEG and motion capture data.

Article Snippet: Xsens data was extracted using the MVN Analyze software package, utilizing its built-in export function to obtain displacement, velocity, and acceleration variables.

Techniques: Plasmid Preparation, Software