Investigation of the changes in the presynaptic inhibition in association with the subthalamic nucleus stimulation in Parkinson's disease

Temporal Investigations of the Changes in Presynaptic Inhibition Associated With Subthalamic Nucleus-Deep-Brain Stimulation

BACKGROUND AND PURPOSE

There are controversies regarding the role of presynaptic inhibition (PSI) in the mechanisms underlying the efficacy of deep-brain stimulation (DBS) in Parkinson's disease (PD). We sought to determine the involvement of PSI in DBS-related mechanisms and clinical correlates.

METHODS

We enrolled PD subjects who had received subthalamic nucleus DBS (STN-DBS) therapy and had been admitted to our clinic between January 2022 and March 2022. The tibial H-reflex was studied bilaterally during the medication-off state, and all tests were repeated 10 and 20 minutes after the simulation was turned off. Simultaneous evaluations based on the Movement-Disorder-Society-sponsored revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III) were performed in all of the patients.

RESULTS

Ultimately we enrolled 18 patients aged 58.7±9.3 years (mean±standard deviation, 10 females). Fifty percent of the patients showed a decrease in the MDS-UPDRS-III score of more than 60% during the stimulation-on period. Comparative analyses of the repeated measurements made according to the stimulation status revealed significant differences only in the left H-reflex/M-response amplitude ratio (H/M ratio). However, no difference in the left H/M ratio was found in the subgroup of patients with a prominent clinical response to stimulation (n=9). Analyses of the less-affected side revealed differences in the H-reflex amplitude and H/M ratio.

CONCLUSIONS

We found evidence of PSI recovery on the less-affected side of our PD subjects associated with STN-DBS. We hypothesize that the involvement of this spinal pathway and its contribution to the mechanisms of DBS differ between individuals based on the severity of the disease and which brainstem regions and descending tracts are involved.

Cortical atrophy distinguishes idiopathic normal-pressure hydrocephalus from progressive supranuclear palsy: A machine learning approach

INTRODUCTION

Progressive supranuclear palsy (PSP) and idiopathic normal pressure hydrocephalus (iNPH) share several clinical and radiological features, making the differential diagnosis challenging. In this study, we aimed to differentiate between these two diseases using a machine learning approach based on cortical thickness and volumetric data.

METHODS

Twenty-three iNPH patients, 50 PSP patients and 55 control subjects were enrolled. All participants underwent a brain 3T-MRI, and cortical thickness and volumes were extracted using Freesurfer 6 on T1-weighted images and compared among groups. Finally, the performance of a machine learning approach with random forest using the extracted cortical features was investigated to differentiate between iNPH and PSP patients.

RESULTS

iNPH patients showed cortical thinning and volume loss in the frontal lobe, temporal lobe and cingulate cortex, and thickening in the superior parietal gyrus in comparison with controls and PSP patients. PSP patients only showed mild thickness and volume reduction in the frontal lobe, compared to control subjects. Random Forest algorithm distinguished iNPH patients from controls with AUC of 0.96 and from PSP patients with AUC of 0.95, while a lower performance (AUC 0.76) was reached in distinguishing PSP from controls.

CONCLUSION

This study demonstrated a more severe and widespread cortical involvement in iNPH than in PSP, possibly due to the marked lateral ventricular enlargement which characterizes iNPH. A machine learning model using thickness and volumetric data led to accurate differentiation between iNPH and PSP patients, which may help clinicians in the differential diagnosis and in the selection of patients for shunt procedures.