Deep brain stimulation in Parkinson's disease: analysis of brain fractional anisotropy differences in operated patients

Introduction. Deep brain stimulation (DBS) of the subthalamic nucleus is currently an evidence-based therapeutic option for motor symptoms in patients with Parkinson's disease (PD), although other non-motor symptoms can be affected by stimulation. Aim. Our objective is to evaluate the global changes in the connectivity of the large-scale structural network in PD patients that have obtained a benefit from subthalamic DBS. Subjects and methods. Retrospective study of 31 subjects: 7 PD patients with subthalamic DBS (group A), 12 age and gender-matched non-operated PD (B) and 12 healthy controls (C). All subjects had undergone a 1.5 T brain MRI with DTI. DICOM images were processed with the FSL5.0 software and TBSS tool. Results. The study group comprised 23 men and 8 women. No statistically significant differences in age, gender, scores on the H&Y scale and mean follow-up between group A and B were found, and in age and gender between groups A and C. Statistical analysis revealed differences in the fractional anisotropy of the different groups in certain areas: bilateral corticospinal tract, anterior thalamic radiations, bilateral fronto-occipital fascicle, both superior longitudinal fascicles, and left inferior longitudinal fascicle. Conclusions. In our series, PD patients treated with bilateral subthalamic DBS showed a significantly higher fractional anisotropy in widespread areas of the cerebral white matter; suggesting that neuromodulation produces connectivity changes in different neural networks.

Automated summary

This study evaluated the global changes in the connectivity of the large-scale structural network in Parkinson's disease patients who received subthalamic DBS. The results showed significantly higher fractional anisotropy in widespread areas of the cerebral white matter in PD patients treated with bilateral subthalamic DBS, suggesting that neuromodulation produces connectivity changes in different neural networks.