The Role of Levodopa Challenge in Predicting the Outcome of Subthalamic Deep Brain Stimulation

Individual Structural Covariance Network Predicts Long-Term Motor Improvement in Parkinson Disease with Subthalamic Nucleus Deep Brain Stimulation

BACKGROUND AND PURPOSE

The efficacy of long-term chronic subthalamic nucleus deep brain stimulation (STN-DBS) in treating Parkinson disease (PD) exhibits substantial variability among individuals. The preoperative identification of suitable deep brain stimulation (DBS) candidates through predictive means becomes crucial. Our study aims to investigate the predictive value of characterizing individualized structural covariance networks for long-term efficacy of DBS, offering patients a precise and cost-effective preoperative screening tool.

MATERIALS AND METHODS

We included 138 patients with PD and 40 healthy controls. We developed individualized structural covariance networks from T1-weighted images utilizing network template perturbation, and computed the networks' topological characteristics. Patients were categorized according to their long-term motor improvement following STN-DBS. Intergroup analyses were conducted on individual network edges and topological indices, alongside correlation analyses with long-term outcomes for the entire patient cohort. Finally, machine learning algorithms were employed for regression and classification to predict post-DBS motor improvement.

RESULTS

Among the patients with PD, 6 edges (left middle frontal and left caudate nucleus, right olfactory and right insula, left superior medial frontal gyrus and right insula, right middle frontal and left paracentral lobule, right middle frontal and cerebellum, left lobule VIIb of the cerebellum and the vermis of the cerebellum) exhibited significant results in intergroup comparisons and correlation analyses. Increased degree centrality and local efficiency of the cerebellum, parahippocampal gyrus, and postcentral gyrus were associated with DBS improvement. A regression model constructed from these 6 edges revealed a significant correlation between predicted and observed changes in the unified PD rating scale (R = 0.671, P < .001) and receiver operating characteristic analysis demonstrated an area under the curve of 0.802, effectively distinguishing between patients with good and moderate improvement post-DBS.

CONCLUSIONS

Our findings reveal the link between individual structural covariance network fingerprints in patients with PD and long-term motor outcome following STN-DBS. Additionally, binary and continuous cerebellum-basal ganglia-frontal structural covariance network edges have emerged as potential predictive biomarkers for DBS motor outcome.

Stratifying quality of life outcome in subthalamic stimulation for Parkinson's disease

BACKGROUND

Subthalamic nucleus deep brain stimulation (STN-DBS) for Parkinson's disease (PD) improves quality of life (QoL), motor and non-motor symptoms (NMS). However, in previous studies, 43%-49% of patients did not experience clinically relevant postoperative QoL improvement. To inform individualised prediction of postoperative QoL improvement, we developed a stratification analysis of QoL outcomes based on preoperative non-motor total burden, severity of motor progression and motor response in levodopa challenge tests.

METHODS

This was a prospective, open-label, multicentre, international study with a 6-month follow-up. A distribution-based threshold identified 'QoL responders' in the PDQuestionnaire-8 Summary Index (PDQ-8 SI). After baseline stratification based on the NMS Scale, Hoehn and Yahr Scale and levodopa response assessed with the Unified PD Rating Scale-III, we compared postoperative QoL response between these strata. To assess the clinical usefulness and statistical feasibility of stratifications, we compared cumulative distribution function curves, respectively PDQ-8 within-stratum variation.

RESULTS

All main outcomes improved postoperatively. Based on the 8.1 points threshold for clinically meaningful PDQ-8 SI improvement, only 80/161 patients were classified as 'QoL responders'. The absolute risk reductions for QoL non-response among respective non-motor, motor progression and levodopa response strata were 23%, 8% and 3%, respectively. Only non-motor stratification reduced PDQ-8 within-stratum variation compared with the overall cohort.

CONCLUSIONS

Non-motor stratification, but not motor progression or levodopa response stratification, is clinically useful and statistically feasible for personalised preoperative prediction of postoperative QoL outcome of STN-DBS for PD. Our findings highlight that non-motor assessments are necessary components of a case-based, holistic approach of DBS indication evaluations geared towards optimising postoperative QoL outcomes.

TRIAL REGISTRATION NUMBER

GermanClinicalTrialsRegister: #6735.

Subthalamic nucleus input-output dynamics are correlated with Parkinson's burden and treatment efficacy

The subthalamic nucleus (STN) is pivotal in basal ganglia function in health and disease. Micro-electrode recordings of >25,000 recording sites from 146 Parkinson's patients undergoing deep brain stimulation (DBS) allowed differentiation between subthalamic input, represented by local field potential (LFP), and output, reflected in spike discharge rate (SPK). As with many natural systems, STN neuronal activity exhibits power-law dynamics characterized by the exponent α. We, therefore, dissected STN data into aperiodic and periodic components using the Fitting Oscillations & One Over F (FOOOF) tool. STN LFP showed significantly higher aperiodic exponents than SPK. Additionally, SPK beta oscillations demonstrated a downward frequency shift compared to LFP. Finally, the STN aperiodic and spiking parameters explained a significant fraction of the variance of the burden and treatment efficacy of Parkinson's disease. The unique STN input-output dynamics may clarify its role in Parkinson's physiology and can be utilized in closed-loop DBS therapy.

Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response

Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.

Personalizing Deep Brain Stimulation Therapy for Parkinson's Disease With Whole-Brain MRI Radiomics and Machine Learning

Background Deep brain stimulation (DBS) is a well-recognised treatment for advanced Parkinson's disease (PD) patients. Structural brain alterations of the white matter can correlate with disease progression and act as a biomarker for DBS therapy outcomes. This study aims to develop a machine learning-driven predictive model for DBS patient selection using whole-brain white matter radiomics and common clinical variables. Methodology A total of 120 PD patients underwent DBS of the subthalamic nucleus. Their therapy effect was assessed at the one-year follow-up with the Unified Parkinson's Disease Rating Scale-part III (UPDRSIII) motor component. Radiomics analysis of whole-brain white matter was performed with PyRadiomics. The following machine learning methods were used: logistic regression (LR), support vector machine, naïve Bayes, K-nearest neighbours, and random forest (RF) to allow prediction of clinically meaningful UPRDSIII motor response before and after. Clinical variables were also added to the model to improve accuracy. Results The RF model showed the best performance on the final whole dataset with an area under the curve (AUC) of 0.99, accuracy of 0.95, sensitivity of 0.93, and specificity of 0.97. At the same time, the LR model showed an AUC of 0.93, accuracy of 0.88, sensitivity of 0.84, and specificity of 0.91. Conclusions Machine learning models can be used in clinical decision support tools which can deliver true personalised therapy recommendations for PD patients. Clinicians and engineers should choose between best-performing, less interpretable models vs. most interpretable, lesser-performing models. Larger clinical trials would allow to build trust among clinicians and patients to widely use these AI tools in the future.

The connection of motor improvement after deep brain stimulation in Parkinson's disease and microstructural integrity of the substantia nigra and subthalamic nucleus

BACKGROUND

Nigrostriatal microstructural integrity has been suggested as a biomarker for levodopa response in Parkinson's disease (PD), which is a strong predictor for motor response to deep brain stimulation (DBS) of the subthalamic nucleus (STN). This study aimed to explore the impact of microstructural integrity of the substantia nigra (SN), STN, and putamen on motor response to STN-DBS using diffusion microstructure imaging.

METHODS

Data was collected from 23 PD patients (mean age 63 ± 7, 6 females) who underwent STN-DBS, had preoperative 3 T diffusion magnetic resonance imaging including multishell diffusion-weighted MRI with b-values of 1000 and 2000 s/mm2 and records of motor improvement available.

RESULTS

The association between a poorer DBS-response and increased free interstitial fluid showed notable effect sizes (rho > |0.4|) in SN and STN, but not in putamen. However, this did not reach significance after Bonferroni correction and controlling for sex and age.

CONCLUSION

Microstructural integrity of SN and STN are potential biomarkers for the prediction of therapy efficacy following STN-DBS, but further studies are required to confirm these associations.

How to Use the New European Academy of Neurology/Movement Disorder Society European Section Guideline for Invasive Therapies in Parkinson's Disease

BACKGROUND

The decision to choose invasive treatments for Parkinson's disease (PD) is complex and needs careful consideration.

OBJECTIVES

Although the recommendations of the European Academy of Neurology/Movement Disorder Society European Section guideline for invasive therapies of PD are useful, the different clinical profiles of people with PD who seek advice for possible invasive therapy need further attention.

METHODS AND RESULTS

Here we describe 8 clinical standard situations of people with PD unsatisfied with their current oral treatment where invasive therapies may be considered. These are PD patients presenting with the following symptoms: (1) severe motor fluctuations, (2) beginning of levodopa-responsive fluctuations, severe tremor at (3) young or (4) advanced age, (5) impulse control disorders and related behavioral disorders, (6) hallucinations and psychosis, (7) minimal cognitive impairment or mild dementia, and (8) patients in need of palliative care. For some of these conditions, evidence at lower level or simple clinical considerations exist.

CONCLUSIONS

There are no one-fits-all answers, but physician and patient should discuss each option carefully considering symptom profile, psychosocial context, availability of therapy alternatives, and many other factors. The current paper outlines our proposed approach to these circumstances.