Differential effects of deep brain stimulation and levodopa on brain activity in Parkinson's disease

Consistent abnormal activity in the putamen by dopamine modulation in Parkinson's disease: A resting-state neuroimaging meta-analysis

OBJECTIVE

This study aimed to elucidate brain areas mediated by oral anti-parkinsonian medicine that consistently show abnormal resting-state activation in PD and to reveal their functional connectivity profiles using meta-analytic approaches.

METHODS

Searches of the PubMed, Web of Science databases identified 78 neuroimaging studies including PD OFF state (PD-OFF) versus (vs.) PD ON state (PD-ON) or PD-ON versus healthy controls (HCs) or PD-OFF versus HCs data. Coordinate-based meta-analysis and functional meta-analytic connectivity modeling (MACM) were performed using the activation likelihood estimation algorithm.

RESULTS

Brain activation in PD-OFF vs. PD-ON was significantly changed in the right putamen and left inferior parietal lobule (IPL). Contrast analysis indicated that PD-OFF vs. HCs had more consistent activation in the right paracentral lobule, right middle frontal gyrus, right thalamus, left superior parietal lobule and right putamen, whereas PD-ON vs. HCs elicited more consistent activation in the bilateral middle temporal gyrus, left occipital gyrus, right inferior frontal gyrus and right caudate. MACM revealed coactivation of the right putamen in the direct contrast of PD-OFF vs. PD-ON. Subtraction analysis of significant coactivation clusters for PD-OFF vs. PD-ON with the medium of HCs showed effects in the sensorimotor, top-down control, and visual networks. By overlapping the MACM maps of the two analytical strategies, we demonstrated that the coactivated brain region focused on the right putamen.

CONCLUSIONS

The convergence of local brain regions and co-activation neural networks are involved the putamen, suggesting its potential as a specific imaging biomarker to monitor treatment efficacy.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD CRD42022304150].

The altered spontaneous neural activity in patients with Parkinson's disease and its predictive value for the motor improvement of deep brain stimulation

BACKGROUND

This study aims to investigate the altered spontaneous neural activity in patients with Parkinson's disease (PD) revealed by amplitudes of low-frequency fluctuations (ALFF) of resting-state fMRI, and the feasibility of using ALFF as neuroimaging predictors for motor improvement after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS).

METHODS

Fourty-four patients and 44 healthy controls were included in this study. First, the ALFF of patients with PD was compared with that of controls; then significant clusters were correlated with motor improvement after DBS (unified Parkinson's disease rating scale (UPDRS-III)) and other clinical variables. Second, regression and classification of the machine learning models were conducted to predict motor improvement after DBS. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model.

RESULTS

Compared with healthy controls, patients with PD showed increased ALFF in the bilateral motor area and decreased ALFF in the bilateral temporal cortex and cerebellum. The Hoehn-Yahr stages correlated with ALFF within the bilateral cerebellum (p = 0.021), and UPDRS-III improvement correlated with ALFF in the left (p < 0.001) and right (p = 0.005) motor areas. The regression model showed a significant correlation between the predicted and observed UPDRS-III changes (R = 0.65, p < 0.001). The ROC analysis revealed an area under the curve (AUC) of 0.94 which differentiated moderate and superior DBS responders.

CONCLUSION

The results revealed altered ALFF patterns in patients with PD and their correlations with clinical variables. Both binary and continuous ALFF can potentially serve as predictive biomarkers for DBS response.

Predicting Motor Outcome and Quality of Life After Subthalamic Deep Brain Stimulation for Parkinson's Disease: The Role of Standard Screening Measures and Wearable-Data

BACKGROUND

Standardized screening for subthalamic deep brain stimulation (STN DBS) in Parkinson's disease (PD) patients is crucial to determine eligibility, but its utility to predict postoperative outcomes in eligible patients is inconclusive. It is unknown whether wearable data can contribute to this aim.

OBJECTIVE

To evaluate the utility of universal components incorporated in the DBS screening, complemented by a wearable sensor, to predict motor outcomes and Quality of life (QoL) one year after STN DBS surgery.

METHODS

Consecutive patients were included in the OPTIMIST cohort study from two DBS centers. Standardized assessments included a preoperative Levodopa Challenge Test (LCT), and questionnaires on QoL and non-motor symptoms including cognition, psychiatric symptoms, impulsiveness, autonomic symptoms, and sleeping problems. Moreover, an ambulatory wearable sensor (Parkinson Kinetigraph (PKG)) was used. Postoperative assessments were similar and also included a Stimulation Challenge Test to determine DBS effects on motor function.

RESULTS

Eighty-three patients were included (median (interquartile range) age 63 (56-68) years, 36% female). Med-OFF (Stim-OFF) motor severity deteriorated indicating disease progression, but patients significantly improved in terms of Med-ON (Stim-ON) motor function, motor fluctuations, QoL, and most non-motor domains. Motor outcomes were not predicted by preoperative tests, including covariates of either LCT or PKG. Postoperative QoL was predicted by better preoperative QoL, lower age, and more preoperative impulsiveness scores in multivariate models.

CONCLUSION

Data from the DBS screening including wearable data do not predict postoperative motor outcome at one year. Post-DBS QoL appears primarily driven by non-motor symptoms, rather than by motor improvement.

Improving fMRI in Parkinson's disease by accounting for brain region-specific activity patterns

In functional magnetic imaging (fMRI) in Parkinson's disease (PD), a paradigm consisting of blocks of finger tapping and rest along with a corresponding general linear model (GLM) is often used to assess motor activity. However, this method has three limitations: (i) Due to the strong magnetic field and the confined environment of the cylindrical bore, it is troublesome to accurately monitor motor output and, therefore, variability in the performed movement is typically ignored. (ii) Given the loss of dopaminergic neurons and ongoing compensatory brain mechanisms, motor control is abnormal in PD. Therefore, modeling of patients' tapping with a constant amplitude (using a boxcar function) and the expected Parkinsonian motor output are prone to mismatch. (iii) The motor loop involves structures with distinct hemodynamic responses, for which only one type of modeling (e.g., modeling the whole block of finger tapping) may not suffice to capture these structure's temporal activation. The first two limitations call for considering results from online recordings of the real motor output that may lead to significant sensitivity improvements. This was shown in previous work using a non-magnetic glove to capture details of the patients' finger movements in a so-called kinematic approach. For the third limitation, modeling motion initiation instead of the whole tapping block has been suggested to account for different temporal activation signatures of the motor loop's structures. In the present study we propose improvements to the GLM as a tool to study motor disorders. For this, we test the robustness of the kinematic approach in an expanded cohort (n = 31), apply more conservative statistics than in previous work, and evaluate the benefits of an event-related model function. Our findings suggest that the integration of the kinematic approach offers a general improvement in detecting activations in subcortical structures, such as the basal ganglia. Additionally, modeling motion initiation using an event-related design yielded superior performance in capturing medication-related effects in the putamen. Our results may guide adaptations in analysis strategies for functional motor studies related to PD and also in more general applications.

DOPA pheomelanin is increased in nigral neuromelanin of Parkinson's disease

Neuromelanin (NM) in dopaminergic neurons of human substantia nigra (SN) has a melanic component that consists of pheomelanin and eumelanin moieties and has been proposed as a key factor contributing to dopaminergic neuron vulnerability in Parkinson's disease (PD). While eumelanin is considered as an antioxidant, pheomelanin and related oxidative stress are associated with compromised drug and metal ion binding and melanoma risk. Using postmortem SN from patients with PD or Alzheimer's disease (AD) and unaffected controls, we identified increased L-3,4-dihydroxyphenylalanine (DOPA) pheomelanin and increased ratios of dopamine (DA) pheomelanin markers to DA in PD SN compared to controls. Eumelanins derived from both DOPA and DA were reduced in PD group. In addition, we report an increase in DOPA pheomelanin relative to DA pheomelanin in PD SN. In AD SN, we observed unaltered melanin markers despite reduced DOPA compared to controls. Furthermore, synthetic DOPA pheomelanin induced neuronal cell death in vitro while synthetic DOPA eumelanin showed no significant effect on cell viability. Our findings provide insights into the different roles of pheomelanin and eumelanin in PD pathophysiology. We anticipate our study will lead to further investigations on pheomelanin and eumelanin individually as biomarkers and possibly therapeutic targets for PD.

Relationship Between Iron Distribution in Deep Gray Matter Nuclei Measured by Quantitative Susceptibility Mapping and Motor Outcome After Deep Brain Stimulation in Patients With Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor deficits in advanced Parkinson's disease (PD) patients, but the degree of motor improvement varies across individuals. PD pathology involves the changes of iron spatial distribution in the deep gray matter nuclei.

PURPOSE

To explore the relationship between the iron spatial distribution and motor improvement among PD patients who underwent STN-DBS surgery in three regions: substantia nigra (SN), STN, and dentate nucleus (DN).

STUDY TYPE

Prospective.

SUBJECTS

Forty PD patients (49.7 ± 8.8 years, 22 males/18 females) who underwent bilateral STN-DBS.

FIELD STRENGTH/SEQUENCE

A 3 T preoperative three-dimensional spoiled bipolar-readout multi-echo gradient recalled echo and two-dimensional fast spin echo sequences.

ASSESSMENT

Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) scores were assessed 2-3 days before and 6 months after STN-DBS. The first- and second-order texture features in regions of interest were measured on susceptibility maps.

STATISTICAL TESTS

Intraclass correlation coefficient was used to determine the consistency of the region of interest volumes delineated by the two raters. Pearson or Spearman's correlation coefficients were used to assess the relationship between motor improvement after DBS and texture features. A P-value <0.05 was considered statistically significant.

RESULTS

MDS-UPDRS III scores were reduced by 59.9% after STN-DBS in 40 PD patients. Motor improvement correlated with second-order texture parameters in the SN including angular second moment (r = -0.449), correlation (rho = 0.326), sum of squares (r = 0.402), sum of entropy (rho = 0.421), and entropy (r = 0.410). Additionally, DBS outcome negatively correlated with mean susceptibility values in the DN (r = -0.400).

DATA CONCLUSION

PD patients with a more homogeneous iron distribution throughout the SN or a higher iron concentration in the DN responded worse to STN-DBS.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Alterations of resting-state networks of Parkinson's disease patients after subthalamic DBS surgery

The implantation of deep brain stimulation (DBS) electrodes in Parkinson's disease (PD) patients can lead to a temporary improvement in motor symptoms, known as the stun effect. However, the network alterations induced by the stun effect are not well characterized. As therapeutic DBS is known to alter resting-state networks (RSN) and subsequent motor symptoms in patients with PD, we aimed to investigate whether the DBS-related stun effect also modulated RSNs. Therefore, we analyzed RSNs of 27 PD patients (8 females, 59.0 +- 8.7 years) using magnetoencephalography and compared them to RSNs of 24 age-matched healthy controls (8 females, 62.8 +- 5.1 years). We recorded 30 min of resting-state activity two days before and one day after implantation of the electrodes with and without dopaminergic medication. RSNs were determined by use of phase-amplitude coupling between a low frequency phase and a high gamma amplitude and examined for differences between conditions (i.e., pre vs post surgery). We identified four RSNs across all conditions: sensory-motor, visual, fronto-occipital, and frontal. Each RSN was altered due to electrode implantation. Importantly, these changes were not restricted to spatially close areas to the electrode trajectory. Interestingly, pre-operative RSNs corresponded better with healthy control RSNs regarding the spatial overlap, although the stun effect is associated with motor improvement. Our findings reveal that the stun effect induced by implantation of electrodes exerts brain wide changes in different functional RSNs.

The Instrumental Activities of Daily Living in Parkinson’s Disease Patients Treated by Subthalamic Deep Brain Stimulation

Background

Everyday functioning and instrumental activities of daily living (IADL) play a vital role in preserving the quality of life in patients with Parkinson’s disease (PD) after deep brain stimulation of the subthalamic nucleus (STN-DBS).

Objective

The main goal of the current study was to examine IADL change in pre-and post-surgery of the STN-DBS. We also analyzed the influence of the levodopa equivalent daily dose (LEDD) and global cognitive performance (Dementia Rating Scale; DRS-2) as covariates in relation to IADL.

Methods

Thirty-two non-demented PD patients were administered before and after STN-DBS neurosurgery the Penn Parkinson’s Daily Activities Questionnaire (PDAQ; self-report), the DRS-2 and Beck Depression Inventory (BDI-II) to assess IADL change, global cognition, and depression.

Results

We found a positive effect of STN-DBS on IADL in the post-surgery phase. Moreover, lower global cognition and lower LEDD are predictive of lower IADL in both pre-surgery and post-surgery examinations.

Summary/Conclusion

STN-DBS in PD is a safe method for improvement of everyday functioning and IADL. In the post-surgery phase, we show a relation of IADL to the severity of cognitive impairment in PD and to LEDD.

Probing responses to deep brain stimulation with functional magnetic resonance imaging

BACKGROUND

Deep brain stimulation (DBS) is an established treatment for certain movement disorders and has additionally shown promise for various psychiatric, cognitive, and seizure disorders. However, the mechanisms through which stimulation exerts therapeutic effects are incompletely understood. A technique that may help to address this knowledge gap is functional magnetic resonance imaging (fMRI). This is a non-invasive imaging tool which permits the observation of DBS effects in vivo.

OBJECTIVE

The objective of this review was to provide a comprehensive overview of studies in which fMRI during active DBS was performed, including studied disorders, stimulated brain regions, experimental designs, and the insights gleaned from stimulation-evoked fMRI responses.

METHODS

We conducted a systematic review of published human studies in which fMRI was performed during active stimulation in DBS patients. The search was conducted using PubMED and MEDLINE.

RESULTS

The rate of fMRI DBS studies is increasing over time, with 37 studies identified overall. The median number of DBS patients per study was 10 (range = 1-67, interquartile range = 11). Studies examined fMRI responses in various disease cohorts, including Parkinson's disease (24 studies), essential tremor (3 studies), epilepsy (3 studies), obsessive-compulsive disorder (2 studies), pain (2 studies), Tourette syndrome (1 study), major depressive disorder, anorexia, and bipolar disorder (1 study), and dementia with Lewy bodies (1 study). The most commonly stimulated brain region was the subthalamic nucleus (24 studies). Studies showed that DBS modulates large-scale brain networks, and that stimulation-evoked fMRI responses are related to the site of stimulation, stimulation parameters, patient characteristics, and therapeutic outcomes. Finally, a number of studies proposed fMRI-based biomarkers for DBS treatment, highlighting ways in which fMRI could be used to confirm circuit engagement and refine DBS therapy.

CONCLUSION

A review of the literature reflects an exciting and expanding field, showing that the combination of DBS and fMRI represents a uniquely powerful tool for simultaneously manipulating and observing neural circuitry. Future work should focus on relatively understudied disease cohorts and stimulated regions, while focusing on the prospective validation of putative fMRI-based biomarkers.

Use of Functional MRI in Deep Brain Stimulation in Parkinson's Diseases: A Systematic Review

Deep brain stimulation (DBS) has been used to modulate aberrant circuits associated with Parkinson's disease (PD) for decades and has shown robust therapeutic benefits. However, the mechanism of action of DBS remains incompletely understood. With technological advances, there is an emerging use of functional magnetic resonance imaging (fMRI) after DBS implantation to explore the effects of stimulation on brain networks in PD. This systematic review was designed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to summarize peer-reviewed articles published within the past 10 years in which fMRI was employed on patients with PD-DBS. Search in PubMed database provided 353 references, and screenings resulted in a total of 19 studies for qualitative synthesis regarding study designs (fMRI scan timepoints and paradigm), methodology, and PD subtypes. This review concluded that fMRI may be used in patients with PD-DBS after proper safety test; resting-state and block-based fMRI designs have been employed to explore the effects of DBS on brain networks and the mechanism of action of the DBS, respectively. With further validation of safety use of fMRI and advances in imaging techniques, fMRI may play an increasingly important role in better understanding of the mechanism of stimulation as well as in improving clinical care to provide subject-specific neuromodulation treatments.

A Multiscale, Systems-Level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching Under Normal, Parkinsonian, and Levodopa Medication Conditions

In order to understand the link between substantia nigra pars compacta (SNc) cell loss and Parkinson's disease (PD) symptoms, we developed a multiscale computational model that can replicate the symptoms at the behavioural level by incorporating the key cellular and molecular mechanisms underlying PD pathology. There is a modelling tradition that links dopamine to reward and uses reinforcement learning (RL) concepts to model the basal ganglia. In our model, we replace the abstract representations of reward with the realistic variable of extracellular DA released by a network of SNc cells and incorporate it in the RL-based behavioural model, which simulates the arm reaching task. Our results successfully replicated the impact of SNc cell loss and levodopa (L-DOPA) medication on reaching performance. It also shows the side effects of medication, such as wearing off and peak dosage dyskinesias. The model demonstrates how differential dopaminergic axonal degeneration in basal ganglia results in various cardinal symptoms of PD. It was able to predict the optimum L-DOPA medication dosage for varying degrees of cell loss. The proposed model has a potential clinical application where drug dosage can be optimised as per patient characteristics.

The amplitude of low-frequency fluctuation predicts levodopa treatment response in patients with Parkinson's disease

INTRODUCTION

Levodopa has become the main therapy for motor symptoms of Parkinson's disease (PD). This study aimed to test whether the amplitude of low-frequency fluctuation (ALFF) computed by fMRI could predict individual patient's response to levodopa treatment.

METHODS

We included 40 patients. Treatment efficacy was defined based on motor symptoms improvement from the state of medication off to medication on, as assessed by the Unified Parkinson's Disease Rating Scale score III. Two machine learning models were constructed to test the prediction ability of ALFF. First, the ensemble method was implemented to predict individual treatment responses. Second, the categorical boosting (CatBoost) classification was used to predict individual levodopa responses in patients classified as moderate and superior responders, according to the 50% threshold of improvement. The age, disease duration and treatment dose were controlled as covariates.

RESULTS

No significant difference in clinical data were observed between moderate and superior responders. Using the ensemble method, the regression model showed a significant correlation between the predicted and the observed motor symptoms improvement (r = 0.61, p < 0.01, mean absolute error = 0.11 ± 0.02), measured as a continuous variable. The use of the Catboost algorithm revealed that ALFF was able to differentiate between moderate and superior responders (area under the curve = 0.90). The mainly contributed regions for both models included the bilateral primary motor cortex, the occipital cortex, the cerebellum, and the basal ganglia.

CONCLUSION

Both continuous and binary ALFF values have the potential to serve as promising predictive markers of dopaminergic therapy response in patients with PD.

Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study

Background

Emerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD.

Objective

To investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD.

Methods

Thirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1–3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone.

Results

For the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42).

Conclusion

Our findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.