Impaired functional connectivity of sensorimotor network predicts recovery in drug-induced parkinsonism

Genetic analyses identify brain functional networks associated with the risk of Parkinson's disease and drug-induced parkinsonism

Brain functional networks are associated with parkinsonism in observational studies. However, the causal effects between brain functional networks and parkinsonism remain unclear. We aimed to assess the potential bidirectional causal associations between 191 brain resting-state functional magnetic resonance imaging (rsfMRI) phenotypes and parkinsonism including Parkinson's disease (PD) and drug-induced parkinsonism (DIP). We used Mendelian randomization (MR) to assess the bidirectional associations between brain rsfMRI phenotypes and parkinsonism, followed by several sensitivity analyses for robustness validation. In the forward MR analyses, we found that three rsfMRI phenotypes genetically determined the risk of parkinsonism. The connectivity in the visual network decreased the risk of PD (OR = 0.391, 95% CI = 0.235 ~ 0.649, P = 2.83 × 10-4, P_FDR = 0.039). The connectivity of salience and motor networks increased the risk of DIP (OR = 4.102, 95% CI = 1.903 ~ 8.845, P = 3.17 × 10-4, P_FDR = 0.044). The connectivity of limbic and default mode networks increased the risk of DIP (OR = 14.526, 95% CI = 3.130 ~ 67.408, P = 6.32 × 10-4, P_FDR = 0.0437). The reverse MR analysis indicated that PD and DIP had no effect on brain rsfMRI phenotypes. Our findings reveal causal relationships between brain functional networks and parkinsonism, providing important interventional and therapeutic targets for different parkinsonism.

Low-frequency EEG power and coherence differ between drug-induced parkinsonism and Parkinson's disease

OBJECTIVE

Drug-induced parkinsonism (DIP) ranks second to Parkinson's disease (PD) in causing parkinsonism. Despite sharing similar symptoms, DIP results from exposure to specific medications or substances, underscoring the need for accurate diagnosis. Here, we used resting-state electroencephalography (rsEEG) to investigate neural markers characterizing DIP and PD.

METHODS

We conducted a retrospective analysis of rsEEG recordings from 18 DIP patients, 43 de novo PD patients, and 12 healthy controls (HC). After exclusions, data from 15 DIP, 41 PD, and 12 HC participants were analyzed. EEG spectral power and inter-channel coherence were compared across the groups.

RESULTS

Our results demonstrated significant differences in rsEEG patterns among DIP, PD, and HC groups. DIP patients exhibited increased theta band power compared with PD patients and HC. Moreover, DIP patients showed higher delta band coherence compared with PD patients.

CONCLUSION

The current study highlights the differences in EEG spectral power and inter-channel coherence between DIP and PD patients.

SIGNIFICANCE

Our results suggest that rsEEG holds promise as a valuable tool for capturing differential characteristics between DIP and PD patients.

Comprehensive Analysis of Drug-Induced Parkinson-like Events

Specific drugs are well known to have the capacity to induce Parkinson-like symptoms. Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Pharmaceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with the associated drug trends and characteristics. Our findings indicate that similar to Parkinson's disease, age-related differences affect the onset of these reported events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, the side effects can vary depending on the site of action. These insights underline the importance of the swift identification of the drugs suspected of causing these events and the implementation of measures to reduce their side effects.

Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring

BACKGROUND

Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded.

PURPOSE

To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD.

STUDY TYPE

Prospective.

SUBJECTS

Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female).

FIELD STRENGTH/SEQUENCE

3-T, T1-weighted gradient echo and EPI sequences.

ASSESSMENT

Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings.

STATISTICAL TESTS

Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05.

RESULTS

With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline.

DATA CONCLUSION

The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Retinal Thickness and Its Interocular Asymmetry Between Parkinson's Disease and Drug-Induced Parkinsonism

BACKGROUND

Drug-induced parkinsonism (DIP) is common, but diagnosis is challenging. Although dopamine transporter imaging is useful, the cost and inconvenience are problematic, and an easily accessible screening technique is needed. We aimed to determine whether optical coherence tomography (OCT) findings could differentiate DIP from Parkinson's disease (PD).

METHODS

We investigated 97 de novo PD patients and 27 DIP patients using OCT and [¹⁸F] N-(3-fluoropropyl)-2b-carbon ethoxy-3b-(4-iodophenyl) nortropane (FP-CIT) positron emission tomography. We compared peripapillary retinal nerve fiber layer thickness (pRNFLT) and macular retinal thickness (mRT) between PD and DIP patients as well as interocular differences in the pRNFLT and the mRT. Asymmetric index (%) for retinal thickness (AIRT) was calculated to measure the interocular differences between pRNFLT and mRT. The correlation between AIRT and total striatal specific/non-specific binding ratio asymmetry index (SNBRAI) was investigated in PD and DIP patients.

RESULTS

No significant differences in pRNFLT and mRT values were observed between PD and DIP patients (all P values > 0.090). The mean SNBRAI was significantly higher in PD than in DIP (P = 0.008) patients; however, AIRT did not differ between PD and DIP patients in pRNFLT and mRT (all P values > 0.100). SNBRAI did not correlate with AIRT of pRNFL or mRT in PD and DIP patients (all P values > 0.060).

CONCLUSION

Our study showed no benefit of retinal thickness and interocular asymmetry measurements using OCT for distinguishing PD from DIP in the early stages. Additional investigations are needed for confirmation.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

Recognition and Management of Antipsychotic-Induced Parkinsonism in Older Adults: A Narrative Review

Background: Parkinsonism is a common side-effect of antipsychotic drugs especially in older adults, who also present with a higher frequency of neurodegenerative disorders like Idiopathic Parkinson’s disease (IPD). Distinguishing between antipsychotic-induced parkinsonism (AIP) and IPD is challenging due to clinical similarities. Up to 20% of older adults may suffer from persisting parkinsonism months after discontinuation of antipsychotics, suggesting underlying neurodegeneration. A review of the literature on AIP in older adults is presented, focusing on epidemiology, clinical aspects, and management. Methods: A literature search was undertaken on EMBASE, MEDLINE and PsycINFO, for articles on parkinsonism induced by antipsychotic drugs or other dopamine 2 receptor antagonists in subjects aged 65 or older. Results: AIP in older adults is the second most common cause of parkinsonism after IPD. Older age, female gender, exposure to high-potency first generation antipsychotics, and antipsychotic dosage are the main risk factors. The clinical presentation of AIP resembles that of IPD, but is more symmetrical, affects upper limbs more, and tends to have associated motor phenomena such as orofacial dyskinesias and akathisia. Presence of olfactory dysfunction in AIP suggests neurodegeneration. Imaging of striatal dopamine transporters is widely used in IPD diagnosis and could help to distinguish it from AIP. There is little evidence base for recommending pharmacological interventions for AIP, the best options being dose-reduction/withdrawal, or switching to a second-generation drug. Conclusions: AIP is a common occurrence in older adults and it is possible to differentiate it from IPD. Further research is needed into its pathophysiology and on its treatment.

Integrating Structural and Functional Interhemispheric Brain Connectivity of Gait Freezing in Parkinson's Disease

Freezing of gait (FOG) has devastating consequences for patients with Parkinson's disease (PD), but the underlying pathophysiological mechanism is unclear. This was investigated in the present study by integrated structural and functional connectivity analyses of PD patients with or without FOG (PD FOG+ and PD FOG-, respectively) and healthy control (HC) subjects. We performed resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging of 24 PD FOG+ patients, 37 PD FOG- patients, and 24 HCs. Tract-based spatial statistics was applied to identify white matter (WM) abnormalities across the whole brain. Fractional anisotropy (FA) and mean diffusivity (MD) of abnormal WM areas were compared among groups, and correlations between these parameters and clinical severity as determined by FOG Questionnaire (FOGQ) score were analyzed. Voxel-mirrored homotopic connectivity (VMHC) was calculated to identify brain regions with abnormal interhemispheric connectivity. Structural and functional measures were integrated by calculating correlations between VMHC and FOGQ score and between FA, MD, and VMHC. The results showed that PD FOG+ and PD FOG- patients had decreased FA in the corpus callosum (CC), cingulum (hippocampus), and superior longitudinal fasciculus and increased MD in the CC, internal capsule, corona radiata, superior longitudinal fasciculus, and thalamus. PD FOG+ patients had more WM abnormalities than PD FOG- patients. FA and MD differed significantly among the splenium, body, and genu of the CC in all three groups (P < 0.05). The decreased FA in the CC was positively correlated with FOGQ score. PD FOG+ patients showed decreased VMHC in the post-central gyrus (PCG), pre-central gyrus, and parietal inferior margin. In PD FOG+ patients, VMHC in the PCG was negatively correlated with FOGQ score but positively correlated with FA in CC. Thus, FOG is associated with impaired interhemispheric brain connectivity measured by FA, MD, and VMHC, which are related to clinical FOG severity. These results demonstrate that integrating structural and functional MRI data can provide new insight into the pathophysiological mechanism of FOG in PD.