Anterior cingulate dopamine turnover and behavior change in Parkinson's disease

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson's disease

BACKGROUND

The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response.

METHODS

This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001.

RESULTS

For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized.

CONCLUSION

The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction.

CRITICAL RELEVANCE STATEMENT

This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy.

KEY POINTS

This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction.

Investigating the Shared Genetic Etiology Between Parkinson's Disease and Depression

Background

Depression is a common symptom in Parkinson's disease (PD), resulting from underlying neuropathological processes and psychological factors. However, the extent to which shared genetic risk factors contribute to the relationship between depression and PD is poorly understood.

Objective

To examine the effects of common genetic variants influencing the etiology of PD and depression risk at the genome-wide and local genomic regional level.

Methods

We comprehensively investigated the genetic relationship between PD and depression using genome-wide association studies data. First, we estimated the genetic correlation at the genome-wide level using linkage-disequilibrium score regression, followed by local genetic correlation analysis using the GWAS-pairwise method and functional annotation to identify genes that may jointly influence the risk for both traits. Also, we performed Latent Causal Variable, Latent Heritable Confounder Mendelian Randomization, and traditional Mendelian Randomization analyses to investigate the potential causal relationship.

Results

Although the genetic correlation between PD and depression was not statistically significant at the genome-wide level, GWAS-pairwise analyses identified 16 genomic segments associated with PD and depression, implicating nine genes. Further analyses revealed distinct patterns within individual genes, suggesting an intricate pattern. These genes involve various biological processes, including neurotransmitter regulation, senescence, and nucleo-cytoplasmic transport mechanisms. We did not observe genetic evidence of causality between PD and depression.

Conclusions

Our findings did not support a genome-wide genetic correlation or a causal association between both conditions. However, we identified genomic segments but identified genomic segments linked to distinct biological pathways influencing their etiology.Further research is needed to understand their functional consequences.

The Intranigral Infusion of Human-Alpha Synuclein Oligomers Induces a Cognitive Impairment in Rats Associated with Changes in Neuronal Firing and Neuroinflammation in the Anterior Cingulate Cortex

Parkinson’s disease (PD) is a complex pathology causing a plethora of non-motor symptoms besides classical motor impairments, including cognitive disturbances. Recent studies in the PD human brain have reported microgliosis in limbic and neocortical structures, suggesting a role for neuroinflammation in the development of cognitive decline. Yet, the mechanism underlying the cognitive pathology is under investigated, mainly for the lack of a valid preclinical neuropathological model reproducing the disease’s motor and non-motor aspects. Here, we show that the bilateral intracerebral infusion of pre-formed human alpha synuclein oligomers (H-αSynOs) within the substantia nigra pars compacta (SNpc) offers a valid model for studying the cognitive symptoms of PD, which adds to the classical motor aspects previously described in the same model. Indeed, H-αSynOs-infused rats displayed memory deficits in the two-trial recognition task in a Y maze and the novel object recognition (NOR) test performed three months after the oligomer infusion. In the anterior cingulate cortex (ACC) of H-αSynOs-infused rats the in vivo electrophysiological activity was altered and the expression of the neuron-specific immediate early gene (IEG) Npas4 (Neuronal PAS domain protein 4) and the AMPA receptor subunit GluR1 were decreased. The histological analysis of the brain of cognitively impaired rats showed a neuroinflammatory response in cognition-related regions such as the ACC and discrete subareas of the hippocampus, in the absence of any evident neuronal loss, supporting a role of neuroinflammation in cognitive decline. We found an increased GFAP reactivity and the acquisition of a proinflammatory phenotype by microglia, as indicated by the increased levels of microglial Tumor Necrosis Factor alpha (TNF-α) as compared to vehicle-infused rats. Moreover, diffused deposits of phospho-alpha synuclein (p-αSyn) and Lewy neurite-like aggregates were found in the SNpc and striatum, suggesting the spreading of toxic protein within anatomically interconnected areas. Altogether, we present a neuropathological rat model of PD that is relevant for the study of cognitive dysfunction featuring the disease. The intranigral infusion of toxic oligomeric species of alpha-synuclein (α-Syn) induced spreading and neuroinflammation in distant cognition-relevant regions, which may drive the altered neuronal activity underlying cognitive deficits.

Current concepts in treating mild cognitive impairment in Parkinson's disease

Impairment in various aspects of cognition is recognized as an important non-motor symptom of Parkinson's disease (PD). Mild cognitive impairment in PD (PD-MCI) is common in non-demented PD patients and is often associated with severity of motor symptoms, disease duration and increasing age. Further, PD-MCI can have a significant negative effect on performance of daily life activities and may be a harbinger of development of PD dementia. Thus, there is significant interest in developing therapeutic strategies to ameliorate cognitive deficits in PD and improve cognitive functioning of PD patients. However, due to significant questions that remain regarding the pathophysiology of cognitive dysfunction in PD, remediation of cognitive dysfunction in PD has proven difficult. In this paper, we will focus on PD-MCI and will review some of the current therapeutic approaches being taken to try to improve cognitive functioning in patients with PD-MCI.

Synaptic potentiation of anterior cingulate cortex contributes to chronic pain of Parkinson's disease

Parkinson’s disease (PD) is a multi-system neurodegenerative disorder. Patients with PD often suffer chronic pain. In the present study, we investigated motor, sensory and emotional changes in three different PD mice models. We found that 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treatment caused significant changes in all measurements. Mechanical hypersensitivity of PD model induced by MPTP peaked at 3 days and persisted for at least 14 days. Using Fos transgenic mice, we found that neurons in the anterior cingulate cortex (ACC) were activated after MPTP treatment. Inhibiting ACC by bilateral microinjection of muscimol significantly reduced mechanical hypersensitivity and anxiety-like responses. By contrast, MPTP induced motor deficit was not affected, indicating ACC activity is mostly responsible for sensory and emotional changes. We also investigated excitatory synaptic transmission and plasticity using brain slices of MPTP treated animals. While L-LTP was blocked or significantly reduced. E-LTP was not significantly affected in slices of MPTP treated animals. LTD induced by repetitive stimulation was not affected. Furthermore, we found that paired-pulse facilitation and spontaneous release of glutamate were also altered in MPTP treated animals, suggesting presynaptic enhancement of excitatory transmission in PD. Our results suggest that ACC synaptic transmission is enhanced in the animal model of PD, and cortical excitation may play important roles in PD related pain and anxiety.

Novel, non-nitrocatechol catechol-O-methyltransferase inhibitors modulate dopamine neurotransmission in the frontal cortex and improve cognitive flexibility

RATIONALE

Cognitive impairment is a primary feature of many neuropsychiatric disorders and there is a need for new therapeutic options. Catechol-O-methyltransferase (COMT) inhibitors modulate cortical dopaminergic function and have been proposed as potential cognitive enhancers. Unfortunately, currently available COMT inhibitors are not good candidates due to either poor blood-brain barrier penetration or severe toxicity.

OBJECTIVES

To address the need for safe, brain-penetrant COMT inhibitors, we tested multiple novel compounds in a set of preclinical in vivo efficacy assays in rats to determine their ability to inhibit COMT function and viability as potential clinical candidates.

METHODS

We measured the change in concentration of dopamine (DA) metabolites in cerebrospinal fluid (CSF) from the cisterna magna and extracellular fluid (ECF) from the frontal cortex produced by our novel compounds. Additionally, we tested the effects of our brain-penetrant COMT inhibitors in an attentional set-shifting assay (ASST). We benchmarked the performance of the novel COMT inhibitors to the effects produced by the known COMT inhibitor tolcapone.

RESULTS

We found that multiple COMT inhibitors, exemplified by LIBD-1 and LIBD-3, significantly modulated dopaminergic function measured as decreases in homovanillic acid (HVA) and increases in 3,4-Dihydroxyphenylacetic acid (DOPAC), two DA metabolites, in CSF and the frontal cortex. Additionally, we found that LIBD-1 significantly improved cognitive flexibility in the ASST, an effect previously reported following tolcapone administration.

CONCLUSIONS

These results demonstrate that LIBD-1 is a novel COMT inhibitor with promising in vivo activity and the potential to serve as a new therapy for cognitive impairment.

Local White Matter Fiber Clustering Differentiates Parkinson's Disease Diagnoses

Scans without evidence of dopaminergic deficit (SWEDD) patients are often misdiagnosed with Parkinson's disease (PD) but have normal dopamine transporter scans. We hypothesised that white matter tracts associated with motor and cognition functions may be affected differently by SWEDD and PD. Automatically annotated fibre clustering (AAFC) is a novel clustering method based on diffusion magnetic resonance imaging (dMRI) tractography that enables highly robust reconstruction of white matter tracts that are composed of corresponding clusters. This study aimed to investigate the white matter properties in the subdivisions of white matter tracts among SWEDD and PD groups. We applied AAFC to identify white matter tracts related to motion and cognition functions in the dataset consisting of SWEDD (n = 22), PD (n = 30) and normal control (NC) (n = 30). Then, we resampled 200 nodes along fibres of cluster, and the diffusion metric values corresponding to each node were calculated and used for comparison. Compared with NC, PD showed significant difference (p < 0.05) in two clusters in thalamo-frontal (TF), one cluster in thalamo-parietal (TP) and one cluster in thalamo-occipital (TO), whereas SWEDD presented no significant difference. Three clusters in cingulum bundle (CB) commonly exhibited significant differences in PD versus SWEDD and NC versus SWEDD. The support vector machine classifier achieved high accuracies in PD-NC, PD-SWEDD and NC-SWEDD classifications. This outcome validated these local white matter differences were useful to separate the three groups. These results suggest that PD exerts more significant effects on thalamo tracts than SWEDD, and unique microstructural changes occur in CB tract in SWEDD.

Neurophysiological Biomarkers of Parkinson's Disease

BACKGROUND

There is a need for reliable and robust Parkinson's disease biomarkers that reflect severity and are sensitive to disease modifying investigational therapeutics.

OBJECTIVE

To demonstrate the utility of EEG as a reliable, quantitative biomarker with potential as a pharmacodynamic endpoint for use in clinical assessments of neuroprotective therapeutics for Parkison's disease.

METHODS

A multi modal study was performed including aquisition of resting state EEG data and dopamine transporter PET imaging from Parkinson's disease patients off medication and compared against age-matched controls.

RESULTS

Qualitative and test/retest analysis of the EEG data demonstrated the reliability of the methods. Source localization using low resolution brain electromagnetic tomography identified significant differences in Parkinson's patients versus control subjects in the anterior cingulate and temporal lobe, areas with established association to Parkinson's disease pathology. Changes in cortico-cortical and cortico-thalamic coupling were observed as excessive EEG beta coherence in Parkinson's disease patients, and correlated with UPDRS scores and dopamine transporter activity, supporting the potential for cortical EEG coherence to serve as a reliable measure of disease severity. Using machine learning approaches, an EEG discriminant function analysis classifier was identified that parallels the loss of dopamine synapses as measured by dopamine transporter PET.

CONCLUSION

Our results support the utility of EEG in characterizing alterations in neurophysiological oscillatory activity associated with Parkinson's disease and highlight potential as a reliable method for monitoring disease progression and as a pharmacodynamic endpoint for Parkinson's disease modification therapy.

Thalamocortical dysrhythmia detected by machine learning

Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern in which resting-state alpha activity is replaced by cross-frequency coupling of low- and high-frequency oscillations. We undertook a data-driven approach using support vector machine learning for analyzing resting-state electroencephalography oscillatory patterns in patients with Parkinson’s disease, neuropathic pain, tinnitus, and depression. We show a spectrally equivalent but spatially distinct form of TCD that depends on the specific disorder. However, we also identify brain areas that are common to the pathology of Parkinson’s disease, pain, tinnitus, and depression. This study therefore supports the validity of TCD as an oscillatory mechanism underlying diverse neurological disorders.

Thalamocortical dysrhythmia has been proposed to occur in a number of neurological and psychiatric disorders. Here, the authors use a data-driven approach to demonstrate thalamocortical dysrhythmia occurs in individuals with Parkinson’s disease, neuropathic pain, tinnitus, and depression.

Dopamine, time perception, and future time perspective

RATIONALE

Impairment in time perception, a critical component of decision-making, represents a risk factor for psychiatric conditions including substance abuse. A therapeutic that ameliorates this impairment could be advantageous in the treatment of impulsivity and decision-making disorders.

OBJECTIVES

Here we hypothesize that the catechol-O-methyltransferase (COMT) inhibitor tolcapone, which increases dopamine tone in frontal cortex (Ceravolo et al Synapse 43:201-207, 2002), improves time perception, with predictive behavioral, genetic, and neurobiological components.

METHODS

Subjects (n = 66) completed a duration estimation task and other behavioral testing in each of two sessions after receiving a single oral dose of tolcapone (200 mg) or placebo in randomized, double-blind, counterbalanced, crossover fashion. Resting state fMRI data were obtained in a subset of subjects (n = 40). Subjects were also genotyped for the COMT (rs4680) polymorphism.

RESULTS

Time perception was significantly improved across four proximal time points ranging from 5 to 60 s (T(524) = 2.04, p = 0.042). The degree of this improvement positively correlated with subjective measures of stress, depression, and alcohol consumption and was most robust in carriers of the COMT Val158 allele. Using seed regions defined by a previous meta-analysis (Wiener et al Neuroimage 49:1728-1740, 2010), we found not only that a connection from right inferior frontal gyrus (RIFG) to right putamen decreases in strength on tolcapone versus placebo (p < 0.05, corrected), but also that the strength of this decrease correlates inversely with the increase in duration estimation on tolcapone versus placebo (r = - 0.37, p = 0.02).

CONCLUSIONS

Compressed time perception can be ameliorated by administration of tolcapone. Additional studies should be conducted to determine whether COMT inhibitors may be effective in treating decision-making disorders and addictive behaviors.

Dopaminergic correlates of metabolic network activity in Parkinson's disease

Parkinson's disease (PD) is associated with distinct metabolic covariance patterns that relate to the motor and cognitive manifestations of the disorder. It is not known, however, how the expression of these patterns relates to measurements of nigrostriatal dopaminergic activity from the same individuals. To explore these associations, we studied 106 PD subjects who underwent cerebral PET with both (18) F-fluorodeoxyglucose (FDG) and (18) F-fluoro-L-dopa (FDOPA). Expression values for the PD motor- and cognition-related metabolic patterns (PDRP and PDCP, respectively) were computed for each subject; these measures were correlated with FDOPA uptake on a voxel-by-voxel basis. To explore the relationship between dopaminergic function and local metabolic activity, caudate and putamen FDOPA PET signal was correlated voxel-wise with FDG uptake over the entire brain. PDRP expression correlated with FDOPA uptake in caudate and putamen (P < 0.001), while PDCP expression correlated with uptake in the anterior striatum (P < 0.001). While statistically significant, the correlations were only of modest size, accounting for less than 20% of the overall variation in these measures. After controlling for PDCP expression, PDRP correlations were significant only in the posterior putamen. Of note, voxel-wise correlations between caudate/putamen FDOPA uptake and whole-brain FDG uptake were significant almost exclusively in PDRP regions. Overall, the data indicate that PDRP and PDCP expression correlates significantly with PET indices of presynaptic dopaminergic functioning obtained in the same individuals. Even so, the modest size of these correlations suggests that in PD patients, individual differences in network activity cannot be explained solely by nigrostriatal dopamine loss.