The role of neuroimaging in Parkinson's disease

Biomarkers of Parkinson's disease in perspective of early diagnosis and translation of neurotrophic therapies

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by progressive loss of dopamine neurons and aberrant deposits of alpha-synuclein (α-syn) in the brain. The symptomatic treatment is started after the onset of motor manifestations in a late stage of the disease. Preclinical studies with neurotrophic factors (NTFs) show promising results of disease-modifying neuroprotective or even neurorestorative effects. Four NTFs have entered phase I-II clinical trials with inconclusive outcomes. This is not surprising because the preclinical evidence is from acute early-stage disease models, but the clinical trials included advanced PD patients. To conclude the value of NTF therapies, clinical studies should be performed in early-stage patients with prodromal symptoms, that is, before motor manifestations. In this review, we summarize currently available diagnostic and prognostic biomarkers that could help identify at-risk patients benefiting from NTF therapies. Focus is on biochemical and imaging biomarkers, but also other modalities are discussed. Neuroimaging is the most important diagnostic tool today, but α-syn imaging is not yet viable. Modern techniques allow measuring various forms of α-syn in cerebrospinal fluid, blood, saliva, and skin. Digital biomarkers and artificial intelligence offer new means for early diagnosis and longitudinal follow-up of degenerative brain diseases.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study

BACKGROUND AND PURPOSE

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia.

MATERIALS AND METHODS

Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed.

RESULTS

Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment.

CONCLUSIONS

The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Parkinson's image detection and classification based on deep learning

OBJECTIVE

There are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images between healthy individuals and Parkinson's patients, and the medical field has not yet established precise lesion localization standards, which poses a huge challenge for the effective prediction of Parkinson's disease through MRI images. Secondly, the early diagnosis of Parkinson's disease traditionally relies on the subjective judgment of doctors, which leads to insufficient accuracy and consistency. This article proposes an improved YOLOv5 detection algorithm based on deep learning for predicting and classifying Parkinson's images.

METHODS

This article improves the YOLOv5s network as the basic framework. Firstly, the CA attention mechanism was introduced to enable the model to dynamically adjust attention based on local features of the image, significantly enhancing the sensitivity of the model to PD related small pathological features; Secondly, replace the dynamic full dimensional convolution module to optimize the multi-level extraction of image features; Finally, the coupling head strategy is adopted to improve the execution efficiency of classification and localization tasks separately.

RESULTS

We validated the effectiveness of the proposed method using a dataset of 582 MRI images from 108 patients. The results show that the proposed method achieves 0.961, 0.974, and 0.986 in Precision, Recall, and mAP, respectively, and the experimental results are superior to other algorithms.

CONSLUSION

The improved model has achieved high accuracy and detection accuracy, and can accurately detect and recognize complex Parkinson's MRI images.

SIGNIFICANCE

This algorithm has shown good performance in the early diagnosis of Parkinson's disease and can provide clinical assistance for doctors in early diagnosis. It compensates for the limitations of traditional methods.

Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia

PURPOSE OF THE REVIEW

In this review, we attempt to summarize the most updated studies that applied resting-state functional magnetic resonance imaging (rs-fMRI) in the field of Parkinsonisms and related dementia.

RECENT FINDINGS

Over the past decades, increasing interest has emerged on investigating the presence and pathophysiology of cognitive symptoms in Parkinsonisms and their possible role as predictive biomarkers of neurodegenerative brain processes. In recent years, evidence has been provided, applying mainly three methodological approaches (i.e. seed-based, network-based and graph-analysis) on rs-fMRI data, with promising results. Neural correlates of cognitive impairment and dementia have been detected in patients with Parkinsonisms along the diseases course. Interestingly, early functional connectivity signatures were proposed to track and predict future progression of neurodegenerative processes. However, longitudinal studies are still sparce and further investigations are needed to overcome this knowledge gap.

Exploring the complexity of EEG patterns in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.

Modulation of ER-mitochondria tethering complex VAPB-PTPIP51: Novel therapeutic targets for aging-associated diseases

Aging is a gradual and irreversible natural process. With aging, the body experiences a functional decline, and the effects amplify the vulnerability to a range of age-related diseases, including neurodegenerative, cardiovascular, and metabolic diseases. Within the aging process, the morphology and function of mitochondria and the endoplasmic reticulum (ER) undergo alterations, particularly in the structure connecting these organelles known as mitochondria-associated membranes (MAMs). MAMs serve as vital intracellular signaling hubs, facilitating communication between the ER and mitochondria when regulating various cellular events, including calcium homeostasis, lipid metabolism, mitochondrial function, and apoptosis. The formation of MAMs is partly dependent on the interaction between the vesicle-associated membrane protein-associated protein-B (VAPB) and protein tyrosine phosphatase-interacting protein-51 (PTPIP51). Accumulating evidence has begun to elucidate the pivotal role of the VAPB-PTPIP51 tether in the initiation and progression of age-related diseases. In this study, we delineate the intricate structure and multifunctional role of the VAPB-PTPIP51 tether and discuss its profound implications in aging-associated diseases. Moreover, we provide a comprehensive overview of potential therapeutic interventions and pharmacological agents targeting the VAPB-PTPIP51-mediated MAMs, thereby offering a glimmer of hope in mitigating aging processes and treating age-related disorders.

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease

BACKGROUND

Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits.

METHODS

We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities.

RESULTS

Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected).

CONCLUSIONS

Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study

OBJECTIVE

To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD).

METHODS

This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model.

RESULTS

The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups.

CONCLUSIONS

The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

How close are we to a breakthrough? The hunt for blood biomarkers in Parkinson's disease diagnosis

Parkinson's disease (PD), being the second largest neurodegenerative disease, poses challenges in early detection, resulting in a lack of timely treatment options to effectively manage the disease. By the time clinical diagnosis becomes possible, more than 60% of dopamine neurons in the substantia nigra (SN) of patients have already degenerated. Therefore, early diagnosis or identification of warning signs is crucial for the prompt and timely beginning of the treatment. However, conducting invasive or complex diagnostic procedures on asymptomatic patients can be challenging, making routine blood tests a more feasible approach in such cases. Numerous studies have been conducted over an extended period to search for effective diagnostic biomarkers in blood samples. However, thus far, no highly effective biomarkers have been confirmed. Besides classical proteins like α-synuclein (α-syn), phosphorylated α-syn and oligomeric α-syn, other molecules involved in disease progression should also be given equal attention. In this review, we will not only discuss proposed biomarkers that are currently under investigation but also delve into the mechanisms underlying the disease, focusing on processes such as α-syn misfolding, intercellular transmission and the crossing of the blood-brain barrier (BBB). Our aim is to provide an updated overview of molecules based on these processes that may potentially serve as blood biomarkers.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease

BACKGROUNDS

Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context.

OBJECTIVES

Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD.

METHODS

This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter.

FINDINGS

This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD.

CONCLUSION

The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

11C-CFT PET brain imaging in Parkinson's disease using a total-body PET/CT scanner

PURPOSE

This study aimed to evaluate the feasibility of 11C-CFT PET brain imaging in Parkinson's Disease using a total-body PET/CT scanner and explore the optimal scan duration to guide the clinical practice.

METHODS

Thirty-two patients with Parkinson's disease (PD) performing 11C-CFT PET/CT brain imaging using a total-body PET/CT scanner were retrospectively enrolled. The PET data acquired over a period of 900 s were reconstructed into groups of different durations: 900-s, 720-s, 600-s, 480-s, 300-s, 180-s, 120-s, 60-s, and 30-s (G900 to G30). The subjective image quality analysis was performed using 5-point scales. Semi-quantitative measurements were analyzed by SUVmean and dopamine transporter (DAT) binding of key brain regions implicated in PD, including the caudate nucleus and putamen. The full-time images (G900) were served as reference.

RESULTS

The overall G900, G720, and G600 image quality scores were 5.0 ± 0.0, 5.0 ± 0.0, and 4.9 ± 0.3 points, respectively, and there was no significant difference among these groups (P > 0.05). A significant decrease in these scores at durations shorter than 600 s was observed when compared to G900 images (P < 0.05). However, all G300 image quality was clinically acceptable (≥ 3 points). As the scan duration reduced, the SUVmean and DAT binding of caudate nucleus and putamen decreased progressively, while there were no statistically significant variations in the SUVmean of the background among the different groups. Moreover, the changes in the lesion DAT binding (ΔDAT-binding) between the full-time reference G900 image and other reconstructed group G720 to G30 images generally increased along with the reduced scan time.

CONCLUSION

Sufficient image quality and lesion conspicuity could be achieved at 600-s scan duration for 11C-CFT PET brain imaging in PD assessment using a total-body PET/CT scanner, while the image quality of G300 was acceptable to meet clinical diagnosis, contributing to improve patient compliance and throughput of PET brain imaging.

Parkinson's disease cerebrovascular reactivity pattern: A feasibility study

A mounting body of research points to cerebrovascular dysfunction as a fundamental element in the pathophysiology of Parkinson's disease (PD). In the current feasibility study, blood-oxygen-level-dependent (BOLD) MRI was used to measure cerebrovascular reactivity (CVR) in response to hypercapnia in 26 PD patients and 16 healthy controls (HC), and aimed to find a multivariate pattern specific to PD. Whole-brain maps of CVR amplitude (i.e., magnitude of response to CO2) and latency (i.e., time to reach maximum amplitude) were computed, which were further analyzed using scaled sub-profile model principal component analysis (SSM-PCA) with leave-one-out cross-validation. A meaningful pattern based on CVR latency was identified, which was named the PD CVR pattern (PD-CVRP). This pattern was characterized by relatively increased latency in basal ganglia, sensorimotor cortex, supplementary motor area, thalamus and visual cortex, as well as decreased latency in the cerebral white matter, relative to HC. There were no significant associations with clinical measures, though sample size may have limited our ability to detect significant associations. In summary, the PD-CVRP highlights the importance of cerebrovascular dysfunction in PD, and may be a potential biomarker for future clinical research and practice.

Positive relation between dopamine neuron degeneration and metabolic connectivity disruption in the MPTP plus probenecid mouse model of Parkinson's disease

The clinical manifestation of Parkinson's disease (PD) appears when neurodegeneration is already advanced, compromising the efficacy of disease-modifying treatment approaches. Biomarkers to identify the early stages of PD are therefore of paramount importance for the advancement of the therapy of PD. In the present study, by using a mouse model of PD obtained by subchronic treatment with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and the clearance inhibitor probenecid (MPTPp), we identified prodromal markers of PD by combining in vivo positron emission tomography (PET) imaging and ex vivo immunohistochemistry. Longitudinal PET imaging of the dopamine transporter (DAT) by [18F]-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ([18F]-FP-CIT), and brain glucose metabolism by 2-deoxy-2-[18F]-fluoroglucose ([18F]-FDG) were performed before MPTPp treatment and after 1, 3, and 10 MPTPp administrations, in order to assess relation between dopamine neuron integrity and brain connectivity. The results show that in vivo [18F]-FP-CIT in the dorsal striatum was not modified after the first administration of MPTPp, tended to decrease after 3 administrations, and significantly decreased after 10 MPTPp administrations. Post-mortem immunohistochemical analyses of DAT and tyrosine hydroxylase (TH) in the striatum showed a positive correlation with [18F]-FP-CIT, confirming the validity of repeated MPTPp-treated mice as a model that can reproduce the progressive pathological changes in the early phases of PD. Analysis of [18F]-FDG uptake in several brain areas connected to the striatum showed that metabolic connectivity was progressively disrupted, starting from the first MPTPp administration, and that significant connections between cortical and subcortical regions were lost after 10 MPTPp administrations, suggesting an association between dopamine neuron degeneration and connectivity disruption in this PD model. The results of this study provide a relevant model, where new drugs that can alleviate neurodegeneration in PD could be evaluated preclinically.

A biological definition of neuronal α-synuclein disease: towards an integrated staging system for research

Parkinson's disease and dementia with Lewy bodies are currently defined by their clinical features, with α-synuclein pathology as the gold standard to establish the definitive diagnosis. We propose that, given biomarker advances enabling accurate detection of pathological α-synuclein (ie, misfolded and aggregated) in CSF using the seed amplification assay, it is time to redefine Parkinson's disease and dementia with Lewy bodies as neuronal α-synuclein disease rather than as clinical syndromes. This major shift from a clinical to a biological definition of Parkinson's disease and dementia with Lewy bodies takes advantage of the availability of tools to assess the gold standard for diagnosis of neuronal α-synuclein (n-αsyn) in human beings during life. Neuronal α-synuclein disease is defined by the presence of pathological n-αsyn species detected in vivo (S; the first biological anchor) regardless of the presence of any specific clinical syndrome. On the basis of this definition, we propose that individuals with pathological n-αsyn aggregates are at risk for dopaminergic neuronal dysfunction (D; the second biological anchor). Our biological definition establishes a staging system, the neuronal α-synuclein disease integrated staging system (NSD-ISS), rooted in the biological anchors (S and D) and the degree of functional impairment caused by clinical signs or symptoms. Stages 0-1 occur without signs or symptoms and are defined by the presence of pathogenic variants in the SNCA gene (stage 0), S alone (stage 1A), or S and D (stage 1B). The presence of clinical manifestations marks the transition to stage 2 and beyond. Stage 2 is characterised by subtle signs or symptoms but without functional impairment. Stages 2B-6 require both S and D and stage-specific increases in functional impairment. A biological definition of neuronal α-synuclein disease and an NSD-ISS research framework are essential to enable interventional trials at early disease stages. The NSD-ISS will evolve to include the incorporation of data-driven definitions of stage-specific functional anchors and additional biomarkers as they emerge and are validated. Presently, the NSD-ISS is intended for research use only; its application in the clinical setting is premature and inappropriate.

Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression

Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

Background

Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).

Methods

155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.

Results

Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion

The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

An overview: Radiotracers and nano-radiopharmaceuticals for diagnosis of Parkinson's disease

Parkinson's disease (PD) is a widespread progressive neurodegenerative disease. Clinical diagnosis approaches are insufficient to provide an early and accurate diagnosis before a substantial of loss of dopaminergic neurons. PET and SPECT can be used for accurate and early diagnosis of PD by using target-specific radiotracers. Additionally, the importance of BBB penetrating targeted nanosystems has increased in recent years. This article reviews targeted radiopharmaceuticals used in clinics and novel nanocarriers for research purposes of PD imaging.

Design, synthesis and preclinical evaluation of muscarine receptor antagonists via a scaffold-hopping approach

Our research group recently identified a rearrangement product of pirenzepine as starting point for a comprehensive rational drug design approach towards orthosteric muscarinic acetylcholine receptor ligands. Chemical reduction and bioscaffold hop lead to the development of sixteen promising compounds featuring either a benzimidazole or carbamate moiety, all exhibiting comparable pharmacophoric characteristics. The synthesized compounds were characterized by NMR, HR-MS, and RP-HPLC techniques. Subsequent evaluation encompassed binding affinity assessment on CHO-hM1-5 cells, mode of action determination, and analysis of physico-chemical parameters. The CNS MPO score indicated favorable drug-like attributes and potential CNS activity for the antagonistic ligands. The most promising compounds displayed Ki-values within a desirable low nanomolar range, and their structural features allow for potential carbon-11 radiolabeling. Our optimization efforts resulted in compounds with a remarkable 138-fold increase in binding affinity compared to the previously mentioned rearrangement product towards human M5, suggesting their prospective utility in positron emission tomography applications.

Rational design synthesis and evaluation of a novel near-infrared fluorescent probe for selective imaging of amyloid-β aggregates in Alzheimer's disease

Alzheimer's disease (AD) is a degenerative neurological disorder that remains incurable to date, seriously affecting the quality of life and health of those affected. One of the key neuropathological hallmarks of AD is the formation of amyloid-β (Aβ) plaques. Near-infrared (NIR) probes that possess a large Stokes shift show great potential for imaging of Aβ plaques in vivo and in vitro. Herein, we proposed a rational strategy for design and synthesis of a series of NIR fluorescent probes that incorporate a tricarbonitrile group as a strong electron-withdrawing group (EWG) to enable NIR emission and large Stokes shift for optimal imaging of Aβ plaques. The probe TCM-UM exhibited remarkable in vitro performance, including strong NIR emission (λem = 670 nm), large Stokes shift (120-245 nm), and its affinity for Aβ42 aggregates (Kd = 43.78 ± 4.09 nM) was superior to the commercially available probe Thioflavin T (ThT, Kd = 896.04 ± 33.43 nM). Further, TCM-UM was selected for imaging Aβ plaques in brain tissue slices and APP/PS1 transgenic (AD) mice, the results indicated that TCM-UM had an excellent ability to penetrate the blood-brain barrier (BBB) compared with ThT, and it could effectively distinguish wild-type (Wt) mice and APP/PS1 transgenic (AD) mice.

Loss of the parkinsonism-associated protein FBXO7 in glutamatergic forebrain neurons in mice leads to abnormal motor behavior and synaptic defects

Mutations in PARK15, which encodes for the F-box protein FBXO7 have been associated with Parkinsonian Pyramidal syndrome, a rare and complex movement disorder with Parkinsonian symptoms, pyramidal tract signs and juvenile onset. Our previous study showed that systemic loss of Fbxo7 in mice causes motor defects and premature death. We have also demonstrated that FBXO7 has a crucial role in neurons as the specific deletion in tyrosine hydroxylase-positive or glutamatergic forebrain neurons leads to late-onset or early-onset motor dysfunction, respectively. In this study, we examined NEX-Cre;Fbxo7fl/fl mice, in which Fbxo7 was specifically deleted in glutamatergic projection neurons. The effects of FBXO7 deficiency on striatal integrity were investigated with HPLC and histological analyses. NEX-Cre;Fbxo7fl/fl mice revealed an increase in striatal dopamine concentrations, changes in the glutamatergic, GABAergic and dopaminergic pathways, astrogliosis and microgliosis and little or no neuronal loss in the striatum. To determine the effects on the integrity of the synapse, we purified synaptic membranes, subjected them to quantitative mass spectrometry analysis and found alterations in the complement system, endocytosis and exocytosis pathways. These neuropathological changes coincide with alterations in spontaneous home cage behavior. Taken together, our findings suggest that FBXO7 is crucial for corticostriatal projections and the synaptic integrity of the striatum, and consequently for proper motor control.

Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.

Integrative gene expression analysis for the diagnosis of Parkinson's disease using machine learning and explainable AI

Parkinson's disease (PD) is a progressive neurodegenerative disorder. Various symptoms and diagnostic tests are used in combination for the diagnosis of PD; however, accurate diagnosis at early stages is difficult. Blood-based markers can support physicians in the early diagnosis and treatment of PD. In this study, we used Machine Learning (ML) based methods for the diagnosis of PD by integrating gene expression data from different sources and applying explainable artificial intelligence (XAI) techniques to find the significant set of gene features contributing to diagnosis. We utilized the Least Absolute Shrinkage and Selection Operator (LASSO), and Ridge regression for the feature selection process. We utilized state-of-the-art ML techniques for the classification of PD cases and healthy controls. Logistic regression and Support Vector Machine showed the highest diagnostic accuracy. SHapley Additive exPlanations (SHAP) based global interpretable model-agnostic XAI method was utilized for the interpretation of the Support Vector Machine model. A set of significant biomarkers that contributed to the diagnosis of PD were identified. Some of these genes are associated with other neurodegenerative diseases. Our results suggest that the utilization of XAI can be useful in making early therapeutic decisions for the treatment of PD. The integration of datasets from different sources made this model robust. We believe that this research article will be of interest to clinicians as well as computational biologists in translational research.

Gut Microbial Metabolome and Dysbiosis in Neurodegenerative Diseases: Psychobiotics and Fecal Microbiota Transplantation as a Therapeutic Approach-A Comprehensive Narrative Review

The comprehensive narrative review conducted in this study delves into the mechanisms of communication and action at the molecular level in the human organism. The review addresses the complex mechanism involved in the microbiota-gut-brain axis as well as the implications of alterations in the microbial composition of patients with neurodegenerative diseases. The pathophysiology of neurodegenerative diseases with neuronal loss or death is analyzed, as well as the mechanisms of action of the main metabolites involved in the bidirectional communication through the microbiota-gut-brain axis. In addition, interventions targeting gut microbiota restructuring through fecal microbiota transplantation and the use of psychobiotics-pre- and pro-biotics-are evaluated as an opportunity to reduce the symptomatology associated with neurodegeneration in these pathologies. This review provides valuable information and facilitates a better understanding of the neurobiological mechanisms to be addressed in the treatment of neurodegenerative diseases.

Effect of rTMS at SMA on task-based connectivity in PD

BACKGROUND

Transcranial magnetic stimulation (TMS) can aid in alleviating clinical symptoms in Parkinson's disease (PD). To better understand the neural mechanism of the intervention, neuroimaging modalities have been used to assess the effects of rTMS.

OBJECTIVE

To study the changes in cortical connectivity and motor performance with rTMS at supplementary motor area (SMA) in PD using clinical assessment tools and task-based functional MRI.

METHODOLOGY

3000 pulses at 5 Hz TMS were delivered at the left SMA once a week for a total of 8 consecutive weeks in 4 sham sessions (week 1-4) and 4 real sessions (week 5 to week 8) in 16 subjects with PD. The outcomes were assessed with UPDRS, PDQ 39 and task-based fMRI at baseline, after sham sessions at week 4, and after real sessions at week 8. Visuo-spatial functional MRI task along with T1 weighted scans (at 3 Tesla) were used to evaluate the effects of rTMS intervention. Multivariate pattern analysis (MVPA) was used to analyse task-based fMRI using Conn toolbox.

RESULTS

Improvements (p < 0.05) were observed in UPDRS II, III, Mobility and ADL of PDQ39 after real sessions of rTMS. MVPA of task-based connectivity revealed clusters of activation in right hemispheric precentral area, superior frontal gyrus, middle frontal gyrus, thalamus and cerebellum (cluster threshold pFDR=0.001).

CONCLUSIONS

Weekly rTMS sessions at SMA incurred clinical motor benefits as revealed by an improvement in clinical scales and dexterity performance. These benefits could be attributed to changes in connectivity remote brain regions in the motor network.

Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.

Neuroinflammation in Parkinson’s Disease: From Gene to Clinic: A Systematic Review

Parkinson’s disease is a neurodegenerative disease whose progression and clinical characteristics have a close bidirectional and multilevel relationship with the process of neuroinflammation. In this context, it is necessary to understand the mechanisms involved in this neuroinflammation–PD link. This systematic search was, hereby, conducted with a focus on the four levels where alterations associated with neuroinflammation in PD have been described (genetic, cellular, histopathological and clinical-behavioral) by consulting the PubMed, Google Scholar, Scielo and Redalyc search engines, including clinical studies, review articles, book chapters and case studies. Initially, 585,772 articles were included, and, after applying the inclusion and exclusion criteria, 84 articles were obtained that contained information about the multilevel association of neuroinflammation with alterations in gene, molecular, cellular, tissue and neuroanatomical expression as well as clinical-behavioral manifestations in PD.

Current Treatments and New, Tentative Therapies for Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative pathology, the origin of which is associated with the death of neuronal cells involved in the production of dopamine. The prevalence of PD has increased exponentially. The aim of this review was to describe the novel treatments for PD that are currently under investigation and study and the possible therapeutic targets. The pathophysiology of this disease is based on the formation of alpha-synuclein folds that generate Lewy bodies, which are cytotoxic and reduce dopamine levels. Most pharmacological treatments for PD target alpha-synuclein to reduce the symptoms. These include treatments aimed at reducing the accumulation of alpha-synuclein (epigallocatechin), reducing its clearance via immunotherapy, inhibiting LRRK2, and upregulating cerebrosidase (ambroxol). Parkinson’s disease continues to be a pathology of unknown origin that generates a significant social cost for the patients who suffer from it. Although there is still no definitive cure for this disease at present, there are numerous treatments available aimed at reducing the symptomatology of PD in addition to other therapeutic alternatives that are still under investigation. However, the therapeutic approach to this pathology should include a combination of pharmacological and non-pharmacological strategies to maximise outcomes and improve symptomatological control in these patients. It is therefore necessary to delve deeper into the pathophysiology of the disease in order to improve these treatments and therefore the quality of life of the patients.

Dopaminergic imaging in degenerative parkinsonisms, an established clinical diagnostic tool

Parkinson's disease (PD) and other neurodegenerative parkinsonisms are characterised by loss of striatal dopaminergic neurons. Dopamine functional deficits can be measured in vivo using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) ligands assessing either presynaptic (e.g. dopamine synthesis and storage, transporter density) or postsynaptic terminals (i.e. D2 receptors availability). Nuclear medicine imaging thus helps the clinician to separate degenerative forms of parkinsonism with other neurological conditions, e.g. essential tremor or drug-induced parkinsonism. With the present study, we aimed at summarizing the current evidence about dopaminergic molecular imaging in the diagnostic evaluation of PD, atypical parkinsonian syndromes and dementia with Lewy bodies (DLB), as well as its potential to distinguish these conditions and to estimate disease progression. In fact, PET/SPECT methods are clinically validated and have been increasingly integrated into diagnostic guidelines (e.g. for PD and DLB). In addition, there is novel evidence on the classification properties of extrastriatal signal. Finally, dopamine imaging has an outstanding potential to detect neurodegeneration at the premotor stage, including REM-sleep behavior disorder and olfactory loss. Therefore, inclusion of subjects at an early stage for clinical trials can largely benefit from a validated in vivo biomarker such as presynaptic dopamine pathways PET/SPECT assessment.

Application of Artificial Intelligence in Image Processing of Neurodegenerative Disorders: A Review Study

: Neurodegenerative diseases can make life difficult and lead to death in many cases. They also can be difficult, time-consuming, and costly to diagnose with enough accuracy/certainty. Artificial intelligence (AI) has shown promise in tackling some of the challenges present in medical imaging and is anticipated to become a crucial tool in health care applications in the near future. In particular, deep learning methods have displayed great performance in various subfields of image processing, including but not limited to image segmentation, image synthesis, and image reconstruction. In this paper, many state-of-the-art applications of deep learning models in image processing were reviewed.

Selecting the Best Animal Model of Parkinson's Disease for Your Research Purpose: Insight from in vivo PET Imaging Studies

Parkinson's disease (PD) is a debilitating neurodegenerative multisystem disorder leading to motor and non-motor symptoms in millions of individuals. Despite intense research, there is still no cure, and early disease biomarkers are lacking. Animal models of PD have been inspired by basic elements of its pathogenesis, such as dopamine dysfunction, alpha-synuclein accumulation, neuroinflammation and disruption of protein degradation, and these have been crucial for a deeper understanding of the mechanisms of pathology, the identification of biomarkers, and evaluation of novel therapies. Imaging biomarkers are non-invasive tools to assess disease progression and response to therapies; their discovery and validation have been an active field of translational research. Here, we highlight different considerations of animal models of PD that can be applied to future research, in terms of their suitability to answer different research questions. We provide the reader with important considerations of the best choice of model to use based on the disease features of each model, including issues related to different species. In addition, positron emission tomography studies conducted in PD animal models in the last 5 years are presented. With a variety of different species, interventions and genetic information, the choice of the most appropriate model to answer research questions can be daunting, especially since no single model recapitulates all aspects of this complex disorder. Appropriate animal models in conjunction with in vivo molecular imaging tools, if selected properly, can be a powerful combination for the assessment of novel therapies and developing tools for early diagnosis.

Evaluation of the α-synuclein PET radiotracer (d3)-[11C]MODAG-001 in pigs

BACKGROUND

A positron emission tomography (PET) radiotracer to neuroimage α-synuclein aggregates would be a crucial addition for early diagnosis and treatment development in disorders such as Parkinson's disease, where elevated aggregate levels are a histopathological hallmark. The radiotracer (d₃)-[¹¹C]MODAG-001 has recently shown promise for visualization of α-synuclein pre-formed fibrils (α-PFF) in rodents. We here test the radiotracer in a pig model where proteins are intracerebrally injected immediately before scanning. Four pigs were injected in one hemisphere with 150 μg α-PFF, and in the other hemisphere, either 75 μg α-PFF or human brain homogenate from either dementia with Lewy bodies (DLB) or Alzheimer's disease (AD) was injected. All pigs underwent one or two (d₃)-[¹¹C]MODAG-001 PET scans, quantified with the non-invasive Logan graphical analysis using the occipital cortex as a reference region.

RESULTS

The α-PFF and AD homogenate injected brain regions had high uptake of (d₃)-[¹¹C]MODAG-001 compared to the occipital cortex or cerebellum. BP_ND values in 150 μg α-PFF injected regions was 0.78, and in the AD homogenate injected regions was 0.73. By contrast, the DLB homogenate injected region did not differ in uptake and clearance compared to the reference regions. The time-activity curves and BP_ND values in the 150 μg and 75 μg injected regions of α-PFFs show a dose-dependent effect, and the PET signal could be blocked by pretreatment with unlabeled MODAG-001.

CONCLUSION

We find that both α-PFF and AD brain homogenates give rise to increased binding of (d₃)-[¹¹C]MODAG-001 when injected into the pig brain. Despite its limited specificity for cerebral α-synuclein pathology, (d₃)-[¹¹C]MODAG-001 shows promise as a lead tracer for future radiotracer development.

The Role of Hydrogen Sulfide Targeting Autophagy in the Pathological Processes of the Nervous System

Autophagy is an important cellular process, involving the transportation of cytoplasmic contents in the double membrane vesicles to lysosomes for degradation. Autophagy disorder contributes to many diseases, such as immune dysfunction, cancers and nervous system diseases. Hydrogen sulfide (H₂S) is a volatile and toxic gas with a rotten egg odor. For a long time, it was considered as an environmental pollution gas. In recent years, H₂S is regarded as the third most important gas signal molecule after NO and CO. H₂S has a variety of biological functions and can play an important role in a variety of physiological and pathological processes. Increasingly more evidences show that H₂S can regulate autophagy to play a protective role in the nervous system, but the mechanism is not fully understood. In this review, we summarize the recent literatures on the role of H₂S in the pathological process of the nervous system by regulating autophagy, and analyze the mechanism in detail, hoping to provide the reference for future related research.

Effects of Exercise on Parkinson’s Disease: A Meta-Analysis of Brain Imaging Studies

Background

Exercise is increasingly recognized as a key component of Parkinson’s disease (PD) treatment strategies, but the underlying mechanism of how exercise affects PD is not yet fully understood.

Objective

The activation likelihood estimation (ALE) method is used to study the mechanism of exercise affecting PD, providing a theoretical basis for studying exercise and PD, and promoting the health of patients with PD.

Methods

Relevant keywords were searched on the PubMed, Cochrane Library, and Web of Science databases. Seven articles were finally included according to the screening criteria, with a total sample size of 97 individuals. Using the GingerALE 3.0.2 software, an ALE meta-analysis was performed using seven studies that met the requirements, and the probability of the cross-experiment activation of each voxel was calculated.

Results

The meta-analysis produced seven clusters, and major activations were found in the cerebellum, occipital lobe, parietal lobe, and frontal lobe brain regions.

Conclusion

Exercise for PD mainly results in the enhanced activation of the cerebellum, occipital lobe, parietal lobe, and frontal lobe. Exercise for PD does not cause a change in the activation of a single brain area, and the observed improvement may result from coordinated changes in multiple brain areas.