Search Results (37)

Parkinsonian syndromes comprise overlapping neurodegenerative and non-degenerative disorders, making aetiological diagnosis difficult, while existing procedural guidance does not fully integrate tracer-specific biology with clinical decision-making, multimodal strategies, and emerging quantitative and pathology-directed biomarkers. This review synthesises evidence on the molecular targets, radiopharmaceutical characteristics, biological interpretation, and clinical applications of dopaminergic single-photon emission computed tomography (SPECT) and positron emission tomography (PET), focusing on the dopamine transporter (DAT), aromatic L-amino acid decarboxylase (AADC), vesicular monoamine transporter type 2 (VMAT2), dopamine D₂/D₃ receptors, differential diagnosis, semiquantification, kinetic modelling, and artificial intelligence-assisted interpretation. The evidence confirms that DAT SPECT with [¹²³I]ioflupane ([¹²³I]FP-CIT) remains the most established method for demonstrating or arguing against presynaptic nigrostriatal dysfunction, but an abnormal result cannot establish aetiology, and a normal result can redirect evaluation towards non-degenerative mimics; target-specific PET provides complementary biological information, although availability, standardisation, and prospective validation remain limiting. By additionally integrating genetic and prodromal applications, question-driven multimodal imaging, emerging acquisition approaches, α-synuclein imaging and seed amplification assays, and contemporary biological staging frameworks, this review extends procedural guidance and positions dopaminergic imaging as a targeted functional biomarker selected according to the unresolved clinical question rather than as a stand-alone disease label. Full article

Background/Objectives: Dementia with Lewy bodies (DLB) is a clinically heterogeneous neurodegenerative disorder characterized by cognitive, neuropsychiatric, motor, and autonomic manifestations. Reduced striatal dopamine transporter availability on ¹²³I-ioflupane single-photon emission computed tomography (SPECT) is an established indicative biomarker of DLB. However, the associations among expert visual interpretation, regional semiquantitative dopamine transporter measurements, autonomic manifestations, and dementia severity remain incompletely characterized. This study aimed to evaluate the relationship between visual ¹²³I-ioflupane SPECT classification and regional DaTQUANT z-scores in patients with clinically probable DLB and to explore their associations with autonomic manifestations and dementia severity. Methods: This retrospective, cross-sectional study included 30 patients who received a clinical diagnosis of probable DLB according to the 2017 DLB Consortium criteria before the ¹²³I-ioflupane SPECT results became available. SPECT images were assessed visually by experienced nuclear medicine physicians and semiquantitatively using DaTQUANT software. Bilateral striatal, putaminal, caudate, and putamen-to-caudate ratio z-scores were analyzed in relation to visual scan classification, Clinical Dementia Rating (CDR) scores, and retrospectively ascertained autonomic manifestations, including orthostatic hypotension, delayed orthostatic hypotension, supine hypertension, postprandial hypotension, constipation, and urinary incontinence. Results: Scans were visually classified as supportive of nigrostriatal dopaminergic degeneration in 23 of 30 patients (76.7%) and as non-supportive in seven (23.3%). Bilateral striatal, putaminal, and caudate z-scores were significantly lower in visually supportive scans than in non-supportive scans (all p < 0.001), whereas putamen-to-caudate ratio z-scores did not differ significantly between the groups. None of the evaluated autonomic manifestations was significantly associated with either visual scan classification or regional DaTQUANT measurements. No regional DaTQUANT measurement was significantly associated with dementia severity. A modest positive correlation was observed between the left putamen-to-caudate ratio z-score and CDR (Spearman’s ρ = 0.373, nominal p = 0.050); however, given the small sample size and multiple regional comparisons, this borderline finding was considered exploratory. Conclusions: Regional DaTQUANT measurements were consistent with expert visual interpretation of ¹²³I-ioflupane SPECT in patients with clinically probable DLB. Associations of dopaminergic imaging measurements with autonomic manifestations and dementia severity were limited. Semiquantitative analysis may complement visual interpretation, but its findings should be interpreted within the broader clinical and biomarker context. Larger prospective studies incorporating standardized autonomic testing, appropriate control groups, longitudinal follow-up, and complementary biomarkers are warranted. Full article

Background: Parkinson’s disease (PD) is characterized by a complex interplay of dopaminergic degeneration, glial activation, and lipid metabolic dysregulation. However, accurately describing how natural product interventions remodel these pathologies across distinct brain regions and cellular microenvironments remains a critical challenge. Methods: We established an integrated multi-omics framework to decode the neuroprotective mechanisms of solanesol (Sol) in an MPTP-induced PD mouse model. We combined single-cell eQTL-based Mendelian randomization (scMR), transcriptomic localization, and virtual knockout analyses to prioritize cell-type-specific regulatory nodes across neuronal, glial, and vascular populations, avoiding the limitations of traditional bulk targeting. In vivo behavioral assays were conducted, alongside orthogonal validation via airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and gene–metabolite co-enrichment analysis, to map regional metabolic networks and structural spatial reprogramming. Results: Computational prioritization highlighted cell-type-specific regulatory nodes including PRKCB, PRKCE, PDGFRB, and FABP3/5. In vivo, Sol attenuated motor and cognitive deficits and largely restored the highly compartmentalized spatial distributions of striatal dopamine, L-DOPA, and acetylcholine. Crucially, AFADESI-MSI and co-enrichment analysis revealed that Sol specifically reversed MPTP-induced spatial disruptions by rescuing key neuromodulatory metabolites—including cervonoyl ethanolamide, phosphatidylcholine species, taurine, and NADHX—which were tightly coupled to sphingolipid signaling, fatty-acid transport, mitochondrial translation, and cell-adhesion pathways. Conclusions: Sol ameliorates PD-like pathology not through a singular target, but by choreographing a spatially and cellularly compartmentalized restoration of lipid–inflammatory homeostasis. Furthermore, our integrated single-cell and spatial metabolomic blueprint sets a new methodological paradigm for elucidating the precise execution programs of natural neurotherapeutics. Full article

Cerebellar climbing fiber–Purkinje cell (CF–PC) long-term depression (LTD) plays a critical role in motor learning and is modulated by locus coeruleus (LC) noradrenergic afferents via distinct adrenergic receptor (AR) subtypes. Nevertheless, the mechanisms underlying LC noradrenergic neuron-mediated regulation of CF–PC LTD remain poorly understood. Here, we investigated the effects of chemogenetic activation of LC noradrenergic afferents on CF–PC LTD in cerebellar slices from dopamine β-hydroxylase (DBH)-Cre mice using electrophysiology, glutamate sensor imaging, immunofluorescence and pharmacological approaches. Tetanic stimulation (5 Hz) of CFs induced CF–PC LTD under control conditions, and this LTD was enhanced by chemogenetic activation of LC noradrenergic afferents. Blockade of group I metabotropic glutamate receptors (mGluR1) abolished LTD under control conditions, whereas chemogenetic activation of LC noradrenergic afferents triggered a novel form of CF–PC LTD accompanied by an increased N2/N1 ratio. With mGluR1 blocked, chemogenetic activation of LC noradrenergic afferents failed to trigger the novel CF–PC LTD following blockade of α₂-AR or α₂A-AR, but not α₂B-AR or α₂C-AR. Importantly, chemogenetic activation of LC noradrenergic afferents triggered LTD of glutamate fluorescence at CF terminals, which was abolished by blockade of α_2-AR or α₂A-AR, but not α₂B-AR or α₂C-AR. Notably, inhibition of either cyclin-dependent kinase 5 (CDK5) or presynaptic, but not postsynaptic, protein kinase A (PKA) completely abolished the CF–PC LTD triggered by chemogenetic activation of LC noradrenergic afferents in mouse cerebellar slices. Immunofluorescence results showed robust α₂A-AR expression throughout the cerebellar molecular layer, with intense signals along PC dendrites and clear colocalization with vesicular glutamate transporter 2 (vGluT2) at cerebellar CF terminals. These results indicate that activation of LC noradrenergic afferents potentiates CF–PC LTD by triggering Glu-LTD at CF terminals through the α₂A-AR/CDK5/PKA signaling cascade in the mouse cerebellar cortex. Full article

Introduction: Progressive supranuclear palsy (PSP) is an atypical Parkinsonian disorder characterized by a range of clinical phenotypes, reflecting its multiple subtypes. As a result, accurate diagnosis during life remains challenging, underscoring the need for reliable biomarkers. The present narrative review aims to evaluate whether dopamine transporter single-photon emission computed tomography (DaT-SPECT) can serve as a biomarker in the assessment of PSP. Methods: The database search identified 31 original research articles relevant to our study objective. Of these, 17 studies included PSP patients and utilized DaT-SPECT as the sole molecular imaging modality; 9 studies combined DaT-SPECT with at least one additional molecular imaging technique; and 5 studies integrated DaT-SPECT with a laboratory-based biomarker of neurodegenerative disease. Results: DaT-SPECT appears to demonstrate low specificity and variable sensitivity for PSP across studies. Discussion: Combining DaT-SPECT with other diagnostic biomarkers, especially brain magnetic resonance imaging and other nuclear imaging modalities, may improve diagnostic accuracy, especially given its relatively low specificity for PSP. Nevertheless, these initially promising findings need to be validated in large, multicenter studies that include and clearly define multiple, autopsy-confirmed PSP subtypes. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder for which dopamine transporter single-photon emission computed tomography (DaT-SPECT) is widely used to support clinical diagnosis. Recent convolutional neural network (CNN)-based studies have reported high classification accuracy on DaT-SPECT datasets. However, the relative contributions of network architecture and training protocol design to these results remain insufficiently explored, particularly for small and moderately sized medical imaging datasets. In this study, a training-oriented evaluation of CNNs for PD classification is conducted using two DaT-SPECT datasets derived from the Parkinson’s Progression Markers Initiative (PPMI). First, a previously published experimental setup is faithfully reproduced on a curated dataset of 645 DaT-SPECT images using identical preprocessing procedures, data splits, and model architectures. Under the reproduced experimental setting, previously reported classification accuracies for individual CNN architectures ranged from 93.02% to 95.34%, while an ensemble approach achieved 98.45% accuracy. The same architectures are then evaluated using a unified training protocol incorporating standardized optimization and regularization strategies. Using this protocol, ResNet50 achieves 100% classification accuracy, with all evaluation metrics reaching 1.0, while VGG16, Inception V3, and Xception each achieve an accuracy of 99.22%. On a larger, independently constructed PPMI-derived dataset with higher spatial resolution, previously reported classification accuracies ranged from 93.27% for PD vs. SWEDD to 95.33% for PD vs. control. Using the proposed unified training protocol, the evaluated CNN architectures achieve classification accuracies of 98% for PD vs. SWEDD and 100% for PD vs. control. These results indicate that training protocol design has a stronger influence on DaT-SPECT-based PD classification performance than the specific choice of CNN architecture. Full article

Parkinson disease (PD) and mood disorders represent two substantial global health burdens that increasingly co-occur as both conditions rise in prevalence worldwide. Diagnosing Parkinson disease in patients with pre-existing mood disorders is clinically challenging due to overlapping symptoms, medication effects, and shared neurobiological mechanisms. Apathy, psychomotor slowing, and fatigue may mimic depressive symptoms, leading to delayed recognition of early parkinsonism. Development of an underlying neurodegenerative disorder could account for some treatment-resistant symptoms or treatment failures if not recognized. Therefore, the identification of PD will change the treatment and management plan significantly. Accurate diagnosis of PD requires a detailed neurologic examination focusing on bradykinesia, rigidity, and resting tremor, supported when appropriate by dopamine transporter imaging (DaT scan) or other emerging biomarkers. Understanding the temporal relationship between psychiatric and motor features helps differentiate prodromal PD from primary mood disorders. Management of patients with both mood disorders and PD integrates dopaminergic replacement therapy for motor symptoms with individualized treatment of psychiatric comorbidities. Levodopa remains the cornerstone for motor control, while dopamine agonists, MAO-B inhibitors, and COMT inhibitors can be added as needed. For depression and anxiety, SSRIs and SNRIs are first-line choices; quetiapine or clozapine are preferred when treatment for psychosis is necessary. Intentional, thoughtful polypharmacy is frequently required. Non-pharmacologic interventions—including cognitive behavioral therapy, structured exercise, and patient–caregiver education—enhance mood, function, and quality of life. Multidisciplinary collaboration between neurology, psychiatry, and allied health professionals is essential for optimal outcomes. This review offers guidance to healthcare providers as well as other interested parties involved in patients with mood disorders who may also be developing or have PD, especially to those who may have limited access to neurologic resources. Full article

Dopamine transporter (DaT) imaging is commonly used for monitoring Parkinson’s disease (PD), where the amount of striatal DaT uptake serves as the PD severity indicator. MRI of the nigral region has recently emerged as a safer and more available alternative. This work introduces a 3D convolutional network-based symmetric regressor for predicting the DaT-uptake amount from nigral MRI patches. Unlike the typical deep networks, the proposed model leverages the lateral symmetry between right and left nigrae by incorporating a paired input–output architecture that concurrently predicts DaT uptakes for both the right and left striata, while employing a symmetric loss that constrains the difference between right-to-left predictions. To improve model reliability, we also propose a symmetric Monte Carlo dropout strategy for providing fruitful uncertainty estimates about the prediction. Evaluated on 734 3D nigral patches, our symmetric regressor demonstrated a $12.11\%$ improvement in prediction error compared to standard deep-learning models. Furthermore, the reliability was enhanced, resulting in a $5\%$ reduction in the prediction uncertainty interval at a $95\%$ coverage probability for the true DaT-uptake amount. Our findings demonstrate that integrating structural symmetry into model design is a powerful strategy for achieving accurate and reliable predictions for PD severity analysis. Full article

Background: Differentiating Parkinson’s disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with ¹²³I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT^® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed ¹²³I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT^® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT^® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT^® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of ¹²³I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT^® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging. Full article

Introduction: Bayesian penalized likelihood (BPL) reconstruction algorithms, commercially implemented as Q.Clear (GE Healthcare), enhance image quality but may alter quantitative metrics. The impact of BPL on dopamine transporter (DAT) PET quantification, including ratios, remains unclear. This study investigates whether Q.Clear processing alters key metrics such as specific binding ratios (SBRs) and interregional ratios. Methods: We retrospectively analyzed 170 paired F-18 FP-CIT PET datasets reconstructed with conventional 3D-OSEM (baseline-DICOM) and Q.Clear (Q.Clear-DICOM). Quantification was performed using BTXBrain-DAT (Brightonix Imaging), yielding 57 specific binding ratios (SBRs), three asymmetry indices, and nine interregional ratios. Paired statistical tests, Bland–Altman plots, and reproducibility checks were conducted. Visual reads by two nuclear medicine physicians were also compared between datasets. Results: Q.Clear processing significantly altered all quantitative metrics (p < 0.001). SBR values changed in all 57 regions, with most high-uptake regions showing an increase and low-uptake regions showing a decrease. Striatal and caudate asymmetry indices showed significant differences (p < 0.0001), whereas the putamen index remained stable. All interregional ratios differed significantly, although Bland–Altman analysis indicated relative stability for ratios compared with asymmetric indices. BTXBrain-DAT showed perfect reproducibility on repeat analysis, and visual interpretation was unaffected by reconstruction method. Conclusions: Q.Clear (BPL) reconstruction substantially influences F-18 FP-CIT PET quantification, including ratios and asymmetry indices, while leaving visual interpretation unchanged. These findings highlight the need for caution when using image enhancement functions for quantitative analysis, particularly in clinical studies involving low-uptake regions or multicenter data comparisons. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder marked by the progressive loss of dopaminergic neurons in the nigrostriatal pathway. The dopamine active transporter (DAT), a key protein involved in dopamine reuptake, serves as a selective biomarker for dopaminergic terminals in the striatum. DAT binding has been extensively studied using in vivo imaging techniques such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET). To support the design of new radiotracers targeting DAT, we employ Quantitative Structure–Activity Relationship (QSAR) analysis on a structurally diverse dataset composed of 57 compounds with known affinity constants for DAT. The best-performing QSAR model includes four molecular descriptors and demonstrates robust statistical performance: R² = 0.7554, Q²_LOO = 0.6800, and external R² = 0.7090. These values indicate strong predictive capability and model stability. The predicted compounds are evaluated using a docking methodology to check the correct coupling and interactions with the DAT. The proposed approach—combining QSAR modeling and docking—offers a valuable strategy for screening and optimizing potential PET/SPECT radiotracers, ultimately aiding in the neuroimaging and early diagnosis of Parkinson’s disease. Full article

Background/Objectives: 123I-FP-CIT dopamine transporter imaging is commonly used for the diagnosis of Parkinsonian syndromes in patients whose clinical presentation is atypical. Prolonged immobility, which can be difficult to maintain in this population, is required to perform SPECT acquisition. In this study we aimed to develop a Convolutional Neural Network (CNN) able to predict the outcome of the full examination based on the first acquired projection, and reliably detect normal patients. Methods: All 123I-FP-CIT SPECT performed in our center between June 2017 and February 2024 were included and split between a training and a validation set (70%/30%). An additional 100 SPECT were used as an independent test set. Examinations were labeled by two independent physicians. A VGG16-like CNN model was trained to assess the probability of examination abnormality from the first acquired projection (anterior and posterior view at 0°), taking age into consideration. A threshold maximizing sensitivity while maintaining good diagnostic accuracy was then determined. The model was validated in the independent testing set. Saliency maps were generated to visualize the most impactful areas in the classification. Results: A total of 982 123I-FP-CIT SPECT were retrieved and labelled (training set: 618; validation set: 264; independent testing set: 100). The trained model achieved a sensibility of 98.0% and a negative predictive value of 96.3% (one false negative) while maintaining an accuracy of 75.0%. The saliency maps confirmed that the regions with the greatest impact on the final classification corresponded to clinically relevant areas (basal ganglia and background noise). Conclusions: Our results suggest that this trained CNN could be used to exclude presynaptic dopaminergic loss with high reliability from the first acquired projection. It could be particularly useful in patients with compliance issues. Confirmation with images from other centers will be necessary. Full article

Background/Objectives: The introduction of dopamine transporter scan (DaTscan) in clinical diagnostics has revolutionized the way clinicians approach movement disorders, offering valuable insights into presynaptic striatal dopaminergic deficits and revealing subjacent neurodegeneration. The aim of our study was to evaluate the impact of DaTscan on diagnostic decisions regarding movement disorders, particularly Parkinson’s disease (PD) and atypical parkinsonian syndromes, under real-world circumstances in Greece. Methods: We retrospectively analyzed data from 360 patients who underwent a DaTscan examination between 2018 and 2023 at a tertiary hospital in Greece, including referrals from both movement disorder specialists and general neurologists, either hospital-based or in private practice. Demographics, primary referral symptoms, and both pre-scan and post-scan diagnoses were collected and analyzed. Results: The mean age in our cohort was 60 ± 13.5 years, and tremor was the leading referral symptom (40.8%). The initial diagnosis changed in nearly half of the cases (48.3%) following DaTscan. Significant shifts included transitions from an “Unclear” or “Dystonia” diagnosis to “Parkinson’s disease” in 78.1% and 72.7% of patients, respectively. However, the particularly high concordance rates between pre-scan and post-scan diagnosis for “Vascular parkinsonism” (100%), “Parkinson’s disease” (89.3%), and “Essential/Dystonic Tremor” (86%) suggest that the test may have been over-utilized or ordered beyond its intended indications. Conclusions: DaTscan markedly enhances diagnostic accuracy for movement disorders, particularly for general neurologists, addressing the complexities of overlapping clinical presentations. Continuous medical training is essential to ensure the cost-effective utilization of DaTscan in routine clinical practice; ongoing technological advancements will further refine and expand their applications, benefiting both patients and the broader medical community. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes. Full article

Background/Objectives: The genetic underpinnings of Parkinson’s disease (PD) and parkinsonism have drawn increasing attention in recent years. Mutations in the Factor-Induced Gene 4 (FIG4) have been implicated in various neurological disorders, including Charcot-Marie-Tooth disease type 4J (CMT4J), amyotrophic lateral sclerosis (ALS), and Yunis-Varón syndrome. This review aims to explore the association between FIG4 mutations and parkinsonism, with a specific focus on the rare missense mutation p.Ile41Thr (I41T). Methods: We identified 12 cases from 10 different families in which parkinsonism was reported in conjunction with CMT4J polyneuropathy. All cases involved the I41T mutation in a compound heterozygous state, combined with a FIG4 loss-of-function mutation. Data from clinical observations, neuroimaging studies, and genetic analyses were evaluated to understand the characteristics of parkinsonism in these patients. Results: In all 12 cases, parkinsonism developed either concurrently or following the onset of CMT4J neuropathy, but was never observed in isolation. Cases of both early- and late-onset parkinsonism were identified, reflecting similarities to genetic forms of parkinsonism with autosomal recessive inheritance. Imaging studies, including Dopamine transporter Single Photon Emission Computed Tomography (DaTscan) and brain magnetic resonance imaging (MRI), revealed abnormalities indicative of neurodegeneration, consistent with findings in other neurodegenerative disorders. Conclusions: The co-occurrence of parkinsonism with CMT4J in patients carrying the I41T mutation suggests an expanded spectrum of FIG4-related disorders, potentially implicating the same molecular mechanisms seen in other neurodegenerative disorders. Further research into FIG4-mediated pathways may offer valuable insights into potential therapeutic targets for disorders of both the central and peripheral nervous systems. Full article