Positron emission computed tomography/single photon emission computed tomography in Parkinson disease

Current trends in blood biomarkers detection and neuroimaging for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by both motor and cognitive impairments. A significant challenge in managing PD is the variability of symptoms and disease progression rates. This variability is primarily attributed to unclear biomarkers associated with the disease and the lack of early diagnostic technologies and effective imaging methods. PD-specific biomarkers are essential for developing practical tools that facilitate accurate diagnosis, patient stratification, and monitoring of disease progression. Hence, creating valuable tools for detecting and diagnosing PD based on specific biomarkers is imperative. Blood testing, less invasive than obtaining cerebrospinal fluid through a lumbar puncture, is an ideal source for these biomarkers. Although such biomarkers were previously lacking, recent advancements in various detection techniques related to PD biomarkers and new imaging methods have emerged. However, basic research requires more detailed guidelines on effectively implementing these biomarkers in diagnostic procedures to enhance the diagnostic accuracy of PD blood testing in clinical practice. This review discusses the developmental trends of PD-related blood biomarker detection technologies, including optical analysis platforms. Despite the progress in developing various biomarkers for PD, their specificity and sensitivity remain suboptimal. Therefore, the integration of multimodal biomarkers along with optical and imaging technologies is likely to significantly improve diagnostic accuracy and facilitate the implementation of personalized medicine. This review forms valid research hypotheses for PD research and guides future empirical studies.

Resting-State Changes in Aging and Parkinson's Disease Are Shaped by Underlying Neurotransmission: A Normative Modeling Study

BACKGROUND

Human healthy and pathological aging is linked to a steady decline in brain resting-state activity and connectivity measures. The neurophysiological mechanisms that underlie these changes remain poorly understood.

METHODS

Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we tested in the UK Biobank cohort (n = 25,917) whether and how age- and Parkinson's disease-related resting-state changes in commonly applied local and global activity and connectivity measures colocalize with underlying neurotransmitter systems.

RESULTS

We found that the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission correlated with age-related changes across functional activity and connectivity measures. Colocalization patterns in Parkinson's disease deviated from normative aging trajectories for these, as well as for cholinergic and GABAergic (gamma-aminobutyric acidergic) neurotransmission. The deviation from normal colocalization of brain function and GABAA correlated with disease duration.

CONCLUSIONS

These findings provide new insights into molecular mechanisms underlying age- and Parkinson's-related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson's disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson's disease-related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms that underlie specific clinical conditions.

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.

Neuro-Vulnerability in Energy Metabolism Regulation: A Comprehensive Narrative Review

This comprehensive narrative review explores the concept of neuro-vulnerability in energy metabolism regulation and its implications for metabolic disorders. The review highlights the complex interactions among the neural, hormonal, and metabolic pathways involved in the regulation of energy metabolism. The key topics discussed include the role of organs, hormones, and neural circuits in maintaining metabolic balance. The review investigates the association between neuro-vulnerability and metabolic disorders, such as obesity, insulin resistance, and eating disorders, considering genetic, epigenetic, and environmental factors that influence neuro-vulnerability and subsequent metabolic dysregulation. Neuroendocrine interactions and the neural regulation of food intake and energy expenditure are examined, with a focus on the impact of neuro-vulnerability on appetite dysregulation and altered energy expenditure. The role of neuroinflammation in metabolic health and neuro-vulnerability is discussed, emphasizing the bidirectional relationship between metabolic dysregulation and neuroinflammatory processes. This review also evaluates the use of neuroimaging techniques in studying neuro-vulnerability and their potential applications in clinical settings. Furthermore, the association between neuro-vulnerability and eating disorders, as well as its contribution to obesity, is examined. Potential therapeutic interventions targeting neuro-vulnerability, including pharmacological treatments and lifestyle modifications, are reviewed. In conclusion, understanding the concept of neuro-vulnerability in energy metabolism regulation is crucial for addressing metabolic disorders. This review provides valuable insights into the underlying neurobiological mechanisms and their implications for metabolic health. Targeting neuro-vulnerability holds promise for developing innovative strategies in the prevention and treatment of metabolic disorders, ultimately improving metabolic health outcomes.

Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering

The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.

Diagnosis of Cardiac Rehabilitation after Percutaneous Coronary Intervention in Acute Myocardial Infarction Patients by Emission Computed Tomography Image Features under Filtered Back Projection Reconstruction Algorithm

This study aimed to explore the application value of emission computed tomography (ECT) imaging technology based on filtered back projection reconstruction algorithm (FBP) in cardiac function examination after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). Eighty patients with myocardial infarction diagnosed by medical history, electrocardiograph (ECG), and myocardial enzyme admitted to hospital from February 2018 to February 2019 were selected as the research objects. All patients underwent PCI seven days after the onset of myocardial infarction. ECT was performed for all patients before and after surgery. In addition, all ECT images were processed by the FBP reconstruction algorithm. On this basis, preoperative and postoperative cardiac surgery function and ischemia of the patients were diagnosed. Then, the diagnostic results were compared with the results of coronary angiography and echocardiogram. The results showed that all patients had a total of 541 segments before PCI surgery. ECT examination revealed 294 abnormal segments of the ventricular wall, with a total score of 585 points. A total of 100 segments were scored with 1 point, a total of 194 segments were scored with 2 points, and a total of 50 segments were scored with 3 points. After PCI, the number of abnormal segments was reduced to 58, with a total score of 193. There were 6 segments with a score of 1, 44 segments with a score of 2, and 5 segments with a score of 3. The left ventricular diastolic volume (EDV), left ventricular systolic volume (ESV), stroke volume (CO), and ejection fraction (EF) of the patients before the operation were 148 ± 16 mL, 77 ± 14.5 mL, 4.29 ± 0.37 L/min, and 41.9 ± 8%, respectively. The EDV, ESV, CO, and EF of the patients after surgery were 132 ± 16 mL, 62 ± 13 mL, 4.89 ± 0.71, and 53 ± 6%, respectively. Significant changes occurred in various systolic function parameters before and after surgery, P < 0.05. The standardized regression coefficients of the three groups were 0.32, 0.41, and 0.47, respectively, P < 0.05, which indicated that the greater the coronary artery stenosis rate, the higher the diagnostic coincidence rate of left anterior descending limb (LAD), left circumflex branch (LCX), and left coronary artery (RCA). The conformity of ECT imaging in the LCX group for diagnosis of myocardial ischemia was higher than that of UCG, P < 0.05. To sum up, the ECT technology based on the FBP reconstruction algorithm had a good application prospect in the diagnosis of cardiac function recovery in AMI patients after PCI.