Early-stage Parkinson's Disease Research Articles

Insular and limbic abnormal functional connectivity in early-stage Parkinson's disease patients with autonomic dysfunction.

Autonomic symptoms in Parkinson's disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson's disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson's disease patients using high-density EEG. 53 early Parkinson's disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-β-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and functional connectivity in Parkinson's disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson's disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson's disease.

Exploring brain asymmetry in early-stage Parkinson's disease through functional and structural MRI.

This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early-stage Parkinson's disease (PD). Ninety-three early-stage PD patients (ePD, H-Y stages 1-2.5) were recruited, divided into 47 mild (ePD-mild, H-Y stages 1-1.5) and 46 moderate (ePD-moderate, H-Y stages 2-2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H-Y) staging system for disease severity assessment and utilized voxel-mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel-based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry. The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD-moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD-mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H-Y stages, UPDRS, and UPDRS-III scores. No significant asymmetry in gray matter was detected. Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early-stage PD.

MiR-193b-3p/ PGC-1α pathway regulates an insulin dependent anti-inflammatory response in Parkinson's disease

It has been shown that many miRNAs, including miR-193b-3p, are differentially expressed in Parkinson's disease (PD). Dysregulation of miR-193b-3p/PGC-1α axis may alter homeostasis in cells and can induce an inflammatory response commonly accompanied by metabolic disturbances. The aim of the present study is to investigate if dysregulation of the miR-193-3p/PGC-1α axis may contribute to the pathological changes observed in the PD brain. Brain tissue were obtained from middle frontal gyrus of non-demented controls and individuals with a PD diagnosis. RT-qPCR was used to determine the expression of miR-193b-3p and in situ hybridization (ISH) and immunological analysis were employed to establish the cellular distribution of miR-193b-3p. Functional assays were performed using SH-SY5Y cells, including transfection and knock-down of miR-193b-3p. We found significantly lower expression of miR-193b-3p in the early stages of PD (PD4) which increased throughout disease progression. Furthermore, altered expression of PGC-1α suggested a direct inhibitory effect of miR-193b-3p in the brain of individuals with PD. Moreover, we observed changes in expression of insulin after transfection of SH-SY5Y cells with miR-193b-3p, which led to dysregulation in the expression of several pro- or anti - inflammatory genes. Our findings indicate that the miR-193b-3p/PGC-1α axis is involved in the regulation of insulin signaling. This regulation is crucial, since insulin induced inflammatory response may serve as a protective mechanism during acute situations but potentially evolve into a pathological process in chronic conditions. This novel regulatory mechanism may represent an interesting therapeutic target with potential benefits for various neurodegenerative diseases.

Preliminary study of substantia nigra analysis by tensorial feature extraction.

Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis. The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD. We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.

STUDY OF fMRI BRAIN ACTIVITY DURING NON-HANDWRITING AND HANDWRITING TASKS ON ESSENTIAL HAND TREMOR

Essential hand tremor (EHT) is a prevalent neurological condition affecting geriatric populations, yet its underlying mechanisms remain poorly understood. This study aims to investigate the neural substrates associated with motor tasks in EHT patients, illuminating the complex neural activity characterizing this condition. Twenty participants underwent a thorough evaluation to ensure eligibility, excluding factors such as mental illness, drug dependency, or Parkinson’s disease (PD). Functional magnetic resonance imaging (fMRI) was utilized to examine brain activation patterns during non-handwriting tasks (NHWT) and handwriting tasks (HWT). Participants received training to standardize hand and forearm movements for effective fMRI assessment. fMRI data preprocessing included motion correction, filtering, removal of linear trends, normalization to Montreal Neurological Institute (MNI) space, and spatial smoothing. Distinctive patterns of brain activation were observed during motor tasks in individuals with EHT compared to controls. During NHWT, the EHT group showed significantly increased activation in the precentral gyrus, supplementary motor area, thalamus, and posterior cerebellar lobe, highlighting their role in mediating motor challenges in EHT. Similarly, during HWT, the EHT group exhibited heightened activation in the precentral gyrus and supplementary motor areas. In contrast, reduced activation was noted in the caudate nucleus, inferior temporal gyrus, and precuneus during HWT in the EHT group compared to controls. These findings align with previous research on involuntary movement disorders, such as early-stage PD, emphasizing the importance of the caudate nucleus and related regions in EHT. In conclusion, this study sheds light on the intricate neural activity underlying motor tasks in EHT patients. The identified neural regions and their functions offer insights into the neurophysiological basis of EHT-related motor impairments. These findings have the potential to enhance the understanding of EHT beyond its surface-level effects. This study has identified the brain regions involved in motor tasks affected by EHT. This sets a foundation for future research to better understand the complexities of this neurological condition. These discoveries may lead to novel therapeutic interventions tailored to address the unique challenges faced by individuals with EHT, representing a significant milestone in understanding and managing EHT.

Differential diagnosis value of sympathetic skin response and cutaneous silent period on early-stage multiple system atrophy and Parkinson disease

PurposeEarly differentiation between Parkinson's disease (PD) and Multiple system atrophy (MSA), particularly the parkinsonian subtypes (MSA-P), is challenging due to similar clinical symptoms. We aimed to evaluate Sympathetic skin response (SSR) and Cutaneous silent period (CSP) parameters in patients with MSA-P and PD to identify possible biomarkers that could distinguish the two groups of patients in early stage. Methods22 individuals with early-stage MSA-P, 29 with early-stage PD, and 28 healthy controls were recruited from Guangdong Provincial People's Hospital. Demographic data was collected for all participants. Their SSR and CSP were evaluated using clinical electromyography equipment. Data were compared between different groups. The diagnostic accuracy of SSR and CSP parameters was calculated using the ROC curve. Logistic regression was used to produce an integration model to enhance diagnostic utility. ResultsFoot amplitude, CSP end latency and duration distinguished MSA-P from PD with the area under the curve (AUC) 0.770, 0.806, and 0.776, respectively. Foot and hand SSR amplitude distinguished PD from HC with the AUC 0.871 and 0.768, respectively. Foot SSR amplitude, hand SSR amplitude, and CSP end latency distinguished MSA-P from HC with the AUC 0.964, 0.872, and 0.812, respectively. The combination of SSR and CSP parameters differentiation between MSA-P and PD, PD and HC with the AUC 0.829 and 0.879, respectively. ConclusionsAnalysis of SSR and CSP parameters showed excellent diagnostic accuracy in discriminating patients with early-stage MSA-P from HC and good diagnostic accuracy in discriminating patients with MSA-P from PD with early stages.

Analysis of LRRN3, MEF2C, SLC22A, and P2RY12 Gene Expression in the Peripheral Blood of Patients in the Early Stages of Parkinson's Disease.

Parkinson's disease (PD) is one of the most common human neurodegenerative diseases. Belated diagnoses of PD and late treatment are caused by its elongated prodromal phase. Thus, searching for new candidate genes participating in the development of the pathological process in the early stages of the disease in patients who have not yet received therapy is relevant. Changes in mRNA and protein levels have been described both in the peripheral blood and in the brain of patients with PD. Thus, analysis of changes in the mRNA expression in peripheral blood is of great importance in studying the early stages of PD. This work aimed to analyze the changes in MEF2C, SLC22A4, P2RY12, and LRRN3 gene expression in the peripheral blood of patients in the early stages of PD. We found a statistically relevant and PD-specific change in the expression of the LRRN3 gene, indicating a disruption in the processes of neuronal regeneration and the functioning of synapses. The data obtained during the study indicate that this gene can be considered a potential biomarker of the early stages of PD.

Classification of Parkinson's disease severity using gait stance signals in a spatiotemporal deep learning classifier.

Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.

Association analyses of apolipoprotein E genotypes and cognitive performance in patients with Parkinson’s disease

BackgroundCognitive impairment is a common non-motor symptom of Parkinson’s disease (PD). The apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer’s disease (AD). However, the effect of APOEε4 on cognitive function of PD patients remains unclear. In this study, we aimed to understand whether and how carrying APOEε4 affects cognitive performance in patients with early-stage and advanced PD.MethodsA total of 119 Chinese early-stage PD patients were recruited. Movement Disorder Society Unified Parkinson’s Disease Rating Scale, Hamilton anxiety scale, Hamilton depression scale, non-motor symptoms scale, Mini-mental State Examination, Montreal Cognitive Assessment, and Fazekas scale were evaluated. APOE genotypes were determined by polymerase chain reactions and direct sequencing. Demographic and clinical information of 521 early-stage and 262 advanced PD patients were obtained from Parkinson’s Progression Marker Initiative (PPMI).ResultsNo significant difference in cognitive performance was found between ApoEε4 carriers and non-carriers in early-stage PD patients from our cohort and PPMI. The cerebrospinal fluid (CSF) Amyloid Beta 42 (Aβ42) level was significantly lower in ApoEε4 carrier than non-carriers in early-stage PD patients from PPMI. In advanced PD patients from PPMI, the BJLOT, HVLT retention and SDMT scores seem to be lower in ApoEε4 carriers without reach the statistical significance.ConclusionsAPOEε4 carriage does not affect the cognitive performance of early-stage PD patients. However, it may promote the decline of CSF Aβ42 level and the associated amyloidopathy, which is likely to further contribute to the cognitive dysfunction of PD patients in the advanced stage.

An Ethnographic Study of Patient Life Experience in Early-Stage Parkinson's Disease in the United States and Germany.

Existing qualitative research on early-stage Parkinson's disease draws on patients' reported disease experience, aiming to capture the symptoms and impacts most relevant to patients living with the disease. As a complement to this research, the present study investigated the patient experience of early-stage Parkinson's disease from a holistic, ethnographic perspective. We explored the attitudes, beliefs, and social structures that shape how people understand and adapt to life with early-stage Parkinson's disease. Researchers interviewed 30 people with early-stage Parkinson's disease, 10 relatives, and 10 neurologists and movement disorder specialists in the USA and Germany. Many of these interviews took place in-person, allowing researchers to spend time in participants' homes and witness their daily lives. A multidisciplinary team of social scientists, clinical researchers, and patient organization representatives led the mixed-methods study design and analysis. In-depth ethnographic interviews yielded qualitative insights, with a quantitative survey following to assess their prevalence in a larger sample of 150 patients. In addition to developing a patient life experience pathway of early-stage Parkinson's disease, we identified five key thematic findings that provide insight into how the clinical features of the disease become meaningful to patients on the context of their daily lives, family relations, and subjective well-being: (1) People with early-stage Parkinson's disease start coming to terms with their disease before receiving a medical diagnosis; (2) Acceptance is not a finalized achievement, but a cyclical process; (3) People with early-stage Parkinson's disease "live in the moment" to make the future more manageable; (4) Slowing disease progression is an important goal driving the actions of people with early-stage Parkinson's; and (5) People with early-stage Parkinson's disease value information that is grounded in lived experience and relevant to their stage of disease progression. This holistic, ethnographic approach to patient life experience provided five key thematic findings that complement insights from qualitative and quantitative datasets on early-stage Parkinson's disease. An enhanced understanding of how early-stage Parkinson's symptoms impact patients' health-related quality of life and their broader social lives can help us better understand how patients make decisions about their usage of healthcare services and therapies.

Modulation of response times in early-stage Parkinson’s disease during emotional processing of embodied and non-embodied stimuli

Valence (positive and negative) and content (embodied vs non-embodied) characteristics of visual stimuli have been shown to influence motor readiness, as tested with response time paradigms. Both embodiment and emotional processing are affected in Parkinson’s disease (PD) due to basal ganglia dysfunction. Here we aimed to investigate, using a two-choice response time paradigm, motor readiness when processing embodied (emotional body language [EBL] and emotional facial expressions [FACS]) vs non-embodied (emotional scenes [IAPS]) stimuli with neutral, happy, and fearful content. We enrolled twenty-five patients with early-stage PD and twenty-five age matched healthy participants. Motor response during emotional processing was assessed by measuring response times (RTs) in a home-based, forced two-choice discrimination task where participants were asked to discriminate the emotional stimulus from the neutral one. Rating of valence and arousal was also performed. A clinical and neuropsychological evaluation was performed on PD patients. Results showed that RTs for PD patients were longer for all conditions compared to HC and that RTs were generally longer in both groups for EBL compared to FACS and IAPS, with the sole exception retrieved for PD, where in discriminating fearful stimuli, RTs for EBL were longer compared to FACS but not to IAPS. Furthermore, in PD only, when discriminating fearful respect to neutral stimuli, RTs were shorter when discriminating FACS compared to IAPS. This study shows that PD patients were faster in discriminating fearful embodied stimuli, allowing us to speculate on mechanisms involving an alternative, compensatory, emotional motor pathway for PD patients undergoing fear processing.

Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease.

Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.

Distinct effects of long-term Tai Chi Chuan and aerobic exercise interventions on motor and neurocognitive performance in early-stage Parkinson's disease: a randomized controlled trial.

Parkinson's disease (PD) is a neurodegenerative condition characterized by movement disorders and probable cognitive impairment. Exercise plays an important role in PD management, and recent studies have reported improvement in motor symptoms and cognitive function following aerobic and Tai Chi Chuan exercise. To explore the different effects of Tai Chi Chuan and aerobic exercise on the clinical motor status and neurocognitive performance of patients with early-stage PD. A randomized controlled trial. Parkinson's Disease Center at Kaohsiung Chang Gung Memorial Hospital and National Cheng Kung University Hospital. Patients with idiopathic PD. Fifty-six patients with PD were recruited and divided into three groups: aerobic exercise (AE, N.=14), Tai Chi Chuan exercise (TE, N.=16), and control (CG, N.=13). Before and after a 12-week intervention period, we used unified Parkinson's disease rating scale Part III (UPDRS-III) scores and neuropsychological (e.g., accuracy rates [ARs] and reaction times [RTs]) and neurophysiological (e.g., event-related potential [ERP] N2 and P3 latencies and amplitudes) parameters to respectively assess the patients' clinical motor symptoms and neurocognitive performance when performing a working memory (WM) task. Compared to baseline, UPDRS-III scores were significantly lower in the AE and TE groups after the intervention period, whereas those for the CG group were higher. In terms of the neurocognitive parameters, when performing the WM task after the intervention period, the AE group exhibited significantly faster RTs and larger ERP P3 amplitudes, the TE group exhibited an improvement only in ERP P3 amplitude, and the CG group exhibited a significantly reduced ERP P3 amplitude. However, neither the TE nor the AE group exhibited improved ARs and ERP N2 performance. The present study supported the distinct effectiveness of Tai Chi Chuan and aerobic exercise for improving motor symptoms and providing neurocognitive benefits in PD patients. These results have important implications regarding the use of these exercise interventions for managing PD, particularly in the early stages.

High pH stability and detection of α-synuclein using an EGFET biosensor with an HfO2 gate deposited by high-power pulsed magnetron sputtering

In this research, hafnium oxide (HfO2) was first fabricated in an in-house-developed high-power pulsed magnetron sputtering system (HiPIMS) functionalized for application as a high-performance extended gate field-effect transistor (EGFET) for the detection of pH and proteins associated with Parkinson's disease (PD). PD is the second most common neurodegenerative disease in elderly people after Alzheimer's disease. The α-synuclein protein in patients’ blood may be a potential biomarker in the early stage of PD. Detection of α-synuclein with high sensitivity and nonspecific binding through HfO2-EGFET biosensors has the advantages of low cost and simple fabrication from disposable EGs. Among the duty cycle conditions tested, HfO2 deposited at a 25% duty cycle in HiPIMS exhibited superior pH sensitivity and linearity, with values of 56.4 mV/pH and 99.7%, respectively. The hysteresis width and drift coefficient are −3 mV and 0.3 mV/h, respectively. To overcome the Debye length limitation, 100-fold diluted phosphate buffer solution and the PMOS configuration were used to determine the α-synuclein for which the isoelectric point was 4.67. The negative gate bias in the PMOS configuration successfully causes α-synuclein to approach the surface of HfO2 EG to achieve a sensitivity of 12.1 mV/dec and a linearity of 99.5% in the range of 0.1–1000 pg/mL. The limit of detection is 0.198 pg/mL, and the immobilized surface ultimately binds specifically, resulting in low interference signals from other related biomarkers. This promising study shows that detecting various proteins involved in different diseases and performing clinical tests can be accomplished in the future.

Decreased heart rate variability in sympathetic dominant states in Parkinson's disease and isolated REM sleep behavior disorder

IntroductionParkinson's disease (PD) presents with decreased heart rate variability (HRV) from its early stages. However, most of its evidence originates from HRV measurements in parasympathetic dominant states. In this study, we aimed to examine whether HRV in sympathetic dominant states during the head-up tilt table test (HUT) serves as a marker of autonomic dysfunction in PD and isolated REM sleep behavior disorder (iRBD). MethodsWe retrospectively assessed 102 patients with PD, 10 patients with iRBD, and 43 healthy controls. We then measured the coefficient of variation of RR intervals as an HRV parameter in sympathetic dominant states (CVRR-S) and parasympathetic dominant states (CVRR-P). Furthermore, we evaluated parameters of cardiac autonomic function, including HUT and the heart-to-mediastinum (H/M) ratio of cardiac metaiodobenzylguanidine scintigraphy. ResultsPatients with iRBD and PD at Hoehn and Yahr stage I exhibited a significantly decreased CVRR-S compared to healthy controls (controls vs. iRBD vs. PD; 1.82 ± 0.64 % vs. 1.13 ± 0.41 % vs. 1.15 ± 0.51 %, p < 0.001), although no further deterioration was observed in PD at more severe Hoehn and Yahr stages. CVRR-S showed a significant correlation with the H/M ratio in PD (r = 0.51, p < 0.001). Additionally, receiver operating characteristic (ROC) analysis revealed a larger area under the ROC curve in CVRR-S compared to that in CVRR-P for discriminating PD or iRBD from healthy controls. ConclusionHRV in sympathetic dominant states shows the potential to be a marker of autonomic dysfunction in iRBD and early-stage PD, aiding in early diagnosis and patient stratification.

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.

The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM). In our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model. The hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral "swallow tail" sign region was significant for classification. The integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand.

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Genetic variation and pesticide exposure influence blood DNA methylation signatures in females with early-stage Parkinson’s disease

Although sex, genetics, and exposures can individually influence risk for sporadic Parkinson’s disease (PD), the joint contributions of these factors to the epigenetic etiology of PD have not been comprehensively assessed. Here, we profiled sex-stratified genome-wide blood DNAm patterns, SNP genotype, and pesticide exposure in agricultural workers (71 early-stage PD cases, 147 controls) and explored replication in three independent samples of varying demographics (n = 218, 222, and 872). Using a region-based approach, we found more associations of blood DNAm with PD in females (69 regions) than in males (2 regions, Δβadj| ≥0.03, padj ≤ 0.05). For 48 regions in females, models including genotype or genotype and pesticide exposure substantially improved in explaining interindividual variation in DNAm (padj ≤ 0.05), and accounting for these variables decreased the estimated effect of PD on DNAm. The results suggested that genotype, and to a lesser degree, genotype-exposure interactions contributed to variation in PD-associated DNAm. Our findings should be further explored in larger study populations and in experimental systems, preferably with precise measures of exposure.

A53T α-synuclein mutation increases susceptibility to postoperative delayed neurocognitive recovery via hippocampal Ang-(1–7)/MasR axis

Delayed neurocognitive recovery (dNCR) is a common complication in geriatric surgical patients. The impact of anesthesia and surgery on patients with neurodegenerative diseases, such as Parkinson’s disease (PD) or prion disease, has not yet been reported. In this study, we aimed to determine the association between a pre-existing A53T genetic background, which involves a PD-related point mutation, and the development of postoperative dNCR. We observed that partial hepatectomy induced hippocampus-dependent cognitive deficits in 5-month-old A53T transgenic mice, a model of early-stage PD without cognitive deficits, unlike in age-matched wild-type (WT) mice. We respectively examined molecular changes at 6 h, 1 day, and 2 days after partial hepatectomy and observed that cognitive changes were accompanied by weakened angiotensin-(1–7)/Mas receptor [Ang-(1–7)/MasR] axis, increased alpha-synuclein (α-syn) expression and phosphorylation, decreased methylated protein phosphatase-2A (Me-PP2A), and prompted microglia M1 polarization and neuronal apoptosis in the hippocampus at 1 day after surgery. Nevertheless, no changes in blood–brain barrier (BBB) integrity or plasma α-syn levels in either A53T or WT mice. Furthermore, intranasal administration of selective MasR agonist AVE 0991, reversed the mentioned cognitive deficits in A53T mice, enhanced MasR expression, reduced α-syn accumulation and phosphorylation, and attenuated microglia activation and apoptotic response. Our findings suggest that individuals with the A53T genetic background may be more susceptible to developing postoperative dNCR. This susceptibility could be linked to central α-syn accumulation mediated by the weakened Ang-(1–7)/MasR/methyl-PP2A signaling pathway in the hippocampus following surgery, independent of plasma α-syn level and BBB.

Advanced Recognizing and Categorizing System for Alzheimer's and Parkinson's Syndromes using CNN Algorithm and Clinical Data

Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) are advancing neurodegenerative diseases that impact a large number of individuals globally. This study provides a novel method to identify and categorize individuals in the early stages of PD and AD by analyzing online handwriting structures, leveraging the current progress in Machine Learning (ML) in the medical domain. The research explores multiple data augmentation strategies to address the limited training information available for classifying neuro-degenerative disorders based on behavioral information. The study used a Convolutional Neural Network (CNN) classification as a foundation model by identifying essential radiomics characteristics from the T1-weighted Magnetic Resonance Imaging (MRI) data. The research explores data augmentation strategies designed explicitly for time-series information, such as online handwriting patterns, instead of standard methods used for hardware-based detection. This approach, with more validation, might assist in distinguishing between difficult instances of AD and PD that have a comparable mild motor and non-motor symptoms.