Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning

A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis

The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study's objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual's conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.

Influence of cognitive reserve on cognitive and motor function in α-synucleinopathies: A systematic review and multilevel meta-analysis

Cognitive reserve has shown promise as a justification for neuropathologically unexplainable clinical outcomes in Alzheimer's disease. Recent evidence suggests this effect may be replicated in conditions like Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. However, the relationships between cognitive reserve and different cognitive abilities, as well as motor outcomes, are still poorly understood in these conditions. Additionally, it is unclear whether the reported effects are confounded by medication. This review analysed studies investigating the relationship between cognitive reserve and clinical outcomes in these α-synucleinopathy cohorts, identified from MEDLINE, Scopus, psycINFO, CINAHL, and Web of Science. 85 records, containing 176 cognition and 31 motor function effect sizes, were pooled using multilevel meta-analysis. There was a significant, positive association between higher cognitive reserve and both better cognition and motor function. Cognition effect sizes differed by disease subtype, cognitive reserve measure, and outcome type; however, no moderators significantly impacted motor function. Review findings highlight the clinical implications of cognitive reserve and importance of engaging in reserve-building behaviours.

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review

BACKGROUND

Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes.

METHODS

To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted.

RESULTS

Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy.

CONCLUSIONS

Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

Fifty Years of Handedness Research: A Neurological and Methodological Update

Handedness, a complex human aspect that reflects the functional lateralization of the hemispheres, also interacts with the immune system. This study aimed to expand the knowledge of the lateralization of hand, foot, and eye activities in patients with immune-mediated (IM) or other (noIM) neurological diseases and to clarify the properties of the Edinburgh Handedness Inventory (EHI) in an Italian population. Three hundred thirty-four patients with IM or noIM diseases affecting the brain or spine and peripheral nervous system were interviewed about stressful events preceding the disease, subjective handedness, and familiarity for left-handedness or ambidexterity. The patients and 40 healthy subjects underwent EHI examination. In the whole group of participants, 24 items of the EHI were classified into five factors (Hand Transitive, Hand Refined, Hand Median, Foot, Eye), demonstrating good reliability and validity. Chronological age had a significant influence on hand and foot EHI factors and the laterality quotient (LQ), particularly on writing and painting. In the patient groups, EHI factors and the LQ were also predicted by age of disease onset, duration of disease, and family history of left-handedness or ambidexterity. No differences were found between patients and healthy subjects, but pencil use scored significantly lower in patients with IM diseases than in those with noIM brain diseases. These results demonstrate that the lateralization of hand and foot activities is not a fixed human aspect, but that it can change throughout life, especially for abstract and symbolic activities. Chronic neurological diseases can cause changes in handedness. This may explain why, unlike systemic immunological diseases, IM neurological diseases are not closely associated with left-handedness. In these patients, the long version of the EHI is appropriate for determining the lateralization of body activities to contextualize the neurological picture; therefore, these findings extend the Italian normative data sets.

Neurocognitive Impairment and Social Cognition in Parkinson's Disease Patients

In addition to motor symptoms, neurocognitive impairment (NCI) affects patients with prodromal Parkinson's disease (PD). NCI in PD ranges from subjective cognitive complaints to dementia. The purpose of this review is to present the available evidence of NCI in PD and highlight the heterogeneity of NCI phenotypes as well as the range of factors that contribute to NCI onset and progression. A review of publications related to NCI in PD up to March 2023 was performed using PubMed/Medline. There is an interconnection between the neurocognitive and motor symptoms of the disease, suggesting a common underlying pathophysiology as well as an interconnection between NCI and non-motor symptoms, such as mood disorders, which may contribute to confounding NCI. Motor and non-motor symptom evaluation could be used prognostically for NCI onset and progression in combination with imaging, laboratory, and genetic data. Additionally, the implications of NCI on the social cognition of afflicted patients warrant its prompt management. The etiology of NCI onset and its progression in PD is multifactorial and its effects are equally grave as the motor effects. This review highlights the importance of the prompt identification of subjective cognitive complaints in PD patients and NCI management.

Transcriptome sequencing of circular RNA reveals the involvement of hsa-SCMH1_0001 in the pathogenesis of Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is the second most common neurodegenerative disease. Exosomes are endosome-derived extracellular vesicles that can take part in intercellular communication. Circular RNAs (circRNAs) are noncoding RNAs characterized by covalently closed-loop structures, which perform a crucial function in many diseases.

AIM

To clarify the expression and function of exosomal circRNSs of PD patients and look for circRNAs that might be related to the pathogenesis of PD.

MATERIALS AND METHODS

We examined circRNA and mRNA expression profiles in peripheral exosomes from PD patients (n = 23) and healthy controls (n = 15) using next-generation sequencing (NGS) technology, functional annotation, and quantitative polymerase chain reaction. Correlation analysis was performed between the expression levels of the circRNAs and the clinical characteristics of PD patients. The binding miRNAs and target genes were predicted using TargetScanHuman, miRDB, and miRTarBase. The predicted target genes were compared with the differentially expressed mRNAs in sequencing results.

RESULTS

According to the NGS, 62 upregulated and 37 downregulated circRNAs in the PD group were screened out. Correlation analysis revealed that hsa-SCMH1_0001 has strong clinical relevance. We identified 17 potential binding miRNAs of hsa-SCMH1_0001 with 149 potential target genes. ARID1A and C1orf115 belong to the intersection of the predicted target genes and the differentially expressed mRNAs obtained by sequencing.

CONCLUSION

This study suggested that hsa-SCMH1_0001 and its target genes ARID1A and C1orf115 are downregulated in PD patients and may be involved in the occurrence of PD.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

Genetic risk assessment based on association and prediction studies

The genetic basis of phenotypic emergence provides valuable information for assessing individual risk. While association studies have been pivotal in identifying genetic risk factors within a population, complementing it with insights derived from predictions studies that assess individual-level risk offers a more comprehensive approach to understanding phenotypic expression. In this study, we established personalized risk assessment models using single-nucleotide polymorphism (SNP) data from 200 Korean patients, of which 100 experienced hepatitis B surface antigen (HBsAg) seroclearance and 100 patients demonstrated high levels of HBsAg. The risk assessment models determined the predictive power of the following: (1) genome-wide association study (GWAS)-identified candidate biomarkers considered significant in a reference study and (2) machine learning (ML)-identified candidate biomarkers with the highest feature importance scores obtained by using random forest (RF). While utilizing all features yielded 64% model accuracy, using relevant biomarkers achieved higher model accuracies: 82% for 52 GWAS-identified candidate biomarkers, 71% for three GWAS-identified biomarkers, and 80% for 150 ML-identified candidate biomarkers. Findings highlight that the joint contributions of relevant biomarkers significantly influence phenotypic emergence. On the other hand, combining ML-identified candidate biomarkers into the pool of GWAS-identified candidate biomarkers resulted in the improved predictive accuracy of 90%, demonstrating the capability of ML as an auxiliary analysis to GWAS. Furthermore, some of the ML-identified candidate biomarkers were found to be linked with hepatocellular carcinoma (HCC), reinforcing previous claims that HCC can still occur despite the absence of HBsAg.

Motor symptoms in Parkinson's disease are related to the interplay between cortical curvature and thickness

INTRODUCTION

Brain atrophy in Parkinson's disease occurs to varying degrees in different brain regions, even at the early stage of the disease. While cortical morphological features are often considered independently in structural brain imaging studies, research on the co-progression of different cortical morphological measurements could provide new insights regarding the progression of PD. This study's aim was to examine the interplay between cortical curvature and thickness as a function of PD diagnosis, motor symptoms, and cognitive performance.

METHODS

A total of 359 de novo PD patients and 159 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI) database were included in this study. Additionally, an independent cohort from four databases (182 PD, 132 HC) with longer disease durations was included to assess the effects of PD diagnosis in more advanced cases. Pearson correlation was used to determine subject-specific associations between cortical curvature and thickness estimated from T1-weighted MRI images. General linear modeling (GLM) was then used to assess the effect of PD diagnosis, motor symptoms, and cognitive performance on the curvature-thickness association. Next, longitudinal changes in the curvature-thickness correlation as well as the predictive effect of the cortical curvature-thickness association on changes in motor symptoms and cognitive performance across four years were investigated. Finally, Akaike information criterion (AIC) was used to build a GLM to model PD motor symptom severity cross-sectionally.

RESULTS

A significant interaction effect between PD motor symptoms and age on the curvature-thickness correlation was found (β_standardized = 0.11; t(350) = 2.12; p = 0.03). This interaction effect showed that motor symptoms in older patients were related to an attenuated curvature-thickness association. No significant effect of PD diagnosis was observed for the PPMI database (β = 0.03; t(510) = 0.35; p = 0.72). However, in patients with a longer disease duration, a significant effect of diagnosis on the curvature-thickness association was found (β_standardized = 0.31; t(306.7) = 3.49; p = 0.0006). Moreover, rigidity, but not tremor, in PD was significantly related to the curvature-thickness correlation (β_standardized = 0.11, t(350) = 2.24, p = 0.03; β_standardized = -0.03, t(350) = -0.58, p = 0.56, respectively). The curvature-thickness association was attenuated over time in both PD and HC, but the two groups did not show a significantly different effect (β_standardized = 0.03, t(184.7) = 0.78, p = 0.44). No predictive effects of the CC-CT correlation on longitudinal changes in cognitive performance or motor symptoms were observed (all p-values > 0.05). The best cross-sectional model for PD motor symptoms included the curvature-thickness correlation, cognitive performance, and putamen dopamine transporter (DAT) binding, which together explained 14 % of variance.

CONCLUSION

The association between cortical curvature and thickness is related to PD motor symptoms and age. This research shows the potential of modeling the curvature-thickness interplay in PD.

A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

Internetwork Connectivity Predicts Cognitive Decline in Parkinson’s and Is Altered by Genetic Variants

In Parkinson’s disease (PD) functional changes in the brain occur years before significant cognitive symptoms manifest yet core large-scale networks that maintain cognition and predict future cognitive decline are poorly understood. The present study investigated internetwork functional connectivity of visual (VN), anterior and posterior default mode (aDMN, pDMN), left/right frontoparietal (LFPN, RFPN), and salience (SN) networks in 63 cognitively normal PD (PDCN) and 43 healthy controls who underwent resting-state functional MRI. The functional relevance of internetwork coupling topologies was tested by their correlations with baseline cognitive performance in each group and with 2-year cognitive changes in a PDCN subsample. To disentangle heterogeneity in neurocognitive functioning, we also studied whether α-synuclein (SNCA) and microtubule-associated protein tau (MAPT) variants alter internetwork connectivity and/or accelerate cognitive decline. We found that internetwork connectivity was largely preserved in PDCN, except for reduced pDMN-RFPN/LFPN couplings, which correlated with poorer baseline global cognition. Preserved internetwork couplings also correlated with domain-specific cognition but differently for the two groups. In PDCN, stronger positive internetwork coupling topologies correlated with better cognition at baseline, suggesting a compensatory mechanism arising from less effective deployment of networks that supported cognition in healthy controls. However, stronger positive internetwork coupling topologies typically predicted greater longitudinal decline in most cognitive domains, suggesting that they were surrogate markers of neuronal vulnerability. In this regard, stronger aDMN-SN, LFPN-SN, and/or LFPN-VN connectivity predicted longitudinal decline in attention, working memory, executive functioning, and visual cognition, which is a risk factor for dementia. Coupling strengths of some internetwork topologies were altered by genetic variants. PDCN carriers of the SNCA risk allele showed amplified anticorrelations between the SN and the VN/pDMN, which supported cognition in healthy controls, but strengthened pDMN-RFPN connectivity, which maintained visual memory longitudinally. PDCN carriers of the MAPT risk allele showed greater longitudinal decline in working memory and increased VN-LFPN connectivity, which in turn predicted greater decline in visuospatial processing. Collectively, the results suggest that cognition is maintained by functional reconfiguration of large-scale internetwork communications, which are partly altered by genetic risk factors and predict future domain-specific cognitive progression.

The Impact of SNCA Variations and Its Product Alpha-Synuclein on Non-Motor Features of Parkinson's Disease

Parkinson’s disease (PD) is a common and progressive neurodegenerative disease, caused by the loss of dopaminergic neurons in the substantia nigra pars compacta in the midbrain, which is clinically characterized by a constellation of motor and non-motor manifestations. The latter include hyposmia, constipation, depression, pain and, in later stages, cognitive decline and dysautonomia. The main pathological features of PD are neuronal loss and consequent accumulation of Lewy bodies (LB) in the surviving neurons. Alpha-synuclein (α-syn) is the main component of LB, and α-syn aggregation and accumulation perpetuate neuronal degeneration. Mutations in the α-syn gene (SNCA) were the first genetic cause of PD to be identified. Generally, patients carrying SNCA mutations present early-onset parkinsonism with severe and early non-motor symptoms, including cognitive decline. Several SNCA polymorphisms were also identified, and some of them showed association with non-motor manifestations. The functional role of these polymorphisms is only partially understood. In this review we explore the contribution of SNCA and its product, α-syn, in predisposing to the non-motor manifestations of PD.