A novel CERNNE approach for predicting Parkinson’s Disease-associated genes and brain regions based on multimodal imaging genetics data

Bi, Xia-an; Hu, Xi; Xie, Yiming; Wu, Hao

doi:10.1016/j.media.2020.101830

Cited by 46 publications

(32 citation statements)

References 45 publications

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“…Typical indicators of brain network segregation are average clustering coefficient, modularity, etc. Clustering coefficient is defined as the number of triangles around a single node; that is, two adjacent nodes of a node are adjacent nodes to each other, which expresses the universality of clustering connectivity around each node [34]. It is usually used to describe the functional isolation of network local information processing [35], reflecting the clustering tightness of the adjacent nodes of each node [36].…”

Section: Functional Connectivity Statesmentioning

confidence: 99%

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

Jiao

Gao

et al. 2021

Contrast Media & Molecular Imaging

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Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Researchers tend to discuss its early state (early MCI, eMCI) due to its high conversion rate of dementia and poor treatment effect in the middle and late stages. Currently, the research on the disease evolution of the brain functional networks of patients with MCI has gradually become a research hotspot. In this study, we compare the differences in dynamic functional connectivity among eMCI, late MCI (lMCI), and normal control (NC) groups, and their graph theory indicators reveal the integration and segregation of functional connectivity states. Firstly, dynamic functional network windows were constructed based on the sliding time window method, and then these window samples were clustered by k-means to extract the functional connectivity states. The differences in the three groups were compared by analyzing the graph theory indicators, such as the participation coefficient, module degree distribution, clustering coefficient, global efficiency, and local efficiency, which distinguish the functional connectivity states. The results reveal that the NC group has the strongest integration and segregation, followed by the eMCI group, and the lMCI group has the weakest integration and segregation. We conclude that with the aggravation of MCI, the integration and segregation of dynamic functional connectivity states tend to decline. The results also reflect that the lMCI group has significantly more brain functional connections in some states, such as IPL.L-MTG.R and DCG.R-SMG.L, than the eMCI group, while the lMCI group has significantly less OLF.L-SPG.L than the NC group.

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Section: Functional Connectivity Statesmentioning

confidence: 99%

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

Jiao

Gao

et al. 2021

Contrast Media & Molecular Imaging

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“…Some studies have suggested that the precuneus is involved in extended face recognition 54,55 and participates in facial emotion perception and context integration 56 . Based on a new neural network analysis model, 57 researchers found that the abnormality of precuneus combines with some risk genes has a good recognition ability for MCI and AD 58,59 . A previous study showed that a decrease in confrontation naming task scores in patients with early frontotemporal lobe degeneration was associated with decreased fMRI signals in the precuneus 60 .…”

Section: Discussionmentioning

confidence: 99%

Changes in local brain function in mild cognitive impairment due to semantic dementia

et al. 2021

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Aims Mild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting‐state functional MRI. Methods We administered a battery of neuropsychological tests to twenty‐two patients with mild cognitive impairment due to semantic dementia (MCI‐SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting‐state functional MRI with multiple sub‐bands and indicators to evaluate functional activity. Results Neuropsychological tests revealed a significant decline in semantic performance in the MCI‐SD group, but no decline in other cognitive domains. Resting‐state functional MRI revealed local functional changes in multiple brain regions in the MCI‐SD group, distributed in different sub‐bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub‐bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI‐SD group was positively correlated with general semantic tasks (oral sound naming, word‐picture verification). Conclusion Our study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency‐dependent sub‐bands, especially the slow‐2 and slow‐3 sub‐bands.

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“…First, the preprocessing step for removing duplication of genes is followed by representing genes as DNA FASTA sequences and removing redundancies of sequences. Second, from the DNA sequences, the most significant features are extracted using different feature extraction 24 2020 Diagnosing between diferent cases lncRNAs DFT, Entropy, RefSeq, GreeNC Complex Network Ensembl (v87, v32) Bi et al 23 2021 Predicting PD-related genes brain regions CERNNE PPMI ACC = 88.57%…”

Section: Methodsmentioning

confidence: 99%

Predicting Parkinson’s Disease Related Genes Based on PyFeat and Gradient Boosted Decision Tree (GBDT)

Helmy

El-Daydamony

Mekky

et al. 2022

Preprint

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Identifying genes related to Parkinson's disease (PD) is an active and effective research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. In recent years, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long non-coding (lncRNA) genes, which play an essential role in biological processes and the Transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the UCSC genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using five numerical mapping techniques with Fourier transform and PyFeat method with Adaboost technique as feature selection. Finally, the features were fed to the gradient boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics are used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy (ACC) equals 78.1%, the area under the curve (AUC) equals 84.9%, the area under precision-recall (AUPR) equals 85.0%, F1-score equals 78.2%, Matthews correlation coefficient (MCC) equals 0.564, Sensitivity (SEN) equals 79.1%, and specificity (SPC) equals 77.1%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.

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A novel CERNNE approach for predicting Parkinson’s Disease-associated genes and brain regions based on multimodal imaging genetics data

Cited by 46 publications

References 45 publications

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

Changes in local brain function in mild cognitive impairment due to semantic dementia

Predicting Parkinson’s Disease Related Genes Based on PyFeat and Gradient Boosted Decision Tree (GBDT)

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