Biosensing and Imaging for Neurodegenerative Diseases

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4276

Special Issue Editor


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Guest Editor
Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
Interests: optical imaging of neurotumors: development of diagnostic and therapeutic integrated probes, neural junctions, and neuroimmunoimaging

Special Issue Information

Dear Colleagues,

Accurate diagnoses are crucial in the clinical diagnosis and treatment of neurodegenerative diseases. Increasing evidence has shown that specific biomarkers are helpful for the early detection, prognosis, and efficacy evaluation of neurodegenerative diseases. Highly sensitive sensing technologies (based on biomarkers derived from cerebrospinal fluid, blood, saliva, urine, or tissues and organs) can effectively measure and identify changes in clinically meaningful outcomes. The combination of biosensing with imaging will be more conducive to the accurate diagnosis of diseases and real-time monitoring of treatments. This Special Issue will provide the state of the art of portable sensors and their integration with new imaging methods in the field of neurodegenerative diseases, focusing on the screening of novel biomarkers derived from the periphery (especially blood, saliva, and urine), the exploration of dual-mode biosensors, and new applications of tissue-clearing for 3D imaging and in vivo multimodality imaging techniques.

Prof. Dr. Haiming Luo
Guest Editor

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Keywords

  • accurate diagnosis
  • biomarkers
  • portable sensors
  • multimodality imaging
  • neurodegenerative diseases

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Published Papers (2 papers)

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Research

23 pages, 1025 KiB  
Article
Parkinson’s Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data
by Pranita Patil and W. Randolph Ford
Biosensors 2024, 14(5), 259; https://doi.org/10.3390/bios14050259 - 19 May 2024
Cited by 1 | Viewed by 1338
Abstract
Parkinson’s disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test [...] Read more.
Parkinson’s disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI—the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction–DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset. Full article
(This article belongs to the Special Issue Biosensing and Imaging for Neurodegenerative Diseases)
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15 pages, 2104 KiB  
Article
Elevated Plasma Oligomeric Amyloid β-42 Is Associated with Cognitive Impairments in Cerebral Small Vessel Disease
by Wensheng Qu, Liding Zhang, Xiaohan Liang, Zhiyuan Yu, Hao Huang, Jing Zhao, Yinping Guo, Xirui Zhou, Shabei Xu, Haiming Luo and Xiang Luo
Biosensors 2023, 13(1), 110; https://doi.org/10.3390/bios13010110 - 7 Jan 2023
Cited by 3 | Viewed by 2065
Abstract
Due to the heterogeneity of amyloid β-42 (Aβ42) species, the potential correlation between plasma oligomeric Aβ42 (oAβ42) and cognitive impairments in cerebral small vessel disease (CSVD) remains unclear. Herein, a sandwich ELISA for the specific detection of Aβ [...] Read more.
Due to the heterogeneity of amyloid β-42 (Aβ42) species, the potential correlation between plasma oligomeric Aβ42 (oAβ42) and cognitive impairments in cerebral small vessel disease (CSVD) remains unclear. Herein, a sandwich ELISA for the specific detection of Aβ42 oligomers (oAβ42) and total Aβ42 (tAβ42) was developed based on sequence- and conformation-specific antibody pairs for the evaluation of plasma samples from a Chinese CSVD community cohort. After age and gender matching, 3-Tesla magnetic resonance imaging and multidimensional cognitive assessment were conducted in 134 CSVD patients and equal controls. The results showed that plasma tAβ42 and oAβ42 levels were significantly elevated in CSVD patients. By regression analysis, these elevations were correlated with the presence of CSVD and its imaging markers (i.e., white matter hyperintensities). Plasma Aβ42 tests further strengthened the predictive power of vascular risk factors for the presence of CSVD. Relative to tAβ42, oAβ42 showed a closer correlation with memory domains evaluated by neuropsychological tests. In conclusion, this sensitive ELISA protocol facilitated the detection of plasma Aβ42; Aβ42, especially its oligomeric form, can serve as a biosensor for the presence of CSVD and associated cognitive impairments represented by memory domains. Full article
(This article belongs to the Special Issue Biosensing and Imaging for Neurodegenerative Diseases)
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