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Auer T, Goldthorpe R, Peach R, Hebron H, Violante IR. Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. Hum Brain Mapp 2024; 45:e26687. [PMID: 38651629 PMCID: PMC11036379 DOI: 10.1002/hbm.26687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/22/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.
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Affiliation(s)
- Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordUK
| | | | | | - Henry Hebron
- School of PsychologyUniversity of SurreyGuildfordUK
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Yuan Z, Yang X, Hu Z, Gao Y, Yan P, Zheng F, Hong K, Cen K, Mai Y, Bai Y, Guo Y, Zhou J. Investigating the impact of inflammatory response-related genes on renal fibrosis diagnosis: a machine learning-based study with experimental validation. J Biomol Struct Dyn 2024:1-13. [PMID: 38381715 DOI: 10.1080/07391102.2024.2317992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/07/2024] [Indexed: 02/23/2024]
Abstract
Renal fibrosis plays a crucial role in the progression of renal diseases, yet the lack of effective diagnostic markers poses challenges in scientific and clinical practices. In this study, we employed machine learning techniques to identify potential biomarkers for renal fibrosis. Utilizing two datasets from the GEO database, we applied LASSO, SVM-RFE and RF algorithms to screen for differentially expressed genes related to inflammatory responses between the renal fibrosis group and the control group. As a result, we identified four genes (CCL5, IFITM1, RIPK2, and TNFAIP6) as promising diagnostic indicators for renal fibrosis. These genes were further validated through in vivo experiments and immunohistochemistry, demonstrating their utility as reliable markers for assessing renal fibrosis. Additionally, we conducted a comprehensive analysis to explore the relationship between these candidate biomarkers, immunity, and drug sensitivity. Integrating these findings, we developed a nomogram with a high discriminative ability, achieving a concordance index of 0.933, enabling the prediction of disease risk in patients with renal fibrosis. Overall, our study presents a predictive model for renal fibrosis and highlights the significance of four potential biomarkers, facilitating clinical diagnosis and personalized treatment. This finding presents valuable insights for advancing precision medicine approaches in the management of renal fibrosis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ziwei Yuan
- Department of Endocrinology, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xuejia Yang
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zujian Hu
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanyuan Gao
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Penghua Yan
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Fan Zheng
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kai Hong
- Department of General Surgery, Ningbo First Hospital, Ningbo, China
| | - Kenan Cen
- Department of General Surgery, Ningbo First Hospital, Ningbo, China
| | - Yifeng Mai
- Department of General Surgery, Ningbo First Hospital, Ningbo, China
| | - Yongheng Bai
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yangyang Guo
- Department of General Surgery, Ningbo First Hospital, Ningbo, China
| | - Jingzong Zhou
- Department of Endocrinology, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Taizhou, Zhejiang, China
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3
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Sun Q, Xu X, Liu S, Wu X, Yin C, Wu M, Chen Y, Niu N, Chen L, Bai F. Mo Single-Atom Nanozyme Anchored to the 2D N-Doped Carbon Film: Catalytic Mechanism, Visual Monitoring of Choline, and Evaluation of Intracellular ROS Generation. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37466481 DOI: 10.1021/acsami.3c04761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Single-atom nanozymes (SANs) have attracted great attention in constructing devices for instant biosensing due to their excellent stability and atom utilization. Here, Mo atoms were immobilized in 2D nitrogen-doped carbon films by cascade-anchored one-pot pyrolysis to obtain Mo single-atom nanozyme (Mo-SAN) with high atomic loading (4.79 wt %) and peroxidase-like activity. The coordination environment and enzyme-like activity mechanism of Mo-SAN were studied by combining synchrotron radiation and density functional theory. The strong oxophilicity of single-atom Mo makes the catalytic center more capable of transferring electrons to free radicals to selectively generate •OH in the presence of H2O2. Choline oxidase and Mo-SAN were used as signal opening unit and signal amplification unit, respectively. Combining the portability and visualization functions of smartphone and test strips, a paper-based visual sensing platform was constructed, which can accurately identify choline at a concentration of 0.5-35 μM with a limit of detection as low as 0.12 μM. The recovery of human serum samples was 96.4-102.2%, with an error of less than 5%. Furthermore, the potential of Mo-SAN to efficiently generate toxic •OH in tumor cells was intuitively confirmed. This work provides a technical and theoretical basis for designing highly active SANs and detecting neurological markers.
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Affiliation(s)
- Qijun Sun
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Xiaoyu Xu
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Song Liu
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Xinzhao Wu
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Changchun 130023, P. R. China
| | - Chenhui Yin
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Meng Wu
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Yuxue Chen
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Na Niu
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Ligang Chen
- College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, P. R. China
| | - Fuquan Bai
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Changchun 130023, P. R. China
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Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08350-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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5
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Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques. Sci Rep 2022; 12:22547. [PMID: 36581646 PMCID: PMC9800369 DOI: 10.1038/s41598-022-26644-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.
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Pinotsis DA, Fitzgerald S, See C, Sementsova A, Widge AS. Toward biophysical markers of depression vulnerability. Front Psychiatry 2022; 13:938694. [PMID: 36329919 PMCID: PMC9622949 DOI: 10.3389/fpsyt.2022.938694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.
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Affiliation(s)
- D. A. Pinotsis
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - S. Fitzgerald
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
| | - C. See
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. Sementsova
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
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Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:ijms23116079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
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8
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Wu EQ, Peng XY, Chen SD, Zhao XY, Tang ZR. Detecting Alzheimer’s Dementia Degree. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2020.3015131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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9
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Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:349-361. [PMID: 34862559 DOI: 10.1007/978-3-030-85292-4_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.
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Sone JY, Li Y, Hobson N, Romanos SG, Srinath A, Lyne SB, Shkoukani A, Carrión-Penagos J, Stadnik A, Piedad K, Lightle R, Moore T, Li Y, Bi D, Shenkar R, Carroll T, Ji Y, Girard R, Awad IA. Perfusion and permeability as diagnostic biomarkers of cavernous angioma with symptomatic hemorrhage. J Cereb Blood Flow Metab 2021; 41:2944-2956. [PMID: 34039038 PMCID: PMC8756480 DOI: 10.1177/0271678x211020587] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Cavernous angiomas with symptomatic hemorrhage (CASH) have a high risk of rebleeding, and hence an accurate diagnosis is needed. With blood flow and vascular leak as established mechanisms, we analyzed perfusion and permeability derivations of dynamic contrast-enhanced quantitative perfusion (DCEQP) MRI in 745 lesions of 205 consecutive patients. Thirteen respective derivations of lesional perfusion and permeability were compared between lesions that bled within a year prior to imaging (N = 86), versus non-CASH (N = 659) using machine learning and univariate analyses. Based on logistic regression and minimizing the Bayesian information criterion (BIC), the best diagnostic biomarker of CASH within the prior year included brainstem lesion location, sporadic genotype, perfusion skewness, and high-perfusion cluster area (BIC = 414.9, sensitivity = 74%, specificity = 87%). Adding a diagnostic plasma protein biomarker enhanced sensitivity to 100% and specificity to 85%. A slightly modified derivation achieved similar accuracy (BIC = 321.6, sensitivity = 80%, specificity = 82%) in the cohort where CASH occurred 3-12 months prior to imaging after signs of hemorrhage would have disappeared on conventional MRI sequences. Adding the same plasma biomarker enhanced sensitivity to 100% and specificity to 87%. Lesional blood flow on DCEQP may distinguish CASH after hemorrhagic signs on conventional MRI have disappeared and are enhanced in combination with a plasma biomarker.
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Affiliation(s)
- Je Yeong Sone
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Yan Li
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA.,Center for Research Informatics, University of Chicago, Chicago, USA
| | - Nicholas Hobson
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Sharbel G Romanos
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Abhinav Srinath
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Seán B Lyne
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Abdallah Shkoukani
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Julián Carrión-Penagos
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Agnieszka Stadnik
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Kristina Piedad
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Rhonda Lightle
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Thomas Moore
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Ying Li
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Dehua Bi
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA.,Department of Public Health Sciences, University of Chicago, Chicago, USA
| | - Robert Shenkar
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Timothy Carroll
- Department of Diagnostic Radiology, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, USA
| | - Romuald Girard
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
| | - Issam A Awad
- Neurovascular Surgery Program, Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, USA
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Sone JY, Hobson N, Srinath A, Romanos SG, Li Y, Carrión-Penagos J, Shkoukani A, Stadnik A, Piedad K, Lightle R, Moore T, DeBiasse D, Bi D, Shenkar R, Carroll T, Ji Y, Girard R, Awad IA. Perfusion and Permeability MRI Predicts Future Cavernous Angioma Hemorrhage and Growth. J Magn Reson Imaging 2021; 55:1440-1449. [PMID: 34558140 PMCID: PMC8942875 DOI: 10.1002/jmri.27935] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Cerebral cavernous angioma (CA) is a capillary vasculopathy affecting more than a million Americans with a small fraction of cases demonstrating lesional bleed or growth with major clinical sequelae. Perfusion and permeability are fundamental features of CA pathophysiology, but their role as prognostic biomarkers is unclear. PURPOSE To investigate whether perfusion or permeability lesional descriptors derived from dynamic contrast-enhanced quantitative perfusion (DCEQP) magnetic resonance imaging (MRI) can predict subsequent lesional bleed/growth in the year following imaging. STUDY TYPE Single-site case-controlled study. SUBJECTS Two hundred and five consecutively enrolled patients (63.4% female). FIELD STRENGTH/SEQUENCE Three-Tesla/T1 -mapping with contrast-enhanced dynamic two-dimensional (2D) spoiled gradient recalled acquisition (SPGR) sequences. ASSESSMENT Prognostic associations with bleed/growth (present or absent) in the following year were assessed in 745 CA lesions evaluated by DCEQP in the 205 patients in relation to lesional descriptors calculated from permeability and perfusion maps. A subgroup of 30 cases also underwent peripheral blood collection at the time of DCEQP scans and assays of plasma levels of soluble CD14, IL-1β, VEGF, and soluble ROBO4 proteins, whose weighted combination had been previously reported in association with future CA bleeding. STATISTICAL TESTS Mann-Whitney U-test for univariate analyses. Logistic regression models minimizing the Bayesian information criterion (BIC), testing sensitivity and specificity (receiver operating characteristic curves) of weighted combinations of parameters. RESULTS The best prognostic biomarker for lesional bleed or growth included brainstem lesion location, mean lesional permeability, and low-value perfusion cluster mean (BIC = 201.5, sensitivity = 77%, specificity = 72%, P < 0.05). Adding a previously published prognostic plasma protein biomarker improved the performance of the imaging model (sensitivity = 100%, specificity = 88%, P < 0.05). DATA CONCLUSION A combination of MRI-based descriptors reflecting higher lesional permeability and lower perfusion cluster may potentially predict future bleed/growth in CAs. The sensitivity and specificity of the prognostic imaging biomarker can be enhanced when combined with brainstem lesion location and a plasma protein biomarker of CA hemorrhage. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Je Yeong Sone
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Nicholas Hobson
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Abhinav Srinath
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Sharbel G Romanos
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Ying Li
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Julián Carrión-Penagos
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Abdallah Shkoukani
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Agnieszka Stadnik
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Kristina Piedad
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Rhonda Lightle
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Thomas Moore
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Dorothy DeBiasse
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Dehua Bi
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA.,Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Robert Shenkar
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Timothy Carroll
- Department of Diagnostic Radiology, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Romuald Girard
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
| | - Issam A Awad
- Neurovascular Surgery Program, Department of Neurosurgery, University of Chicago Medicine and Biological Sciences, Chicago, Illinois, USA
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Gu X, Cao Z, Jolfaei A, Xu P, Wu D, Jung TP, Lin CT. EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1645-1666. [PMID: 33465029 DOI: 10.1109/tcbb.2021.3052811] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
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Yang W, Pilozzi A, Huang X. An Overview of ICA/BSS-Based Application to Alzheimer's Brain Signal Processing. Biomedicines 2021; 9:386. [PMID: 33917280 PMCID: PMC8067382 DOI: 10.3390/biomedicines9040386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer's patients have been acquired and processed from which AD-related information or "signals" can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.
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Affiliation(s)
- Wenlu Yang
- Department of Electrical Engineering, Information Engineering College, Shanghai Maritime University, Shanghai 200135, China;
| | - Alexander Pilozzi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA;
| | - Xudong Huang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA;
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Yu WY, Low I, Chen C, Fuh JL, Chen LF. Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer's Disease and Mild Cognitive Impairment. ENTROPY (BASEL, SWITZERLAND) 2021; 23:427. [PMID: 33916588 PMCID: PMC8066899 DOI: 10.3390/e23040427] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 12/22/2022]
Abstract
Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer's disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a "transitional change" between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.
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Grants
- AS-BD-108-2 Academia Sinica, Taiwan
- MOST 109-2314-B-010-027, 107-2221-E-010-013, 109-2811-E-010-503, 108-2321-B-075-001, 109-2314-B-075-052-MY2 Ministry of Science and Technology, Taiwan
- VGHUST 110-G1-5-1, 110-G1-5-2, 109-V1-5-1, 109-V1-5-2 Veterans General Hospitals-University System of Taiwan Joint Research Program
- V110C-057 Taipei Veterans General Hospital
- Brain Research Center, National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project Taiwan Ministry of Education
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Affiliation(s)
- Wei-Yang Yu
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
| | - Intan Low
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Chien Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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15
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Tang ZQ, Zhao L, Chen GX, Chen CYC. Novel and versatile artificial intelligence algorithms for investigating possible GHSR1α and DRD1 agonists for Alzheimer's disease. RSC Adv 2021; 11:6423-6446. [PMID: 35423219 PMCID: PMC8694922 DOI: 10.1039/d0ra10077c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 01/18/2021] [Indexed: 11/21/2022] Open
Abstract
Hippocampal lesions are recognized as the earliest pathological changes in Alzheimer's disease (AD). Recent researches have shown that the co-activation of growth hormone secretagogue receptor 1α (GHSR1α) and dopamine receptor D1 (DRD1) could recover the function of hippocampal synaptic and cognition. We combined traditional virtual screening technology with artificial intelligence models to screen multi-target agonists for target proteins from TCM database and a novel boost Generalized Regression Neural Network (GRNN) model was proposed in this article to improve the poor adjustability of GRNN. R-square was chosen to evaluate the accuracy of these artificial intelligent models. For the GHSR1α agonist dataset, Adaptive Boosting (AdaBoost), Linear Ridge Regression (LRR), Support Vector Machine (SVM), and boost GRNN achieved good results; the R-square of the test set of these models reached 0.900, 0.813, 0.708, and 0.802, respectively. For the DRD1 agonist dataset, Gradient Boosting (GB), Random Forest (RF), SVM, and boost GRNN achieved good results; the R-square of the test set of these models reached 0.839, 0.781, 0.763, and 0.815, respectively. According to these values of R-square, it is obvious that boost GRNN and SVM have better adaptability for different data sets and boost GRNN is more accurate than SVM. To evaluate the reliability of screening results, molecular dynamics (MD) simulation experiments were performed to make sure that candidates were docked well in the protein binding site. By analyzing the results of these artificial intelligent models and MD experiments, we suggest that 2007_17103 and 2007_13380 are the possible dual-target drugs for Alzheimer's disease (AD).
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Affiliation(s)
- Zi-Qiang Tang
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
| | - Lu Zhao
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China
| | - Guan-Xing Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
- Department of Medical Research, China Medical University Hospital Taichung 40447 Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University Taichung 41354 Taiwan
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16
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Effects of short- and long-term neurostimulation (tDCS) on Alzheimer's disease patients: two randomized studies. Aging Clin Exp Res 2021; 33:383-390. [PMID: 32301028 DOI: 10.1007/s40520-020-01546-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/30/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Non-invasive brain stimulation is an effective treatment for Alzheimer's disease. AIMS The purpose of the two studies presented here is to compare the short- and long-term effects of transcranial direct current stimulation (t-DCS) on two samples of advanced AD patients. METHODS In Study 1 26 patients were involved in a 10-day anodal vs. sham tDCS intervention stimulating the left frontotemporal cortex. A pre-post test assessment was run using two different neurocognitive tests and EEG data. The same protocol was used in Study 2, which involved 18 different patients who underwent the same intervention 10 days a month for 8 months. RESULTS Results confirmed how the t-DCS intervention was effective both in the short- and the long-term to slow down the progression of AD on specific neurophysiological domains and, to a certain extent, on neurophysiological activity. Discussion tDCS appear to be effective and to affect differently neurocognitive and neurophysiological functions when comparing short and long-term outcomes. Conclusions Anodal-tDCS is an effective way to slow down the progression of Alzheimer's both in the short and long term. It can also affect the EEG patterns, but this requires a more protracted intervention.
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Ge Q, Lin ZC, Gao YX, Zhang JX. A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease. Healthcare (Basel) 2020; 8:healthcare8040476. [PMID: 33187374 PMCID: PMC7712949 DOI: 10.3390/healthcare8040476] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/08/2020] [Accepted: 11/09/2020] [Indexed: 01/21/2023] Open
Abstract
(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain’s electrical activity, can be a promising diagnostic tool for Alzheimer’s disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding’s D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.
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Pharmaco-fUS: Quantification of pharmacologically-induced dynamic changes in brain perfusion and connectivity by functional ultrasound imaging in awake mice. Neuroimage 2020; 222:117231. [DOI: 10.1016/j.neuroimage.2020.117231] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 07/24/2020] [Accepted: 07/31/2020] [Indexed: 11/20/2022] Open
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Tait L, Tamagnini F, Stothart G, Barvas E, Monaldini C, Frusciante R, Volpini M, Guttmann S, Coulthard E, Brown JT, Kazanina N, Goodfellow M. EEG microstate complexity for aiding early diagnosis of Alzheimer's disease. Sci Rep 2020; 10:17627. [PMID: 33077823 PMCID: PMC7572485 DOI: 10.1038/s41598-020-74790-7] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 10/05/2020] [Indexed: 11/25/2022] Open
Abstract
The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer's disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.
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Affiliation(s)
- Luke Tait
- Living Systems Institute, University of Exeter, Exeter, UK.
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.
- College of Engineering, Maths, and Physical Sciences, University of Exeter, Exeter, UK.
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
| | - Francesco Tamagnini
- School of Pharmacy, University of Reading, Reading, UK
- University of Exeter Medical School, Exeter, UK
| | | | - Edoardo Barvas
- San Marino Neurological Unit, San Marino Hospital, San Marino, Republic of San Marino
| | - Chiara Monaldini
- San Marino Neurological Unit, San Marino Hospital, San Marino, Republic of San Marino
| | - Roberto Frusciante
- San Marino Neurological Unit, San Marino Hospital, San Marino, Republic of San Marino
| | - Mirco Volpini
- San Marino Neurological Unit, San Marino Hospital, San Marino, Republic of San Marino
| | - Susanna Guttmann
- San Marino Neurological Unit, San Marino Hospital, San Marino, Republic of San Marino
| | | | - Jon T Brown
- University of Exeter Medical School, Exeter, UK
| | - Nina Kazanina
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
- College of Engineering, Maths, and Physical Sciences, University of Exeter, Exeter, UK
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK
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20
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Levitt J, Edhi MM, Thorpe RV, Leung JW, Michishita M, Koyama S, Yoshikawa S, Scarfo KA, Carayannopoulos AG, Gu W, Srivastava KH, Clark BA, Esteller R, Borton DA, Jones SR, Saab CY. Pain phenotypes classified by machine learning using electroencephalography features. Neuroimage 2020; 223:117256. [PMID: 32871260 PMCID: PMC9084327 DOI: 10.1016/j.neuroimage.2020.117256] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 07/24/2020] [Accepted: 08/07/2020] [Indexed: 12/26/2022] Open
Abstract
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.
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Affiliation(s)
- Joshua Levitt
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Muhammad M Edhi
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Ryan V Thorpe
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Jason W Leung
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Mai Michishita
- Laboratory for Pharmacology, Asahi Kasei Pharma Corporation, Mifuku, Shizuoka, Japan
| | - Suguru Koyama
- Laboratory for Pharmacology, Asahi Kasei Pharma Corporation, Mifuku, Shizuoka, Japan
| | - Satoru Yoshikawa
- Laboratory for Pharmacology, Asahi Kasei Pharma Corporation, Mifuku, Shizuoka, Japan
| | - Keith A Scarfo
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | | | - Wendy Gu
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | | | - Bryan A Clark
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | - Rosana Esteller
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | - David A Borton
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Carl Y Saab
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States; Department of Neuroscience, Brown University, Providence, RI, United States.
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21
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Gao Y, Wang YT, Chen Y, Wang H, Young D, Shi T, Song Y, Schepmoes AA, Kuo C, Fillmore TL, Qian WJ, Smith RD, Srivastava S, Kagan J, Dobi A, Sesterhenn IA, Rosner IL, Petrovics G, Rodland KD, Srivastava S, Cullen J, Liu T. Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression. Cancers (Basel) 2020; 12:cancers12051268. [PMID: 32429558 PMCID: PMC7281161 DOI: 10.3390/cancers12051268] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/07/2020] [Accepted: 05/11/2020] [Indexed: 01/17/2023] Open
Abstract
Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.
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Affiliation(s)
- Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Yi-Ting Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Yongmei Chen
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Hui Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Denise Young
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Tujin Shi
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Yingjie Song
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Athena A. Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Claire Kuo
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Thomas L. Fillmore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Richard D. Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
| | - Sudhir Srivastava
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892, USA; (S.S.); (J.K.)
| | - Jacob Kagan
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892, USA; (S.S.); (J.K.)
| | - Albert Dobi
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | | | - Inger L. Rosner
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Gyorgy Petrovics
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Karin D. Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
- Department of Cell, Developmental, and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, USA
- Correspondence: (K.D.R.); (J.C.); (T.L.)
| | - Shiv Srivastava
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
| | - Jennifer Cullen
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA; (Y.C.); (D.Y.); (Y.S.); (C.K.); (A.D.); (G.P.)
- Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (I.L.R.); (S.S.)
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
- Correspondence: (K.D.R.); (J.C.); (T.L.)
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (Y.G.); (Y.-T.W.); (H.W.); (T.S.); (A.A.S.); (T.L.F.); (W.-J.Q.); (R.D.S.)
- Correspondence: (K.D.R.); (J.C.); (T.L.)
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22
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Kosciessa JQ, Kloosterman NA, Garrett DD. Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it? PLoS Comput Biol 2020; 16:e1007885. [PMID: 32392250 PMCID: PMC7241858 DOI: 10.1371/journal.pcbi.1007885] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 05/21/2020] [Accepted: 04/18/2020] [Indexed: 01/10/2023] Open
Abstract
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.
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Affiliation(s)
- Julian Q. Kosciessa
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Niels A. Kloosterman
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Douglas D. Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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Predicting Short-term Survival after Liver Transplantation using Machine Learning. Sci Rep 2020; 10:5654. [PMID: 32221367 PMCID: PMC7101323 DOI: 10.1038/s41598-020-62387-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 03/10/2020] [Indexed: 02/07/2023] Open
Abstract
Liver transplantation is one of the most effective treatments for end-stage liver disease, but the demand for livers is much higher than the available donor livers. Model for End-stage Liver Disease (MELD) score is a commonly used approach to prioritize patients, but previous studies have indicated that MELD score may fail to predict well for the postoperative patients. This work proposes to use data-driven approach to devise a predictive model to predict postoperative survival within 30 days based on patient’s preoperative physiological measurement values. We use random forest (RF) to select important features, including clinically used features and new features discovered from physiological measurement values. Moreover, we propose a new imputation method to deal with the problem of missing values and the results show that it outperforms the other alternatives. In the predictive model, we use patients’ blood test data within 1–9 days before surgery to construct the model to predict postoperative patients’ survival. The experimental results on a real data set indicate that RF outperforms the other alternatives. The experimental results on the temporal validation set show that our proposed model achieves area under the curve (AUC) of 0.771 and specificity of 0.815, showing superior discrimination power in predicting postoperative survival.
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Durongbhan P, Zhao Y, Chen L, Zis P, De Marco M, Unwin ZC, Venneri A, He X, Li S, Zhao Y, Blackburn DJ, Sarrigiannis PG. A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2019; 27:826-835. [DOI: 10.1109/tnsre.2019.2909100] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Tzimourta KD, Giannakeas N, Tzallas AT, Astrakas LG, Afrantou T, Ioannidis P, Grigoriadis N, Angelidis P, Tsalikakis DG, Tsipouras MG. EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions. Brain Sci 2019; 9:E81. [PMID: 31013964 PMCID: PMC6523667 DOI: 10.3390/brainsci9040081] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/10/2019] [Accepted: 04/10/2019] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Medical Physics, Medical School, University of Ioannina, GR45110 Ioannina, Greece.
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece.
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece.
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, GR45110 Ioannina, Greece.
| | - Theodora Afrantou
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece.
| | - Panagiotis Ioannidis
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece.
| | - Nikolaos Grigoriadis
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece.
| | - Pantelis Angelidis
- Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece.
| | - Dimitrios G Tsalikakis
- Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece.
| | - Markos G Tsipouras
- Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece.
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Dauwan M, Hoff JI, Vriens EM, Hillebrand A, Stam CJ, Sommer IE. Aberrant resting-state oscillatory brain activity in Parkinson's disease patients with visual hallucinations: An MEG source-space study. Neuroimage Clin 2019; 22:101752. [PMID: 30897434 PMCID: PMC6425119 DOI: 10.1016/j.nicl.2019.101752] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 02/18/2019] [Accepted: 03/09/2019] [Indexed: 12/31/2022]
Abstract
To gain insight into possible underlying mechanism(s) of visual hallucinations (VH) in Parkinson's disease (PD), we explored changes in local oscillatory activity in different frequency bands with source-space magnetoencephalography (MEG). Eyes-closed resting-state MEG recordings were obtained from 20 PD patients with hallucinations (Hall+) and 20 PD patients without hallucinations (Hall-), matched for age, gender and disease severity. The Hall+ group was subdivided into 10 patients with VH only (unimodal Hall+) and 10 patients with multimodal hallucinations (multimodal Hall+). Subsequently, neuronal activity at source-level was reconstructed using an atlas-based beamforming approach resulting in source-space time series for 78 cortical and 12 subcortical regions of interest in the automated anatomical labeling (AAL) atlas. Peak frequency (PF) and relative power in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) were compared between Hall+ and Hall-, unimodal Hall+ and Hall-, multimodal Hall+ and Hall-, and unimodal Hall+ and multimodal Hall+ patients. PF and relative power per frequency band did not differ between Hall+ and Hall-, and multimodal Hall+ and Hall- patients. Compared to the Hall- group, unimodal Hall+ patients showed significantly higher relative power in the theta band (p = 0.005), and significantly lower relative power in the beta (p = 0.029) and gamma (p = 0.007) band, and lower PF (p = 0.011). Compared to the unimodal Hall+, multimodal Hall+ showed significantly higher PF (p = 0.007). In conclusion, a subset of PD patients with only VH showed slowing of MEG-based resting-state brain activity with an increase in theta activity, and a concomitant decrease in beta and gamma activity, which could indicate central cholinergic dysfunction as underlying mechanism of VH in PD. This signature was absent in PD patients with multimodal hallucinations.
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Affiliation(s)
- M Dauwan
- Neuroimaging Center, University Medical Center Groningen, University of Groningen, Neuroimaging Center 3111, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands; Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Postbus 7057, 1007 MB Amsterdam, the Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Postbus 85500, 3508 GA Utrecht, the Netherlands.
| | - J I Hoff
- Department of Neurology, St. Antonius Ziekenhuis, Nieuwegein, Utrecht, the Netherlands
| | - E M Vriens
- Department of Neurology, Diakonessenhuis Utrecht, the Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Postbus 7057, 1007 MB Amsterdam, the Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Postbus 7057, 1007 MB Amsterdam, the Netherlands
| | - I E Sommer
- Neuroimaging Center, University Medical Center Groningen, University of Groningen, Neuroimaging Center 3111, Antonius Deusinglaan 2, 9713 AW Groningen, the Netherlands; Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Norway
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Simpraga S, Mansvelder HD, Groeneveld GJ, Prins S, Hart EP, Poil SS, Linkenkaer-Hansen K. An EEG nicotinic acetylcholine index to assess the efficacy of pro-cognitive compounds. Clin Neurophysiol 2018; 129:2325-2332. [DOI: 10.1016/j.clinph.2018.08.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 06/27/2018] [Accepted: 08/23/2018] [Indexed: 11/26/2022]
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Llorach-Pares L, Nonell-Canals A, Sanchez-Martinez M, Avila C. Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents. Mar Drugs 2017; 15:E366. [PMID: 29186912 PMCID: PMC5742826 DOI: 10.3390/md15120366] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 11/10/2017] [Accepted: 11/14/2017] [Indexed: 01/12/2023] Open
Abstract
Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium, against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.
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Affiliation(s)
- Laura Llorach-Pares
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), Universitat de Barcelona, 08028 Barcelona, Catalonia, Spain.
- Mind the Byte S.L., 08028 Barcelona, Catalonia, Spain.
| | | | | | - Conxita Avila
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), Universitat de Barcelona, 08028 Barcelona, Catalonia, Spain.
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