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Alarjani M, Almarri B. Multivariate pattern analysis of medical imaging-based Alzheimer's disease. Front Med (Lausanne) 2024; 11:1412592. [PMID: 39099597 PMCID: PMC11294205 DOI: 10.3389/fmed.2024.1412592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 06/06/2024] [Indexed: 08/06/2024] Open
Abstract
Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is marked by a relentless decline in memory and cognitive abilities. As the disease progresses, it leads to a significant loss of mental function. Early detection of AD is essential to starting treatments that can mitigate the progression of this disease and enhance patients' quality of life. This study aims to observe AD's brain functional connectivity pattern to extract essential patterns through multivariate pattern analysis (MVPA) and analyze activity patterns across multiple brain voxels. The optimized feature extraction techniques are used to obtain the important features for performing the training on the models using several hybrid machine learning classifiers for performing binary classification and multi-class classification. The proposed approach using hybrid machine learning classification has been applied to two public datasets named the Open Access Series of Imaging Studies (OASIS) and the AD Neuroimaging Initiative (ADNI). The results are evaluated using performance metrics, and comparisons have been made to differentiate between different stages of AD using visualization tools.
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Affiliation(s)
| | - Badar Almarri
- Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Hofuf, Saudi Arabia
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2
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Warren SL, Khan DM, Moustafa AA. Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example. Brain Behav 2024; 14:e3554. [PMID: 38841732 PMCID: PMC11154821 DOI: 10.1002/brb3.3554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. OBJECTIVES AND METHODS In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. RESULTS In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. CONCLUSIONS We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.
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Affiliation(s)
- Samuel L. Warren
- Faculty of Society and Design, School of PsychologyBond UniversityGold CoastQueenslandAustralia
| | - Danish M. Khan
- Department of Electronic EngineeringNED University of Engineering & TechnologyKarachiSindhPakistan
| | - Ahmed A. Moustafa
- Faculty of Society and Design, School of PsychologyBond UniversityGold CoastQueenslandAustralia
- The Faculty of Health Sciences, Department of Human Anatomy and PhysiologyUniversity of JohannesburgAuckland ParkSouth Africa
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Zuo Q, Li R, Shi B, Hong J, Zhu Y, Chen X, Wu Y, Guo J. U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis. Front Comput Neurosci 2024; 18:1387004. [PMID: 38694950 PMCID: PMC11061376 DOI: 10.3389/fncom.2024.1387004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data. Methods In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics. Results We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement. Conclusion Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.
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Affiliation(s)
- Qiankun Zuo
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
- Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, Hubei, China
| | - Ruiheng Li
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
| | - Binghua Shi
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
| | - Jin Hong
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yanfei Zhu
- School of Foreign Languages, Sun Yat-sen University, Guangzhou, China
| | - Xuhang Chen
- Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China
| | - Yixian Wu
- School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Jia Guo
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
- Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, Hubei, China
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4
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Zhang Q, Wang H, Shi Y, Li W. White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model. Quant Imaging Med Surg 2024; 14:3086-3106. [PMID: 38617147 PMCID: PMC11007501 DOI: 10.21037/qims-23-1478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/07/2024] [Indexed: 04/16/2024]
Abstract
Background Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.
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Affiliation(s)
- Qi Zhang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Haoran Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Yonghong Shi
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Wensheng Li
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
- Department of Human Anatomy and Histoembryology, School of Basic Medical Science, Fudan University, Shanghai, China
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Khazaee A, Mohammadi A. Editorial for "Altered Dynamics of Brain Spontaneous Activity and Functional Networks Associated with Cognitive Impairment in Patients with Type 2 Diabetes". J Magn Reson Imaging 2024. [PMID: 38411284 DOI: 10.1002/jmri.29318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/28/2024] Open
Affiliation(s)
- Ali Khazaee
- Department of Electrical Engineering, University of Bojnord, Bojnord, Iran
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Huang Y, Zhang J, He K, Mo X, Yu R, Min J, Zhu T, Ma Y, He X, Lv F, Lei D, Liu M. Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models. Diagnostics (Basel) 2024; 14:389. [PMID: 38396428 PMCID: PMC10888009 DOI: 10.3390/diagnostics14040389] [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: 01/10/2024] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.
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Affiliation(s)
- Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jingbo Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Kewei He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xue Mo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jing Min
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Tong Zhu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Yunfeng Ma
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xiangqian He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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Han S, Sun Z, Zhao K, Duan F, Caiafa CF, Zhang Y, Solé-Casals J. Early prediction of dementia using fMRI data with a graph convolutional network approach. J Neural Eng 2024; 21:016013. [PMID: 38215493 DOI: 10.1088/1741-2552/ad1e22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/12/2024] [Indexed: 01/14/2024]
Abstract
Objective. Alzheimer's disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at:https://github.com/Shuning-Han/FC-based-GCN.
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Affiliation(s)
- Shuning Han
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic 08500, Catalonia, Spain
- Image Processing Research Group, RIKEN Center for Advanced Photonics, RIKEN, Wako-Shi, Saitama, Japan
| | - Zhe Sun
- Faculty of Health Data Science, Juntendo University, Urayasu, Chiba, Japan
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, United States of America
| | - Feng Duan
- Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin, People's Republic of China
| | - Cesar F Caiafa
- Instituto Argentino de Radioastronomía-CCT La Plata, CONICET / CIC-PBA / UNLP, V. Elisa 1894, Argentina
- Tensor Learning Team, Riken AIP, Tokyo, Tokyo 103-0027, Japan
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, United States of America
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, United States of America
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic 08500, Catalonia, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, United Kingdom
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Guimarães P, Serranho P, Duarte JV, Crisóstomo J, Moreno C, Gomes L, Bernardes R, Castelo-Branco M. The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach. Front Neuroinform 2024; 17:1321178. [PMID: 38250018 PMCID: PMC10796780 DOI: 10.3389/fninf.2023.1321178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction There is a need to better understand the neurophysiological changes associated with early brain dysfunction in Type 2 diabetes mellitus (T2DM) before vascular or structural lesions. Our aim was to use a novel unbiased data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients, focusing on their potential as biomarkers. Methods We meshed task-based event-related (visual speed discrimination) functional magnetic resonance imaging with DL to show, from an unbiased perspective, that T2DM patients' blood-oxygen-level dependent response is altered. Relevance analysis determined which brain regions were more important for discrimination. We combined explainability with deconvolution generalized linear model to provide a more accurate picture of the nature of the neural changes. Results The proposed approach to discriminate T2DM patients achieved up to 95% accuracy. Higher performance was achieved at higher stimulus (speed) contrast, showing a direct relationship with stimulus properties, and in the hemispherically dominant left visual hemifield, demonstrating biological interpretability. Differences are explained by physiological asymmetries in cortical spatial processing (right hemisphere dominance) and larger neural signal-to-noise ratios related to stimulus contrast. Relevance analysis revealed the most important regions for discrimination, such as extrastriate visual cortex, parietal cortex, and insula. These are disease/task related, providing additional evidence for pathophysiological significance. Our data-driven design allowed us to compute the unbiased HRF without assumptions. Conclusion We can accurately differentiate T2DM patients using a data-driven classification of the HRF. HRF differences hold promise as biomarkers and could contribute to a deeper understanding of neurophysiological changes associated with T2DM.
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Affiliation(s)
- Pedro Guimarães
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
| | - Pedro Serranho
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- Department of Sciences and Technology, Universidade Aberta, Lisbon, Portugal
| | - João V. Duarte
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- University of Coimbra, Faculty of Medicine (FMUC), Coimbra, Portugal
| | - Joana Crisóstomo
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
| | - Carolina Moreno
- Department of Endocrinology, University Hospital of Coimbra (CHUC), Coimbra, Portugal
| | - Leonor Gomes
- Department of Endocrinology, University Hospital of Coimbra (CHUC), Coimbra, Portugal
| | - Rui Bernardes
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- University of Coimbra, Clinical Academic Center of Coimbra (CACC), Faculty of Medicine (FMUC), Coimbra, Portugal
| | - Miguel Castelo-Branco
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- University of Coimbra, Clinical Academic Center of Coimbra (CACC), Faculty of Medicine (FMUC), Coimbra, Portugal
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Kucikova L, Kalabizadeh H, Motsi KG, Rashid S, O'Brien JT, Taylor JP, Su L. A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations. Ageing Res Rev 2024; 93:102159. [PMID: 38056505 DOI: 10.1016/j.arr.2023.102159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/14/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.
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Affiliation(s)
- Ludmila Kucikova
- Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Hoda Kalabizadeh
- Oxford Machine Learning in NeuroImaging Lab, OMNI, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | | | - Sidrah Rashid
- Academic Unit of Medical Education, University of Sheffield, Sheffield, United Kingdom
| | - John T O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
| | - Li Su
- Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom; Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
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10
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Westendorp J, Geerse OP, van der Lee ML, Schoones JW, van Vliet MHM, Wit T, Evers AWM, van Vliet LM. Harmful communication behaviors in cancer care: A systematic review of patients and family caregivers perspectives. Psychooncology 2023; 32:1827-1838. [PMID: 37957777 DOI: 10.1002/pon.6247] [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: 08/04/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVE Issues regarding clinician communication remain an important source of complaints within healthcare. This systematic review aims to determine cancer patients' and their family caregivers' views on which clinicians' communication behaviors can harm (i.e. eliciting negative feelings/consequences for patients/family caregivers). METHODS We searched for all types of peer-reviewed studies that determined adult (≥18 years) cancer patients' and/or family caregivers' perspectives on which clinicians' communication behaviors can harm in several databases (PubMed, Embase, Web of Science, Cochrane Library, Emcare, PsycINFO and Academic Search Premier), supplemented by expert-consultation. Studies were screened using the Artificial intelligence screening tool of ASReview and data was analyzed using Thematic Analysis. To assess the quality of the studies the Qualsyst critical appraisal tool was used. RESULTS A total of 47 studies were included. Four main themes of harmful communication behaviors were identified: (1) Lack of tailored information provision (e.g. giving too little or too much/specific information) (2) Lack of tailored decision making (ranging from; patient exclusion, to the patients' responsibility, and/or haste) (3) Lack of feeling seen and heard (seen as a disease, not as a human being; not listened to concerns and emotions) (4) Lack of feeling held and remembered (forgotten agreements; lack of care continuity). CONCLUSIONS Our results reveal an overview of patients' and family caregivers' perspectives on which clinicians' communication behaviors can harm. Harm could be prevented when information and decision involvement are tailored and patients' and family caregivers' needs to feel seen, heard, held and remembered are met.
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Affiliation(s)
- Janine Westendorp
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Olaf P Geerse
- Department of Medical Oncology, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Pulmonary Diseases, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marije L van der Lee
- Scientific Research Department, Helen Dowling Institute, Centre for Psycho-Oncology, Bilthoven, The Netherlands
- Department of Medical and Clinical Psychology, Tilburg University, Tilburg, The Netherlands
| | - Jan W Schoones
- Directorate of Research Policy, Leiden University Medical Center, Leiden, The Netherlands
| | - Milon H M van Vliet
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Tamara Wit
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Andrea W M Evers
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
- Medical Delta, Leiden University, TU Delft and Erasmus University, Leiden, The Netherlands
| | - Liesbeth M van Vliet
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
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11
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Otin S, Ávila FJ, Mallen V, Garcia-Martin E. Detecting Structural Changes in the Choroidal Layer of the Eye in Neurodegenerative Disease Patients through Optical Coherence Tomography Image Processing. Biomedicines 2023; 11:2986. [PMID: 38001986 PMCID: PMC10669633 DOI: 10.3390/biomedicines11112986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE To evaluate alterations of the choroid in patients with a neurodegenerative disease versus healthy controls, a custom algorithm based on superpixel segmentation was used. DESIGN A cross-sectional study was conducted on data obtained in a previous cohort study. SUBJECTS Swept-source optical coherence tomography (OCT) B-scan images obtained using a Triton (Topcon, Japan) device were compiled according to current OSCAR IB and APOSTEL OCT image quality criteria. Images were included from three cohorts: multiple sclerosis (MS) patients, Parkinson disease (PD) patients, and healthy subjects. Only patients with early-stage MS and PD were included. METHODS In total, 104 OCT B-scan images were processed using a custom superpixel segmentation (SpS) algorithm to detect boundary limits in the choroidal layer and the optical properties of the image. The algorithm groups pixels with similar structural properties to generate clusters with similar meaningful properties. MAIN OUTCOMES SpS selects and groups the superpixels in a segmented choroidal area, computing the choroidal optical image density (COID), measured as the standard mean gray level, and the total choroidal area (CA), measured as px2. RESULTS The CA and choroidal density (CD) were significantly reduced in the two neurodegenerative disease groups (higher in PD than in MS) versus the healthy subjects (p < 0.001); choroidal area was also significantly reduced in the MS group versus the healthy subjects. The COID increased significantly in the PD patients versus the MS patients and in the MS patients versus the healthy controls (p < 0.001). CONCLUSIONS The SpS algorithm detected choroidal tissue boundary limits and differences optical density in MS and PD patients versus healthy controls. The application of the SpS algorithm to OCT images potentially acts as a non-invasive biomarker for the early diagnosis of MS and PD.
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Affiliation(s)
- Sofia Otin
- Department of Applied Physics, University of Zaragoza, 50009 Zaragoza, Spain;
| | - Francisco J. Ávila
- Department of Applied Physics, University of Zaragoza, 50009 Zaragoza, Spain;
| | - Victor Mallen
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (V.M.); (E.G.-M.)
- Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain
| | - Elena Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (V.M.); (E.G.-M.)
- Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain
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12
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Mieling M, Meier H, Bunzeck N. Structural degeneration of the nucleus basalis of Meynert in mild cognitive impairment and Alzheimer's disease - Evidence from an MRI-based meta-analysis. Neurosci Biobehav Rev 2023; 154:105393. [PMID: 37717861 DOI: 10.1016/j.neubiorev.2023.105393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/17/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023]
Abstract
Recent models of Alzheimer's disease (AD) suggest that neuropathological changes of the medial temporal lobe, especially entorhinal cortex, are preceded by degenerations of the cholinergic Nucleus basalis of Meynert (NbM). Evidence from imaging studies in humans, however, is limited. Therefore, we performed an activation-likelihood estimation meta-analysis on whole brain voxel-based morphometry (VBM) MRI data from 54 experiments and 2581 subjects in total. It revealed, compared to healthy older controls, reduced gray matter in the bilateral NbM in AD, but only limited evidence for such an effect in patients with mild cognitive impairment (MCI), which typically precedes AD. Both patient groups showed less gray matter in the amygdala and hippocampus, with hints towards more pronounced amygdala effects in AD. We discuss our findings in the context of studies that highlight the importance of the cholinergic basal forebrain in learning and memory throughout the lifespan, and conclude that they are partly compatible with pathological staging models suggesting initial and pronounced structural degenerations within the NbM in the progression of AD.
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Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Hannah Meier
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
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13
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Mieling M, Göttlich M, Yousuf M, Bunzeck N. Basal forebrain activity predicts functional degeneration in the entorhinal cortex in Alzheimer's disease. Brain Commun 2023; 5:fcad262. [PMID: 37901036 PMCID: PMC10608112 DOI: 10.1093/braincomms/fcad262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/23/2023] [Accepted: 10/07/2023] [Indexed: 10/31/2023] Open
Abstract
Recent models of Alzheimer's disease suggest the nucleus basalis of Meynert (NbM) as an early origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. We analysed resting-state functional fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (n = 71) at baseline and 2 years later. At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations, differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. Our findings give novel insights into the pathogenesis of Alzheimer's disease by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.
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Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Martin Göttlich
- Department of Neurology, University of Lübeck, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck 23562, Germany
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck 23562, Germany
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14
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Wang YTT, Rosa-Neto P, Gauthier S. Advanced brain imaging for the diagnosis of Alzheimer disease. Curr Opin Neurol 2023; 36:481-490. [PMID: 37639461 DOI: 10.1097/wco.0000000000001198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
PURPOSE OF REVIEW The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD). RECENT FINDINGS Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities. SUMMARY Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.
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15
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Dang T, Fermin ASR, Machizawa MG. oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data. Front Neuroinform 2023; 17:1266713. [PMID: 37829329 PMCID: PMC10566623 DOI: 10.3389/fninf.2023.1266713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/08/2023] [Indexed: 10/14/2023] Open
Abstract
The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.
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Affiliation(s)
- Tung Dang
- Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Alan S. R. Fermin
- Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Maro G. Machizawa
- Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
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16
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Kawaguchi A. Network-based diagnostic probability estimation from resting-state functional magnetic resonance imaging. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17702-17725. [PMID: 38052533 DOI: 10.3934/mbe.2023787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Brain functional connectivity is a useful biomarker for diagnosing brain disorders. Connectivity is measured using resting-state functional magnetic resonance imaging (rs-fMRI). Previous studies have used a sequential application of the graphical model for network estimation and machine learning to construct predictive formulas for determining outcomes (e.g., disease or health) from the estimated network. However, the resulting network had limited utility for diagnosis because it was estimated independent of the outcome. In this study, we proposed a regression method with scores from rs-fMRI based on supervised sparse hierarchical components analysis (SSHCA). SSHCA has a hierarchical structure that consists of a network model (block scores at the individual level) and a scoring model (super scores at the population level). A regression model, such as the multiple logistic regression model with super scores as the predictor, was used to estimate diagnostic probabilities. An advantage of the proposed method was that the outcome-related (supervised) network connections and multiple scores corresponding to the sub-network estimation were helpful for interpreting the results. Our results in the simulation study and application to real data show that it is possible to predict diseases with high accuracy using the constructed model.
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17
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Gao J, Liu J, Xu Y, Peng D, Wang Z. Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease. Front Neurosci 2023; 17:1222751. [PMID: 37457008 PMCID: PMC10347411 DOI: 10.3389/fnins.2023.1222751] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI). Methods In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD. Results The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age. Discussion Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.
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Affiliation(s)
| | | | | | | | - Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
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18
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Guo T, Xiong K, Yuan B, Zhang Z, Wang L, Zhang Y, Liang C, Liu Z. Homogeneous-resolution photoacoustic microscopy for ultrawide field-of-view neurovascular imaging in Alzheimer's disease. PHOTOACOUSTICS 2023; 31:100516. [PMID: 37313359 PMCID: PMC10258506 DOI: 10.1016/j.pacs.2023.100516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 05/25/2023] [Accepted: 05/25/2023] [Indexed: 06/15/2023]
Abstract
Neurovascular imaging is essential for investigating neurodegenerative diseases. However, the existing neurovascular imaging technology suffers from a trade-off between a field of view (FOV) and resolution in the whole brain, resulting in an inhomogeneous resolution and lack of information. Here, homogeneous-resolution arched-scanning photoacoustic microscopy (AS-PAM), which has an ultrawide FOV to cover the entire mouse cerebral cortex, was developed. Imaging of the neurovasculature was performed with a homogenous resolution of 6.9 µm from the superior sagittal sinus to the middle cerebral artery and caudal rhinal vein in an FOV of 12 × 12 mm2. Moreover, using AS-PAM, vascular features of the meninges and cortex were quantified in early Alzheimer's disease (AD) and wild-type (WT) mice. The results demonstrated high sensitivity to the pathological progression of AD on tortuosity and branch index. The high-fidelity imaging capability in large FOV enables AS-PAM to be a promising tool for precise brain neurovascular visualization and quantification.
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Affiliation(s)
- Ting Guo
- School of Medicine South China University of Technology, Guangzhou 510006, China
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Kedi Xiong
- MOE Key Laboratory of Laser Life Science Institute of Laser Life Science, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Bo Yuan
- MOE Key Laboratory of Laser Life Science Institute of Laser Life Science, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Zhenhui Zhang
- MOE Key Laboratory of Laser Life Science Institute of Laser Life Science, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Lijuan Wang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, China
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangzhou 510080, China
| | - Yuhu Zhang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, China
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangzhou 510080, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
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Mieling M, Göttlich M, Yousuf M, Bunzeck N. Basal forebrain activity predicts functional degeneration in the entorhinal cortex and decreases with Alzheimer's Disease progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534523. [PMID: 37034733 PMCID: PMC10081194 DOI: 10.1101/2023.03.28.534523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Recent models of Alzheimer's Disease (AD) suggest the nucleus basalis of Meynert (NbM) as the origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. METHODS We analyzed resting-state (rs)fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=71) at baseline and two years later. RESULTS At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations (fALFF), differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. DISCUSSION Our findings give novel insights into the pathogenesis of AD by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.
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Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Martin Göttlich
- Department of Neurology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
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Bok J, Ha J, Ahn BJ, Jang Y. Disease-Modifying Effects of Non-Invasive Electroceuticals on β-Amyloid Plaques and Tau Tangles for Alzheimer's Disease. Int J Mol Sci 2022; 24:ijms24010679. [PMID: 36614120 PMCID: PMC9821138 DOI: 10.3390/ijms24010679] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/22/2022] [Accepted: 12/27/2022] [Indexed: 01/04/2023] Open
Abstract
Electroceuticals refer to various forms of electronic neurostimulators used for therapy. Interdisciplinary advances in medical engineering and science have led to the development of the electroceutical approach, which involves therapeutic agents that specifically target neural circuits, to realize precision therapy for Alzheimer's disease (AD). To date, extensive studies have attempted to elucidate the disease-modifying effects of electroceuticals on areas in the brain of a patient with AD by the use of various physical stimuli, including electric, magnetic, and electromagnetic waves as well as ultrasound. Herein, we review non-invasive stimulatory systems and their effects on β-amyloid plaques and tau tangles, which are pathological molecular markers of AD. Therefore, this review will aid in better understanding the recent technological developments, applicable methods, and therapeutic effects of electronic stimulatory systems, including transcranial direct current stimulation, 40-Hz gamma oscillations, transcranial magnetic stimulation, electromagnetic field stimulation, infrared light stimulation and ionizing radiation therapy, and focused ultrasound for AD.
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Affiliation(s)
- Junsoo Bok
- Department of Medical and Digital Engineering, College of Engineering, Hanyang University, Seoul 04736, Republic of Korea
| | - Juchan Ha
- Department of Biomedical Engineering, College of Engineering, Hanyang University, Seoul 04736, Republic of Korea
| | - Bum Ju Ahn
- Department of Pharmacology, College of Medicine, Hanyang University, Seoul 04736, Republic of Korea
| | - Yongwoo Jang
- Department of Medical and Digital Engineering, College of Engineering, Hanyang University, Seoul 04736, Republic of Korea
- Department of Pharmacology, College of Medicine, Hanyang University, Seoul 04736, Republic of Korea
- Correspondence: ; Tel.: +82-2-2220-0655
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