Medical Image and Signal Analysis in Brain Research

1st Edition, Volume 290 - September 25, 2024
Editors: Chi-Wen Jao, Yu-Te Wu
Language: English
Hardback ISBN:
9 7 8 - 0 - 4 4 3 - 2 3 8 4 4 - 4
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 3 8 4 5 - 1

Progress in Brain Research, Volume 290 highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of author… Read more

Medical Image and Signal Analysis in Brain Research

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Progress in Brain Research, Volume 290 highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Chapters in this release include Image Analysis for Spinocerebellar Ataxia Type 3, Brain structural network modular and connectivity alterations in subtype of patients with migraine, Brain Functional Networks and Structures that Categorize Type 2 Bipolar Disorder and Major Depression, Reinforcement Learning Classification in fNIRS Signal of Olfactory Stimulated, The method of brain-computer interface using the forehead instead of the visual area to receive SSVEP signals and signal processing, and more.

Additional chapters cover The relationship between flash-induced SSVEP in DELTA frequency band and sleep, Enhancing Facial Feature De-Identification in Multi-Frame Brain Images: A Generative Adversarial Network Approach, Comparative Analysis of MRI Markers in Heat and Mechanical Pain Sensitivity, and Analysis of the Difference Between Alzheimer’s Disease, Mild Cognitive Impairment and Normal People by Using Fractal Dimensions and Small-World Network.

Medical Image and Signal Analysis in Brain Research
Cover image
Title page
Table of Contents
Series Page
Copyright
Contributors
Preface
Chapter One Morphological changes of cerebral gray matter in spinocerebellar ataxia type 3 using fractal dimension analysis
Abstract
Keywords
1 Introduction
2 Materials and methods
2.1 Participants
2.2 Data acquisition and processing
2.3 The procedures of FD measure
2.4 Statistical analysis
3 Results
3.1 SCA3 showed significant FD values decreased in bilateral parietal and occipital lobes
3.2 The FD values of bilateral postcentral gyrus can serve as effective imaging biomarkers for the diagnosis of SCA3
4 Discussion and conclusion
Conflicts of interest
References
Chapter Two Brain structural network modular and connectivity alterations in subtypes of patients with migraine and medication overuse headache
Abstract
Keywords
1 Introduction
2 Materials and methods
2.1 Normal participants and patients with EM/CM/MOH
2.2 Image acquisition and gray matter cortical thickness measurement
2.3 Brain structural network analysis
2.4 Statistical analysis
3 Results
3.1 Cortical thickness thinning in migraine patients demonstrates left lateralization
3.2 Correlation map change of subtypes of migraine
3.3 HC and EM groups revealed similar number and pattern in their brain network
4 Discussion
5 Conclusion
Conflict of interest
References
Chapter Three Brain functional networks and structures that categorize type 2 bipolar disorder and major depression
Abstract
Keywords
1 Introduction
2 Methods
2.1 Participants
2.2 Acquisition of resting-state functional and structural magnetic resonance imaging images
2.3 Preprocessing for structural and resting-state functional MRI data
2.4 Structural features extraction and FC extracted by FC-based atlas
2.5 Comparison of the patients with BD II and MDD and HCs
2.6 The classification of the patients with BD II and MDD
3 Results
3.1 Demographic characteristics of BD II, MDD, and HC groups
3.2 Comparison among BD II, MDD, and HC groups
3.3 Feature selection for BD II and MDD classification
3.4 Classification performance of BD vs. MDD using functional and structural neuroimaging data
4 Discussion
5 Conclusion
Acknowledgments
References
Chapter Four Enhancing classification accuracy of HRF signals in fNIRS using semi-supervised learning and filtering
Abstract
Keywords
1 Introduction
2 Methods
2.1 k-nearest neighbors (kNN) algorithm
2.2 Label propagation
2.3 Support vector machines with Gaussian
2.4 Support vector machines with linear
3 Methodology and materials
3.1 Subjects
3.2 Experimental setup and data acquisition
3.3 Pre-processing and dataflow
3.4 Feature extraction
3.5 Design HRF filter
3.6 Software and tools
4 Experimental results and analysis classifying data
4.1 Filtered feature HRF data
4.2 Comparing accuracy ratio and standard error
4.3 Filter performance
4.4 ROC curve and AUC
5 Discussion
6 Conclusion
References
Chapter Five Optimizing user experience in SSVEP-BCI systems
Abstract
Keywords
1 Introduction
2 Materials and methods
3 Results
4 Discussion
5 Conclusion
References
Chapter Six Relationship of SSVEP response between flash frequency conditions
Abstract
Keywords
1 Introduction
2 Materials and methods
3 Results
4 Discussion
5 Conclusion
References
Chapter Seven Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach
Abstract
Keywords
1 Introduction
2 Literature review
3 Method and materials
3.1 Facial orientation adjustment
3.2 Facial feature detection
3.3 Generating facial features
3.4 Evaluation of structural similarity
4 Results
5 Discussion
6 Conclusions
References
Chapter Eight Comparative analysis of rs-fMRI markers in heat and mechanical pain sensitivity
Abstract
Keywords
1 Introduction
2 Material and methods
2.1 Participants
2.2 Quantitative sensory testing procedure
2.3 Magnetic resonance imaging data collection
2.4 Imaging data preprocessing and analysis
2.5 Pain sensitivity modeling
3 Results
3.1 Demographics and pain sensitivity score
3.2 Functional components and network classification
3.3 Functional connectivity correlation with heat and mechanical pain score
3.4 Linear and nonlinear models for pain sensitivity prediction
4 Discussion
4.1 Differential pain sensitivity in SMN and SN: Heat vs. mechanical pain
4.2 Other contributing regions in pain sensitivity
4.3 Brainstem features and predictive models enhancement
4.4 Composite critical resting-state functional connectivity value for univariable prediction
5 Conclusion
References
Chapter Nine Analysis of the difference between Alzheimer's disease, mild cognitive impairment and normal people by using fractal dimensions and small-world network
Abstract
Keywords
1 Introduction
2 Methods
2.1 Data sets
2.2 MR image acquisition
2.3 Image preprocessing
2.4 Fractal dimensions: Box-counting method
2.5 Structural correlation matrix
2.6 Small-world network
2.7 Statistical analysis
3 Results
3.1 Fractal dimensions
3.2 Small-world network
4 Discussion
5 Conclusion
6 Acknowledgments including declarations
References

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Medical Image and Signal Analysis in Brain Research

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