Evaluation and implementation of neural brain activity detection methods for fMRI

The brain is the most fascinating and least understood organ in the human body. For scientists and philosophers have tried to find the relationship between behaviour, emotion, memory, thought, consciousness and the physical body. In recent years,techniques for non-invasive monitoring of the working brain have experienced a strong development. Neuroimaging tools and methods have been designed to study brain functionality to enhance our understanding of the brain. Functional Magnetic Resonance Imaging (fMRI) is one neuroimaging technique with the capacity to map neural activity with high spatial resolution. The technique is based on MRI, a painless, non-invasive image acquisition method without harmful radiation. In fMRI radio waves and a strong magnetic field are used to measure the correlation be-tween physical changes in the brain and the mental functioning during the performance of cognitive tasks. The physical changes are small intensity changes in MR images due to local blood oxygenation changes.


1 Introduction
2 Motivation
2.1 Interaction between engineering and medicine
2.2 Medical signal processing and neurology
3 Background
3.1 Magnetic Resonance Imaging (MRI)
3.1.1 Introduction
3.1.2 Basic principles
3.1.3 Imaging parameters
3.1.4 Field of application
3.2 Functional Magnetic Resonance Imaging (fMRI)
3.2.1 Introduction
3.2.2 The Blood Oxygen Level Dependent (BOLD) response
3.2.3 Brain activity detection
3.3 Statistical Parametric Mapping (SPM)
3.3.1 Introduction
3.3.2 SPM approach
3.3.3 SPM image format
4 Theory
4.1 GLM method
4.2 Canonical Correlation Analysis (CCA)
4.2.1 The canonical correlation
4.2.2 Constrained CCA
4.3 Principal Component Analysis (PCA)
4.4 Impulse response
4.5 Spatial filter functions
4.5.1 Gaussian filters
4.5.2 Steerable filters
4.6 Statistics
4.6.1 ROC (synthetic datasets)
4.6.2 Modified ROC (real human datasets)
75 Methods
5.1 Evaluation of neural activity detection methods
5.1.1 Brain data acquisition
5.1.2 Brain data handling
5.1.3 Implementation of the CCA method
5.1.4 Generation of a hemodynamic response model
5.1.5 Spatial modelling
5.1.6 Generation of a correlation map
5.1.7 Noise filtering
5.1.8 Statistics
5.1.9 Matlab GUI
5.2 SPM implementation
5.2.1 Structure of SPM software
5.2.2 Testing SPM2 code
5.2.3 Integration of CCA methods
6 Results
6.1 Some important values
6.2 Evaluation on synthetic data
6.2.1 Related work
6.3 Evaluation on real human data
6.3.1 Steerable spatial filters
6.3.2 Constrained CCA
6.4 Implementation in SPM
6.4.1 New SPM code for CCA
6.4.2 New SPM architecture
6.4.3 New functionalities
6.4.4 Evaluation in SPM: GLM versus constrained CCA
7 Discussion
8 Conclusion

Author: Breitenmoser, Sabina

Source: Linköping University

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