Multiscale Statics and Dynamics of Cerebral Functional Connectivity Open Access

Billings, Jacob Charles Wright (Fall 2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/ff3656181?locale=en
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Abstract

The advent of whole-brain functional imaging through Blood-Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) invites novel analytical frameworks to understand the brain's intrinsic functional organization. As brains are complex self-assembled systems, a mechanistic view of brain activity is expected to observe emergent structures interacting across multiple spectral, spatial, and temporal scales. Analytical frameworks that incorporate information at multiple scales may therefore provide additional insights into brain physiology. Chapter 1 introduces this line of reasoning in greater detail. Because BOLD fMRI is an indirect measure of neuronal activity, Chapter 2 pursues an optimal preprocessing strategy for increasing the information content of the BOLD signal. A stratigy that normalizes voxel-wise BOLD signals via z-scoring and removes motion noise via motion parameter regression was found to effectively isolated BOLD signal energetics to the brain's gray matter. Enhancing the signal-to-noise ratio of gray matter BOLD signals is expected to most effectively enhance the proportion of spontaneous hemodynamic (BOLD) fluctuations attributable to neuronal signaling. This is because synaptic activity accounts for the majority of energy usage in the brain, and the dendritic arbor of the central nervous system is unmylenated gray matter. In Chapter 3, preprocessed, voxel-level BOLD signals are filtered into multiple spectral domains in order to identify the spectral components that best reveal the brain's intrinsic organization. Graphs of the brain's functional connectivity--its spatial network architecture--most closely resemble known brain networks in several pass-bands within the low-frequency fluctuation range (~0.1 to ~0.01 Hz). To discover just why low-frequency spectra of the BOLD signal are most effective at revealing the brain's architecture, Chapter 4 links hemodynamic connectivity to neuroelectric connectivity through multimodal studies in the rodent brain. Long-term (static) BOLD connectivity is demonstrated to correspond to static local field potential (LFP) connectivity when neuroelectric activity is filtered into either delta (1-4 Hz), alpha (8-12 Hz), or gamma (40-60 Hz) pass-bands. These findings support the theoretical interpretation of neurovascular coupling as a diffusion-mediated process involving small signaling molecules that communicate information about changing neuronal metabolic load to the cardiovascular system. Essentially, low-frequency fluctuations in the BOLD signal are low-pass filtered versions of neuroelectric activity. Whereas Chapters 2 through 4 pursue long-term trends in coordinated brain activity, Chapter 5 pursues the question of how to identify the kinds of time-varying BOLD dynamics expected to relate to ongoing mental activity. To this end, the instintaneous state space of multi-scale BOLD dynamics is embedded onto a two-dimensional sheet, thereby providing a visually tractable map of the brain dynamics. Discrete epochs of experimentally defined tasks are shown to agglomerate into densely populated peaks in the map space. The brain activitions associated with each map region are further investigated in order to better understand how the brain produces a range of experimentally defined states. Taken as a whole, the enclosed dissertation research demonstrates the pervasiveness of the brain's multi-scalar architecture, and the utility that this perspective affords towards the interpretation of various and complex brain functions.

Table of Contents

1. Introduction ................................................................................................................. 1

2. Raw Brains: Preprocessing Strategies for Functional Connectivity ..................... 9

2.1. Methods .................................................................................................................. 13

2.2. Results ..................................................................................................................... 16

2.3. Discussion .............................................................................................................. 26

3. Connected Brains: Multiscaler and Multispatial Functional Connectivity ........ 30

3.1. Methods .................................................................................................................. 33

3.1.1. Data acquisition............................................................................................... 33

3.1.2. Preprocessing .................................................................................................. 33

3.1.3. WPT Theory .................................................................................................... 34

3.1.4. Wavelet packet transform (WPT) ................................................................. 36

3.1.5. Wavelet packet entropy .................................................................................. 37

3.1.6. Multi-subject data ........................................................................................... 38

3.1.7. Hierarchical clustering (HC) .......................................................................... 39

3.1.8. FC networks constructed via dendrogram pruning ................................... 41

3.1.9. Comparing WPT-HC networks .................................................................... 44

3.2. Results ..................................................................................................................... 45

3.2.1. Wavelet packet entropy .................................................................................. 45

3.2.2. Variation in information (VarInf) across spectra ....................................... 46

3.2.3. Functional connectivity maps across spectra .............................................. 49

3.2.4. Functional connectivity maps across scan type .......................................... 56

3.2.5. Packets in low frequency oscillation range .................................................. 60

3.2.6. High frequency packets .................................................................................. 62

3.2.7. The broadband network ................................................................................ 62

3.2.8. Network stability after additional noise regression .................................... 63

3.3. Discussion .............................................................................................................. 64

3.4. Limitations and Future Directions ..................................................................... 69

4. Mammalian Brains: Comparative Functional Connectivity ................................ 71

4.1. Evolutionary Foundations of Comparative Neuroscience ............................. 75

4.2. Methods .................................................................................................................. 77

4.2.1. Animal selection .............................................................................................. 77

4.2.2. MRI acquisition ............................................................................................... 78

4.2.3. Regions of Interest ......................................................................................... 79

4.2.4. LFP Acquisition .............................................................................................. 79

4.2.5. Data Analysis ................................................................................................... 80

4.3. Results ..................................................................................................................... 82

4.4. Discussion .............................................................................................................. 84

4.5. Limitations and Future Directions ..................................................................... 86

5. Dynamic Brains: Visualizations through multiscale embedding ........................ 87

5.1. Methods .................................................................................................................. 91

5.1.1. Data Acquisition ............................................................................................. 91

5.1.2. Data Preprocessing ......................................................................................... 91

5.1.3. Spectral and Spatial Filtering ......................................................................... 92

5.1.4. Manifold Embedding ..................................................................................... 93

5.1.5. Sub-Space Identification and Characterization .......................................... 96

5.1.6. Velocity Field ................................................................................................... 97

5.1.7. Comparing Embeddings ................................................................................ 97

5.1.8. Real-Time Dynamics ...................................................................................... 98

5.1.9. Permutation Testing for Labeling the Embedded map. ........................... 99

5.2. Results .................................................................................................................. 100

5.3. Discussion ........................................................................................................... 118

6. Conclusions and Final Remarks ........................................................................... 123

7. References ............................................................................................................... 126

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