Multiscale Statics and Dynamics of Cerebral Functional Connectivity Open Access

Billings, Jacob Charles Wright (Fall 2017)

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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

Table Of Contents

List of Figures

Figure 2.1

Global Signal Correlations - Preprocessing Strategies Set 1


Figure 2.2

Global Signal Correlations - Preprocessing Strategies Set 2


Figure 2.3

Global Signal Correlations - Preprocessing Strategies Set 3


Figure 2.4

Global Signal Correlations Segmented by Tissue Type


Figure 2.5

Global Signal Correlations Across Individuals


Figure 2.6

Global Signal Correlation Time Lags


Figure 2.7

Spatial Deviations from Zero Time Lag


Figure 2.8

Global Signal Spectrum


Figure 3.1

The Wavelet Packet Transform


Figure 3.2

Hierarchical Clustering


Figure 3.3

Network Entropy Across Wavelet Packets


Figure 3.4

Variation in Information among Spectrally Delimited Functional Networks


Figure 3.5

Example Functional Connectivity Dendrograms


Figure 3.6

Connectivity Networks - Set 1 - 112 Volunteers

50 - 51

Figure 3.7

Connectivity Networks - Set 2 - 30 and 5 Volunteers

52 - 53

Figure 3.8

Connectivity Networks - Set 3 - Volunteer #027 and #039

54 - 55

Figure 3.9

Connectivity Networks - Set 4 - 0.016 - 0.028 Hz


Figure 3.10

Connectivity Networks - Set 5 - 0.028 - 0.052 Hz


Figure 3.11

Connectivity Networks - Set 6 - 0.052- 0.100 Hz


Figure 3.12

Multispectral Functional Network Variation in Information across Scan Types


Figure 4.1

Rodent Default Mode Network


Figure 4.1

Rodent Gradient Echo - Echo Planar Image


Figure 4.1

Rodent Local Field Potentials


Figure 4.1

Inter-node Distances Across Spectra and Between Modalities


Figure 4.1

Multispectral Cross-Modal Network Correlations


Figure 5.1

Embedded Brain Dynamics during Rest and Task


Figure 5.2

Watershed Segmentation of Resting-State Dynamics


Figure 5.3

Watershed Segmentation of Task-Active Dynamics


Figure 5.4

Fifty Functional Networks from Independen Component Analysis


Figure 5.5

Cumulative Dwell Time Histograms


Figure 5.6

Embedded Brain Dynamics Segmented across Scan Types


Figure 5.7

Statistical Similarities among Scan-Segmented Embeddings


Figure 5.8

Structural Similarity Values between Scan-Segmented Embeddings


Figure 5.9

Visualization and Statistical Similarities among Event-Segmented Embeddings

109 - 110

Figure 5.10

Temporal Evolution of SOCIAL Task Dynamics


Figure 5.11

Close-up View of Temporal Evolution of SOCIAL Task Dynamics


Figure 5.12

A Labeled State Space of Brain Dynamics


Figure 5.13

Statistical Affinity of Experimental Conditions for each State-Space Regions


Figure 5.14

Contrasting Brain States Evoked by Contrasting SOCIAL Stimuli


List of Tables

Table 2.1

Preprocessing Strategy Definitions


Table 3.1

Cophenetic Coefficients from Multiple Hierarchical Clusterings of Filtered BOLD Data


About this Dissertation

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