Understanding brain activity dynamics through the investigation of quasi-periodic patterns Open Access

Abbas, Muhammad Anzar (Spring 2019)

Permanent URL: https://etd.library.emory.edu/concern/etds/8p58pf126?locale=en


This dissertation explores large-scale brain activity through the investigation of repeating spatiotemporal patterns in the brain. A pattern-finding algorithm applied to brain activity data collected through functional magnetic resonance imaging reveals a reliably recurring quasi-periodic pattern (QPP). The QPP involves propagation of activity in the default mode and task positive networks of the brain. The two networks have been shown to be relevant for task performance and development of neuropsychiatric disorders. Searching for QPPs in resting-state and task-performing individuals reveals that task-performance influences the spatiotemporal pattern of the QPP and the strength and frequency with which it occurs. Differentiating QPPs between healthy individuals and individuals with ADHD, a neuropsychiatric disorder involving disruptions in the default mode and task positive networks, reveals that the spatiotemporal pattern of the QPP is affected in the disrupted regions. Through removal of the QPP from the brain signal, we find that QPPs contribute to functional connectivity within and between the default mode and task positive networks. These findings suggest that QPPs are important for healthy brain function as they contribute to the typical functional architecture of the brain. To understand a neural mechanism behind QPPs, we investigated the role of neuromodulation by deep brain nuclei on the presence of QPPs in brain activity. Pharmacological manipulation of the noradrenergic locus coeruleus in rats led to a disruption in QPP activity compared to healthy controls. This finding advocates for a neural mechanism behind the occurrence of QPPs and subsequently a biological machinery for the maintenance of functional connectivity in the brain. The dissertation provides evidence that studying large-scale brain activity in the form of repeating patterns can assist with understanding healthy brain function and how it is disrupted during disease.

Table of Contents

Chapter 1 – Introduction to Quasi-periodic patterns 1

1.1 – Brain activity is connected across scales 2

1.2 – Development of fMRI to study brain activity 4

1.3 – Resting-state fMRI and functional connectivity 7

1.4 – Dynamics of functional connectivity 9

1.5 – Quasi-periodic patterns in the brain 11

Chapter 2 – Quasi-periodic patterns and functional connectivity in the brain 13

2.1 – Introduction 14

2.2 – Methods 17

2.3 – Results 23

2.4 – Discussion 37

2.5 – Conclusions 48

Chapter 3 – Quasi-periodic patterns in individuals with brain disorders 49

Introduction 51

3.1 – Attention-deficit/hyperactivity disorder 53

3.1.1 – Introduction 53

3.1.2 – Methods 56

3.1.3 – Results 64

3.1.4 – Discussion 77

3.1.5 – Conclusions 89

3.2 – Stroke 90

3.2.1 – Introduction 90

3.2.2 – Methods 92

3.2.3 – Results 95

3.2.4 – Discussion 99

3.2.5 – Conclusions 101

Chapter 4 – Predicting neural drivers of quasi-periodic patterns 102

4.1 – Introduction 103

4.2 – Methods 106

4.3 – Results 109

4.4 – Discussion 115

4.5 – Conclusions 118

Chapter 5 – Conclusions on Quasi-periodic patterns 119

Appendix A: fMRI Preprocessing Pipeline 127

Appendix B: Supplementary Figures 130

Appendix C: Supplementary Tables 142

Further Acknowledgements 170

References 172

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