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Studying Resting State and Stimulus Induced BOLD Activity Using the Generalized Ising Model and Independent Component Graph Analysis

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Studying Resting State and Stimulus Induced BOLD Activity Using the Generalized Ising Model and Independent Component Graph Analysis Western University Scholarship@Western Electronic Thesis and Dissertation Repository 1-29-2020 11:00 AM Studying Resting State and Stimulus Induced BOLD Activity using the Generalized Ising Model and Independent Component Graph Analysis Sivayini Kandeepan The University of Western Ontario Supervisor Soddu, Andrea The University of Western Ontario Co-Supervisor Valluri, Sreeram The University of Western Ontario Graduate Program in Physics A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Sivayini Kandeepan 2020 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Statistical, Nonlinear, and Soft Matter Physics Commons Recommended Citation Kandeepan, Sivayini, "Studying Resting State and Stimulus Induced BOLD Activity using the Generalized Ising Model and Independent Component Graph Analysis" (2020). Electronic Thesis and Dissertation Repository. 6801. https://ir.lib.uwo.ca/etd/6801 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. Abstract Although many technical advancements have been made, neuroscientists still struggle to explain the underlying behaviour of how brain regions communicate with each other to form large-scale functional networks. functional Magnetic Resonance Imaging (fMRI) has been commonly used to investigate changes between brain regions over time using the Blood Oxygen Level Dependent (BOLD) signal. The goal of this thesis is to show the applicability of novel techniques and tools, such as the generalized Ising model (GIM) and the independent component graph analysis (GraphICA), to obtain information on the functional connectivity of populations with altered perception of consciousness. The GIM was used to model brain activity in healthy brains during various stages of consciousness, as induced by an anesthetic agent, propofol, in the auditory paradigm. GraphICA, a tool that combines ICA and graph theory was used to investigate the functional connectivity of resting state networks (RSNs) in patients with altered perception caused by tinnitus and in patients with altered states of consciousness caused by severe brain injury. For the tinnitus patients, we examined whether a correlation exists between tinnitus behavioural scores and functional brain connectivity of RSNs. Moreover, for the severely brain injured patients, a multimodal neuroimaging approach with hybrid FDG-PET/MRI was implemented to study the functional connectivity changes of the RSNs. The GIM simulated with an external field was able to model the brain activity at different levels of consciousness under naturalistic stimulation, at a temperature in the super critical regime. Further, a strong correlation was observed between tinnitus distress and the connectivity pattern within and between the right executive control network and the other RSNs. This suggests that tinnitus distress is the consequence of a hyperactive attention condition. A variability was observed ii in the appearance and temporal/spatial patterns of RSNs among the two resting state fMRI acquisitions acquired within the same scanning session of the severely brain injured patients. This suggests the need for new strategies to be developed in order to pick the best RSN from each acquisition. Overall, this work demonstrated that the GIM and GraphICA were promising tools to investigate brain activity of populations with altered perception of consciousness and in future can be extended to investigate different neurological populations. Key words: Resting state fMRI, Generalized Ising model, Graph theory, Independent component analysis, Tinnitus, Disorders of consciousness, Anesthesia, Naturalistic external stimuli iii Summary for Lay Audience The human brain is one of the largest and most complex organs. It acts as the commanding center of the nervous system. It is made up of more than a hundred billion neurons that work together to successfully generate and transfer information. Despite recent advancements in neuroscience, researchers still struggle to explain how the brain functions in order to effectively complete specific tasks. Our research is to map out the differences in functional connectivity in altered states of consciousness, arising either by the intake of an anesthetic agent or due to pathological conditions. In the first project, we successfully modelled the brain activity of healthy subjects while they were listening to an audio clip from a movie, in awake, mildly sedated, deeply sedated and recovered back, due to anesthesia. Further, synchronization among subjects’ brain regions responsible for perception was found only when the individuals were listening to the movie. In future, this simple model will permit us to see whether a non-responsive patient has the same neural experience of a healthy individual. In the second project, a study focussed on tinnitus (ringing in ears without any sound) patients, we found that distress resulted due to hyper-activity of brain regions in charge of executive functioning. This concludes that tinnitus distress is mainly caused by hyper attention towards internally generated auditory sensations, “auditory hallucinations”. In our final project on patients with disorders of consciousness, we observed a variability between MRI scans acquired within a temporal gap of 30 min. This suggests a necessity to perform multiple scans in the same session to reduce misdiagnosis. iv Co-Authorship Statement This thesis is comprised of three manuscripts which were co-authored with various collaborators. The second (Chapter 3) and third (Chapter 4) papers have been published in “Journal of Neurology” and “Frontiers in Neuroscience”, respectively, and the first (Chapter 2) manuscript will be submitted soon. I am the principal author of projects in Chapters 2 and 3 and co-first author of the project in Chapter 4. For the project in Chapter 2 (generalized Ising model), I modified the generalized Ising model code developed by Dr. Thushar Das, previous member of the group, to incorporate external stimuli. I performed all the simulations using the modified generalized Ising model code, calculated the inter-subject correlations, plotted the brain maps, and wrote the chapter completely. The fMRI data for the project in Chapter 2 were acquired by Dr. Lorina Naci from Robarts Research Institute at the University of Western Ontario. Preprocessing of the fMRI data and extraction of the features of the audio clip were performed by Jorge Rudas, who will be a co- author. Projects in Chapters 3 (Kandeepan et al., 2019) and 4 (Cavaliere and Kandeepan et al., 2018), involved the “GraphICA” tool, which was developed by Demetrius Rebeiro de Paula, from our lab. This tool preprocesses the fMRI data and extracts the resting state networks. For the project in Chapter 3 (tinnitus), I calculated the graph strengths of all the resting state networks and performed correlation analysis. Moreover, I developed codes to perform group statistical analysis such as the stepwise fit analysis, visualized all the results and composed 85% of the manuscript. The fMRI data of 135 tinnitus patients from the University Hospital of Antwerp used in this project were provided by Prof. Dirk de Ridder. Dr. Audrey Maudoux–co-first author of the paper– contributed in writing the introduction and discussion sections. v The project in Chapter 4 (disorders of consciousness) used fMRI and PET data of the patients with severe brain injury from the Neurorehabilitation Unit at Maugeri Clinical and Scientific Institutes, Telese Terme, Italy, and acquired at the SDN of Naples, Italy. I performed correlation analysis between fMRI and PET data obtained from GraphICA, at the network level, while behavioural assessments were performed by members of Dr. Anna Estraneo’s group. I introduced a technique called the ratio of fitness to choose the component which best resembles the network from two different fMRI acquisitions. I developed a code to visualize regions belonging/not belonging to a network and composed about 50% of this chapter, while the remainder was composed by the other co-authors. vi Acknowledgments I would like to express my greatest gratitude to my supervisor Prof. Andrea Soddu for his continuous guidance and tireless explanation throughout the project. I am indebted to him for helping me in defining the problem, providing insights to the solution and ultimately guiding me to make my research a success. I also wish to thank my co-supervisor Prof. Sree Ram Valluri for his patience and support provided throughout the project. I would also like to extend my gratitude to my advisory committee members, Prof. Alex Buchel and Prof. Blaine Chronik, for spending their time to evaluate my work and provide thoughtful discussions. I wish to thank the staff members of the Department of Physics and Astronomy, as well as the Brain and Mind Institute for being so kind to me and always available whenever assistance was needed. My special thanks go to my group members Dr. Emily Nichols, Pubuditha Abeyasinghe, Nasim Mortazavi, Sina Khajehabdollahi, Demetrius Ribeiro de Paula, and Marwa Dawaga for their valuable suggestions. In particular, Pubu was there beside me to provide endless moral support and positive energy whenever I was feeling down. My heartfelt thanks are also extended to Jorge Rudas and all
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