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Assessment of Functional Connectivity Impairment in Rat Brains

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Assessment of Functional Connectivity Impairment in Rat Brains DEGREE PROJECT IN MEDICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2019 Assessment of functional connectivity impairment in rat brains ANDROULA SAVVA KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH Acknowledgements I would like to express my deep gratitude to my supervisor Rodrigo Moreno for trusting me with this project, for providing me with his valuable guidance and for allowing me to take initiatives through the progress of this project. Special thanks are due to my examiner, Professor Örjan Smedby for the insightful comments and suggestions during the development of this thesis. I would also like to thank Daniel Jörgens, PhD student, for his valuable help on the software development of this pipeline and Malin Siegbahn, MD, PhD student, for providing me with her medical expertise and her time contribution. I wish to thank my family and close friends for their support and encouragement through- out this challenging period. Lastly, to Aria, for her patience and support during my studies. Abstract While the rodent model has long been used in brain research, there exists no standardised processing routine that can be employed for analysis and investigation of disease models. The present thesis attempts to investigate a diseased brain model by implementing a collection of scripts, combined with algorithms from existing neuroimaging software, and adapting them to the rodent brain, in an attempt to examine when and how monaural canal atresia affects the functional connectivity of the brain. We show that it is possible to use software tailored to the human brain to pre-process the rodent model. Following conventional pipelines and resting state functional MRI (rs-fMRI)-specific strategies, the developed processing routine implements the most basic steps suggested in literature. On the single-subject level, skull stripping was done using Mialite software, motion correction and distortion correction were based on FMRIB software library (FSL) algorithms and motion artefacts were removed using ICA-based Automatic Removal Of Motion Artifacts (ICA-AROMA). Following denoising, normalisation to standard space, smoothing and temporal filtering, group level analysis was performed. A univariate, hypothesis-driven method and a multivariate, data-driven method were used for group comparison and statistical inference. While seed-based correlation analysis (SCA) did not return any significant results, independent component analysis (ICA) identified two components that show activation in areas of interest. Sammanfattning Djurmodeller med gnagare (råtta och mus) har länge använts i hjärnforskning. Men ännu finns det ingen standardiserad rutin för analys och utvärdering av bilddata från sådana sjuk- domsmodeller. Detta arbete använder en råttmodell av sjukdomen ensidig hörselgångsatresi, som innebär att yttre hörselgången är igensatt på ena sidan. Detta görs genom att mjukvaru- verktyg som utvecklats för att analysera bilddata från magnetkameraundersökning av den mänskliga hjärnan anpassas för att användas på motsvarande bilddata från råtta för att studera hur ensidig hörselgångsatresi påverkar hjärnans funktionella konnektivitet, dvs hur mönstren i hjärnaktivering samvarierar mellan olika delar av hjärnan (rs-fMRI). Vi visar att det är möjligt att använda mjukvara avsedd för människans hjärna för att förbehandla bilder av råtthjärna. Med hjälp av etablerade arbetsflöden och särskilda procedurer för rs-fMRI kunde den utvecklade proceduren implementera de viktigaste stegen i analysen. För varje individ avgränsades hjärnan med programmet Mialite, rörelsekorrigering och korrigering av rumsdis- torsion gjordes med FSL, och rörelseartefakter avlägsnades med ICA-AROMA. Sedan brus tagits bort, och bilddata standardiserats till en standardanatomi och genomgått filtrering i rum och tid, gjordes analys på två grupper, med och utan artificiell hörselgångsatresi. En univariat, hypotesdriven metod och en multivariat, data-driven metod användes för gruppjämförelse och statistisk analys. Frö-baserad korrelationsanalys (SCA) gav inga signifikanta resultat, men oberoende-komponent-analys (ICA) påvisade två anatomiska områden med aktivering relaterad till skillnader mellan grupperna. Table of contents List of figures xi List of abbreviations xiii 1 Introduction1 2 Materials and methods3 2.1 Dataset description . .3 2.1.1 Data acquisition . .3 2.1.2 Data structure . .3 2.1.3 Data format . .4 2.1.4 BIDS organization . .4 2.2 Data preprocessing pipeline . .5 2.2.1 Quality control . .7 2.2.2 Anatomical preprocessing stream . .7 Brain extraction . .8 Bias field correction . .9 Tissue segmentation . .9 Regions of interest (ROI)....................... 10 2.2.3 Functional preprocessing stream . 11 Motion correction . 11 Distortion correction . 11 Multi-stage registration . 12 Spatial filtering . 14 ICA-based cleanup . 14 Highpass temporal filtering . 14 2.3 Statistical analysis . 15 2.3.1 High level design . 15 2.3.2 Dual regression . 16 2.3.3 SCA.................................. 16 2.3.4 Group ICA.............................. 17 3 Results 19 3.1 SCA activation maps . 19 3.2 Group ICA activation maps . 21 x Table of contents 4 Discussion 25 4.1 Appraisal of findings . 25 4.2 Limitations . 27 4.3 Future approaches . 27 4.4 Conclusions . 28 Appendix A State of the art 35 A.1 Magnetic resonance imaging sequences . 35 A.2 The study of functional connectivity . 36 A.2.1 Haemodynamic response and the mechanism of BOLD signal . 37 A.2.2 Brain Networks . 37 A.2.3 Functional connectivity of the resting brain . 38 A.3 Rodent model and the auditory system . 39 A.4 Developed pipelines for fMRI . 41 A.5 Statistical modelling . 45 A.6 Previous research . 47 List of figures 2.1 Revised structure of the dataset . .5 2.2 Flow chart of the rodent data preprocessing pipeline . .6 2.3 Mialite brain mask estimation . .8 2.4 Extracted brain after Mialite . .8 2.5 Bias field correction . .9 2.6 Tissue segmentation masks . 10 2.7 Regions of interest . 10 2.8 Distortion correction . 12 2.9 Multistage registration . 13 2.10 Design matrix . 16 3.1 Left auditory cortex activation cluster for contrast 1 . 20 3.2 Left auditory cortex mean activation for contrast 3 and 4 . 20 3.3 Left cochlear nucleus activation cluster for contrast 1 . 21 3.4 Statistically significant component for contrast 1 as returned by group ICA (gICA)..................................... 22 3.5 Statistically significant component for contrast 2 as returned by gICA... 23 4.1 Effects of susceptibility artefacts on the SCA method . 26 A.1 The human connectome . 38 A.2 Sections of the rodent brain . 40 A.3 Auditory structures of the rodent brain . 40 A.4 Conventional processing routine for fMRI . 42 A.5 FMRIprep workflow . 44 A.6 Resting state specific strategies . 45 List of abbreviations AC auditory cortex ANTs Advanced Normalization Tools BET Brain Extraction Tool BIDS Brain Imaging Data Structure BOLD blood-oxygen-level dependent CCNN connectome convolutional neural network CN cochlear nucleus CSF cerebrospinal fluid DMN default mode network DOF degrees of freedom DW diffusion weighted EEG electroencephalography EPI echo planar imaging FLIRT FMRIB’s Linear Image Registration Tool fMRI functional MRI FNIRT FMRIB’s Non-Linear Image Registration Tool FOV field of view FSL FMRIB software library FWHM full-width half-max gICA group ICA GLM general linear model IC inferior colliculus xiv List of abbreviations ICA independent component analysis ICA-AROMA ICA-based Automatic Removal Of Motion Artifacts ILP inferior-left-posterior INCF International Neuroinformatics Coordinating Facility KERIC Karolinska experimental research and imaging centre LPI left-posterior-inferior MEG magnetoencephalography MELODIC Multivariate Exploratory Linear Optimized Decomposition into Independent Components MGB medial geniculate body MRI magnetic resonance imaging NMR nuclear magnetic resonance PCA principal component analysis RAI right-anterior-inferior RF radio frequency ROI Regions of interest rs-fMRI resting state functional MRI RSFC resting state functional connectivity RSN resting state network SCA seed-based correlation analysis SE-EPI spin echo EPI SOC superior olivary complex TE echo time TR repetition time Chapter 1 Introduction Since the early nineties, when Ogawa et al. [32] and Biswal et al. [12] first introduced the scientific community to the blood-oxygen-level dependent (BOLD) mechanism and the existence of correlated fluctuations in the resting brain respectively, the neuroimaging community has succeeded in characterizing various functional areas of the brain. Rs-fMRI has only recently been used to examine functional connectivity in the auditory system due to inherent limitations. Over the last decade, a substantial amount of publications focused on the auditory structures have surfaced. Cheung et al. [15] demonstrated the feasi- bility of auditory functional MRI (fMRI) in rats, by studying the ascending auditory pathway using task-based fMRI, followed by the examination of sub-cortical binaural processing and sound localization in animal models [27]. Additionally, common resting state networks between humans and rats have been verified [44]. The above mentioned studies examined healthy rodents; understanding the effect that auditory impairments exert to the brain has yet to be investigated. This thesis is a step towards the latter. The purpose of this thesis is twofold; to examine how and when monaural canal atresia affects functional brain connectivity and to
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