DEGREE PROJECT IN MEDICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Clustering-based approach for the localization of Human Brain Nuclei SAMEER MANICKAM KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH Clustering-based approach for the localization of Human Brain Nuclei Sameer Manickam Master in Medical Engineering Supervisor: Benjamin Garzon Host Institute: Karolinska Institutet, Reviewer: Chunliang Wang School of Engineering Sciences in Chemistry, Biotechnology and Health 1 Klusterbaserat tillvägagångssätt för lokalisering av hjärnkärnor Sameer Manickam 2 Dedicated to my Grandparents & my friend Ismael Faruqi 3 Acknowledgement: I want to start by expressing my deepest gratitude to my supervisor Dr Benjamin Garzon who gave me this opportunity to work on such a challenging project. I want to thank him for all the support he showed to me whenever I needed to be it within the project or even in administrative activities. Without his support and guidance, it would have been an impossible job to get a project like this and complete it. I want to thank my reviewer and group supervisor, Dr Chunliang Wang, for his guidance and support through all the supervision meetings. The feedbacks and ideas gave me many insights as to how to present the research problem at hand. My sincerest thanks to Dr Anup Singh for introducing me to the world of MRI and Image processing. All my interest developed in this field is because of the opportunities he provided me to work on the challenges in MR. I want to thank my friends Prashant, Tejas, Shruti, Mahima, Sakshi and Siddharth for their constant support and making my life filled with joy. I want to express my sincerest gratitude and love towards Ranjith, Maggi, Vijay and Swetha for making my stay in Stockholm comfortable as my home. Lastly and most importantly, I thank my parents and my sister, without whom I am nothing. I want to thank them for the love and blessing they shine upon me. I cannot express how lucky to have them in my life. 4 Abstract: The study of brain nuclei in neuroimaging poses challenges owing to its small size. Many neuroimaging studies have been reported for effectively locating these nuclei and characterizing their functional connectivity with other regions of the brain. Hypothalamus, Locus Coeruleus and Ventral Tegmental area are such nuclei found in the human brain, which are challenging to visualize owing to their size and lack of tissue contrast with surrounding regions. Resting-state functional magnetic resonance imaging (rsfMRI) analysis on these nuclei enabled researchers to characterize their connectivity with other regions of the brain. An automated method to successfully isolate voxels belonging to these nuclei is still a great challenge in the field of neuroimaging. Atlas-based segmentation is the most common method used to study anatomy and functional connectivity of these brain nuclei. However, atlas-based segmentation has shown inconsistency due to variation in brain atlases owing to different population studies. Therefore, in this study, we try to address the research problem of brain nuclei imaging using a clustering-based approach. Clustering-based methods separate of voxels utilizing their structural and functional homogeneity to each other. This type of method can help locate and cluster the voxels belonging to the nuclei. Elimination of erroneous voxels by the use of clustering methods would significantly improve the structural and functional analysis of the nuclei in the human brain. Since several clustering methods are available in neuroimaging studies, the goal of this study is to find a robust model that has less variability across different subjects. Non-parametrical statistical analysis was performed as functional magnetic resonance imaging (fMRI) based studies are corrupted with noise and artefact. Statistical investigation on the fMRI data helps to assess the significant experimental effects. Keywords: Neuroimaging, fMRI, Human Brain Nuclei, Clustering-based approach, Atlas-based segmentation 5 Sammanfattning: Studien av hjärnkärnor vid neuroimaging utgör utmaningar på grund av dess lilla storlek. Många neuroimaging-studier har rapporterats för att effektivt lokalisera dessa kärnor och karakterisera deras funktionella koppling till andra hjärnregioner. Hypothalamus, locus coeruleus och ventralt tegmentalt område är sådana kärnor som finns i den mänskliga hjärnan som är svåra att visualisera på grund av deras storlek och felaktig framställning med omgivande regioner. Resting-state functional magnetic resonance imaging (rsfMRI) analys av dessa kärnor gjorde det möjligt för forskare att karakterisera deras anslutning till andra hjärnregioner. En automatiserad metod för att framgångsrikt isolera voxels som tillhör dessa kärnor är fortfarande en stor utmaning inom området neuroavbildning. Atlasbaserad segmentering är den vanligaste metoden som används för att studera anatomi och funktionell anslutning av dessa hjärnkärnor. Atlasbaserad segmentering har dock visat inkonsekvens på grund av variation i hjärnatlas på grund av olika befolkningsstudier. Därför försöker denna studie att ta itu med forskningsproblemet med hjärnkärnavbildning med hjälp av ett klusterbaserat tillvägagångssätt. Klusterbaserade metoder hjälper till att separera voxels med hjälp av deras strukturella och funktionella homogenitet med varandra. Denna typ av metod kan hjälpa till att lokalisera och kluster de voxels som tillhör kärnorna. Eliminering av felaktiga voxels genom användning av klustermetoder skulle avsevärt förbättra den strukturella och funktionella analysen av kärnorna i den mänskliga hjärnan. Eftersom flera klustermetoder finns tillgängliga i neuroimaging-studier är målet med denna studie att hitta en robust modell som har mindre variation mellan olika ämnen. Icke-parametrisk statistisk analys utfördes eftersom funktionella magnetiska resonanstomografi (fMRI) baserade studier är skadade med brus och artefakt. Statistisk undersökning av fMRI-data hjälper till att bedöma de signifikanta experimentella effekterna. 6 Table of Contents 1. Introduction. 2. Materials and Methods. 2.1. Data 2.1.1. Midnight Scan Club Dataset 2.2. Atlas Based Segmentation 2.3. Clustering Methods 2.3.1. fMRI data preparation 2.3.2. Kmeans Clustering 2.3.3. Kmedoids Clustering 2.3.4. Spectral Clustering 2.3.5. Wards Clustering 2.4. Quantitative Analysis 2.4.1. Clustering Evaluation 2.4.1.1. Silhouette Coefficient 2.4.2. Dice Coefficient 2.4.3. Variance 2.5. Qualitative Analysis 2.5.1. Test for significance 2.5.2. FSL Randomise 3. Results. 3.1. Quantitative Analysis 3.1.1. Hypothalamus 3.1.2. Locus Coeruleus 3.1.3. Ventral Tegmental Area 3.2. Qualitative Analysis 3.2.1. Hypothalamus 3.2.2. Locus Coeruleus 3.2.3. Ventral Tegmental Area 4. Discussion. 5. Conclusion and Future Work. 6. References Appendix A. State of the Art 7 1. Introduction: The recent advancement in Magnetic Resonance Imaging (MRI) such as the use of ultra-high field scanners (Stucht et al., 2015), data-processing techniques and data acquisition has opened the field of neuroimaging to study on Human Cognition and Behaviour. Recently, there has been considerable advancement in imaging of the nuclei in the human brain to determine the functional connectivity. These nuclei are challenging to image in an MRI because of their small size and their location in the human brain. Nuclei such as Locus Coeruleus (LC) is studied extensively and has been involved in many imaging studies as it is the principal noradrenergic Nucleus of the brain (Samuels & Szabadi, 2008). Its functions include complex cognitive processing (like working, memory, learning and attention), emotion in adults, pathophysiology in neurodegenerative and neurodevelopmental disorders. LC has also shown its involvement in neurological disorders like dementia, Alzheimer's (Weinshenker, 2008), anxiety disorder (McCall et al., 2015), Parkinson (German et al., 1992), and depression (Weiss et al., 1994). Another critical nucleus is the Ventral Tegmental Area, and it is one of the significant dopaminergic nuclei alongside Substantia Nigra (SN) of the brain. The VTA is heavily linked with rewarding and reinforcing processes, being part of the Dopaminergic system its primary functions are reward-based and associate learning (MacInnes et al., 2016), motivation (Berridge & Kringelbach, 2013), memory (Kahn & Shohamy, 2013)and cognitive control in decision making (Goschke & Bolte, 2014). The other nuclei focused on this study is Hypothalamus (HTH) as it is small and functionally diverse. The HTH is known for its functions in controlling the vital functions of the body like Autonomic and endocrine functions, immune response, food homeostasis, sexual activity, stress, sleep, and thermoregulation. (Saper & Lowell, 2014). The use of functional MRI (fMRI) in addition to the structural MR images to view the functional connectivity of each Nucleus may assist in locating the nuclei. The functional connectivity analysis of resting-state fMRI datasets is a powerful tool to visualize the neuronal design of the human brain (Cole et al., 2014), though the fMRI signal is very noisy. Blood Oxygen Level Dependent (BOLD) fMRI measures the neuronal activity of the brain by detecting the changes in relative levels of oxygenated and deoxygenated blood. The signal acquired using BOLD fMRI is corrupted with noises and artefacts resulting from neuronal and metabolic activities. Non-neural effects in the BOLD fMRI time series voxel comprises