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 throughout 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 produce a processing pipeline routine for analyz- ing auditory areas in rodents. In order to meet the first goal, a cohort of 13 rats, divided in control and patient groups, was preprocessed and statistically analysed. The given dataset was part of a longitudinal study. The pipeline described in the thesis is a collection of Python and shell scripts that were produce by adapting existing human neuroimaging software on rodent data. The methodological challenges were many; large amount of noise, low frequency nature of the BOLD signal and artefacts caused by the high magnetic fields used in animal studies. Lastly, the usage of anesthesia has been shown to affect analysis and interpretation of results [33]. This thesis is divided in four chapters, containing details on the preprocessing routine that was applied on the dataset, subsequent statistical analysis and the final results. In Chapter 2, one can find all the methods used for data preparation and preprocessing in additionto encountered problems and unsuccessful methods. Statistical analysis results are given in Chapter 3, divided in the output of independent component analysis(ICA) and seed-based correlation analysis(SCA), with adequate description of each method. Lastly, Chapter 4 2 Introduction proceeds to evaluation of the results, discussion related both to the thesis at hand as well as current trends of research, limitations and possible future endeavours. For those interested in a more detailed background of this thesis, the state of the art (see AppendixA) can provide insights and detailed explanation of MR sequences, functional connectivity, the rodent auditory system and literature sources related to the field of rs-fMRI. Chapter 2

Materials and methods

In order to study possible implications of monaural canal atresia in the developing brain, a longitudinal investigation of a rat model was developed. In order to mimic the disease, a set of rats had their left ear sutured following birth and were then imaged at age periods 0, 3, 6 and 12 months old. For the present thesis, the second age group was selected for investigation, with the hypothesis that any changes in brain connectivity might be evident as soon as three months after the intervention.

2.1 Dataset description

The age group of 3 months old contains 13 rats of which 5 rats had no intervention (from now on referred to as the control group) and 8 rats had one ear canal closed (from now on referred to as the atresic group). Monaural canal atresia was accomplished by surgically suturing the left ear canal of the rats before 10 days of age. The rats were then scanned at 1, 3, 6 and 12 months of age.

2.1.1 Data acquisition

The dataset was acquired between years 2015 and 2016 at Karolinska experimental research and imaging centre (KERIC), in a 9.4 T MR-scanner (VnmrJ software 3.1, Agilent, Yarnton, UK) using a gradient coil of 12 cm inner diameter, a volume coil of 72 mm inner diameter and a 4-channel surface coil [44]. Acquisition of anatomical scans was done using fast spin echo sequence with 3s repetition time (TR), 256 × 256 data matrix, 32 × 32 mm2 field of view (FOV) and voxel size [0.125, 0.125, 0.315] mm3. Echo planar imaging (EPI) data were acquired using single shot gradient echo sequence with 64 × 128 data matrix, 32 × 32 mm2 FOV,TR 1.2s, echo time (TE) 0.038s, phase encoding direction L to R and voxel size [0.375, 0.25, 0.42] mm3.

2.1.2 Data structure

Information and scans of each subject were provided in a folder described by the acquisition date. Structural, functional and diffusion weighted (DW) scans were contained in 4 Materials and methods various subfolders without specific structure. Distinguishing between different sequences took a long time and was done by reading the headers as well as the acquisition file that came with each subject. In summary, each subject had a high resolution anatomical scan, two lower resolution anatomical scans, two sessions of 400 fMRI, one session of 200 fMRI scans acquired with interleaved rotation of the FOV and two scans of DW scans with opposing encoding directions. Of the above, the ones used for preprocessing and analysis in this project, were the high resolution reference anatomical scan and all fMRI data.

2.1.3 Data format

All scans came in the form of .fid, .fdf and .nii (where .fid were the raw k-space data given by the scanner and .fdf were the same data in a different format). The majority of neuroimaging software packages have adopted the Nifti-1 file format [17] and thus require their input data to be in .nii. Metadata were included in the header and a procpar file that contained all technical parameters as used by the scanner.

2.1.4 BIDS organization

Large, complicated datasets can be simplified and become processing-friendly by following a structure format such as the Brain Imaging Data Structure (BIDS) standard [19]. For this reason it was decided to adopt this standard, as the processing of the original dataset was time consuming. BIDS is a simple, intuitive arrangement of data, where each subject of a study has its own folder with experiments from different modalities, .json files that contain metadata and a clear naming convention, providing a straightforward organisation. The unique structure of the subjects’ folders presented an obstacle in using pre-existing tools for implementing BIDS. A custom-made Python script was created based on the starterkit provided by International Neuroinformatics Coordinating Facility (INCF)[8]. For better performance and efficiency, the script was built under Nipype framework [18]. The dataset was validated using the official tool BIDS-validator [9]. Applying BIDS resulted into a smaller and more consistent set (see Fig. 2.1), making subsequent usage much easier. Pybids package [42] was used for performing queries over highly structured files. 2.2 Data preprocessing pipeline 5

Figure 2.1: Left: Originally, each subject came in a folder described by the acquisition date. Sub-folders contained different sequences but the naming convention did not provide any information as to the exact contents of the folders. Right: After applying the BIDS conversion script, a more compact and structured set is created. The original dataset consisted of 117 directories and 12683 files, whereas after restructuring the result is 65 directories and 206 files.

2.2 Data preprocessing pipeline

Adequate and well-thought preprocessing of the dataset will affect the outcome of the analysis. The goal of preprocessing is to remove as much interfering artefacts and non-neural signal as possible, while simultaneously retaining the signal of interest. Because of the low frequency nature of the BOLD mechanism, conventional fMRI methods were combined with rs-fMRI methods to produce the present pipeline. Details of the three major pipelines consulted for this thesis are given in AppendixA, section A.4. Initially, the quality of the dataset had to be established. One of the subjects had to be excluded, since distortion correction would not be possible due to lack of interleaved acquisitions, and certain images had their header information corrected. Preprocessing began with the reference anatomical images, on which functional data would be later overlayed. The anatomical image had to be skull stripped, corrected for intensity inhomogeneities and segmented in three tissue classes. The functional data were then corrected for motion and distortions and were then registered on the same subject high resolution image. To enable 6 Materials and methods group level comparisons, all the subjects were normalised to the standard space template and were then smoothed. In order to remove motion-related noise, ICA-AROMA software was used. Noise components were identified and removed and the de-noised dataset was time filtered. As a final step, the processed set underwent statistical analysis to extract areasof activation. A graphical representation of the pipeline is given in figure 2.2.

Figure 2.2: Flowchart of the implemented pipeline. One can look at the pipeline as four processes: Data organization subprocess corresponds to section 2.1.4, where the dataset and the BIDS standard are explained. Quality control entails any corrections done on the dataset (see section 2.2.1). Once data is ready for preprocessing, the pipeline is split into anatomical and functional stream (see sections 2.2.2 and 2.2.3 respectively). After the end of the pipeline, statistical analysis can be performed. 2.2 Data preprocessing pipeline 7

2.2.1 Quality control

This step ensures that the data fed into the pipeline for processing are valid and do not present any kind of corruption. Various magnetic resonance imaging (MRI) artefacts can exist and are classified according to origin; tissue properties, patient motion and technical parameters [20]. During data assessment, the following inconsistencies were discovered:

1. Voxel dimensions: According to the acquisition protocol, the high resolution anatomical scan should have voxel size [0.125, 0.125, 0.315] mm3. It was discovered that 8 of the subjects had incorrect header information, where voxel size was [1, 1, 1] mm3. The header was corrected using FSL command:

fslchpixdim image 0.125 0.125 0.315

2. Orientation: Anatomical data came with default orientation left-posterior-inferior (LPI) whereas functional data were oriented in inferior-left-posterior (ILP). Initially, the anatomical data were reoriented in right-anterior-inferior (RAI), in order to be compatible with the segmentation software (see section 2.2.2) and eventually all anatomical data were reoriented back to LPI. Reorienting was made possible using FSL command:

fslswapdim image x(-x) y(-y) z(-z) image_out

3. Image dimensions: Anatomical data acquired in 2016, had size 512 × 256 × 40 whereas data acquired in 2015 had size 256 × 256 × 40. Therefore, the 2016 set had to be manually cropped in order to remove background information, using Fiji [43].

2.2.2 Anatomical preprocessing stream

In fMRI studies, structural images are acquired in addition to the functional ones, to be used as reference. Typically, fMRI images have low resolution and anatomical contrast, thus a high-resolution anatomical scan is required, to enable registration and region demarcation. Structural images require some preprocessing prior to usage as well [38]. 8 Materials and methods

Brain extraction

As a first step, the anatomical image had to be skull stripped. Brain Extraction Tool (BET) from FSL was initially tested but failed to retrieve the correct brain structure. Therefore, a software specifically developed for rat brain segmentation was used[7, 49, 50]. Mialite uses random forest training to extract as much brain tissue as possible (see Fig. 2.3). The output was a binary mask (see Fig. 2.4), which was then multiplied with the T2-weighted structural image using FSL command: fslmaths structural -mul binarymask brain

(a) Iterative brain identification (b) Fine-tuning of mask

Figure 2.3: Mialite intermediate steps for calculating the brain mask

Figure 2.4: The T2 weighted high-resolution image was fed to Mialite which then produced a binary brain mask. By multiplying the two, all non-brain areas are removed producing the brain extracted image on the right. 2.2 Data preprocessing pipeline 9

Bias field correction

By visually inspecting the brain extracted image, variations in intensity were identified. These inhomogeneities were corrected by applying N4ITK algorithm [47]. Module N4BiasFieldCorrection provided by Advanced Normalization Tools (ANTs) was used on the current dataset for bias field correction [2]. The results of this algorithm are shown in figure 2.5.

Figure 2.5: The image on the left shows the original scan. It is visually evident that the superior has a different intensity than the inferior, which is verified by the calculated bias field shown on the right. After correction, the scan in the middle shows a uniform intensity.

Tissue segmentation

Tissue segmentation was performed on a high resolution anatomical scan in order to classify different tissue. All masks were drawn manually using ITKsnap [54]. Instead of doing tissue segmentation on each subject, which would take an enormous amount of time, a random subject was selected as the template upon which all delineation was done. All masks could then be transformed to the individual functional space. Masks of background, brain edge and cerebrospinal fluid (CSF) were created (see Fig. 2.6). These areas do not reflect neural activity in resting-state time-series thus can be regressed out of the analysis [11, 44]. 10 Materials and methods

Figure 2.6: Masks capturing the edges of the brain, the CSF and background were created for later on regression.

Regions of interest (ROI)

ROI had to be drawn for restricting functional connectivity analysis to those areas. Unlike human data, there is currently no standardized coordinate frame for rats distributed with neuroimaging software, thus all ROI were manually delineated using ITKsnap with the help of a medical expert (see Fig. 2.7). Auditory structures do not exhibit strict boundaries; as a result the whole process took a long time for completion. A paper atlas was used for consultation [37]. Subsequent analysis required transformation from structural to functional space.

Figure 2.7: In total, 10 regions (5 structures on each hemisphere) were manually drawn for restricting analysis to those areas. Information on these areas can be found in A.3. Structures were drawn on the high resolution anatomical image of the template subject. 2.2 Data preprocessing pipeline 11

2.2.3 Functional preprocessing stream

The functional preprocessing stream is responsible for correcting artefacts and denoising so that, when the analysis takes place, any temporal correlation emerging solely by neural activation will be revealed. The choice of methods depends on the dataset at hand as well as the type of analysis and the goal of the study.

Motion correction

Each set was corrected by using the middle volume as reference for aligning the rest of the images. Prior to motion correction, reorientation was needed. Unlike anatomical data, functional data needed axes permutation, therefore fslorient was not enough. In order to rearrange the image, it had to be treated as a matrix where the first column should change place with the third column. This was made possible by using MRtrix3[46]:

mrconvert image image_out -stride 1,2,3,4 -axes 0,2,1,3

Following rearrangement, the image was re-sampled in 2 mm3 isotropic voxels to resem- ble human data [3] and correction was performed using MCFLIRT [25].

mcflirt -in img -out img_out -plots

Motion parameters were saved in the form of a file, to be later used in regression [10, 29]. Following correction, signal contribution of misplaced voxels was assigned to the correct coordinates in all time series [6].

Distortion correction

Air-tissue interfaces of the subjects led to geometrical distortions, that were corrected using a data-driven, field inhomogeneity map. Calculation of the field was done byusing images acquired with opposing phase encoding directions [1]. The interleaved 4D volume of fMRI data was split into 200 3D images, which were reoriented, realigned and merged again into a single volume. Reorientation was necessary because the interleaved acquisitions were taken with 0° and 180° phase. Information about the phase of each image was given in the procpar file of the subjects. Following preparation of the volume, topup tool from FSL was used for estimating the field. By comparing the opposing images, topup returned the field that maximized the similarity between them [26, 45].

topup –imain=opposites –datain=acqparams –config=conf –fout=field –iout=result 12 Materials and methods

Topup required as inputs the opposing images opposites, a file containing information about the encoding direction of each image and total readout time acqparams, and a configuration file conf. While the configuration file was provided by FSL, certain parameters had to be adjusted to match rodent brains. Once the susceptibility-induced off-resonance field was calculated, it was applied on all functional data of the subject using applytopup

applytopup –imain=image –inindex=1 –datain=acqparams –topup=result –out=image_out –method=jac

The parameter inindex was used to inform the tool on which direction it should apply the correction e.g. when topup was performed, both L → R and R → L images were used, but the functional data that were eventually corrected and used, follow only one of those directions. The phase encoding direction of the functional dataset was L→ R. The result of distortion correction is given in figure 2.8.

Figure 2.8: On the left, the scan before distortion correction. The middle image represents the estimated off resonance field as given by topup and the image on the right shows the corrected image after applytopup.

Multi-stage registration

Registration of sessions was a two-stage process and was done by adapting features from FEAT-GUI [53]. Initially, the functional data of each subject were registered on the anatomical image of the same subject. Within subject registration requires linear transformation using 6 degrees of freedom (DOF). Using FMRIB’s Linear Image Registration Tool (FLIRT) [23, 25], transformation matrices between functional and anatomical space were calculated and saved for further usage [10]. A co-registration result of an image is given in figure 2.9a. Following registration from functional to anatomical, spatial normalization was performed. Structural images of the subjects were normalised to the standard template. An arbitrary brain of the group was chosen as that [44]. In addition to linear transformation, 2.2 Data preprocessing pipeline 13

spatial normalization required non-linear registration for further refinement. This was done using FMRIB’s Non-Linear Image Registration Tool (FNIRT)[24, 45] and the result can be seen in figure 2.9b. The two transformation matrices produced by the previous steps were then combined into a third one that directly transforms a functional image from native to standard space (see Fig. 2.9c). While it would be possible to do the registration process in only one step, that would have resulted in losing a lot of important details when transforming a functional image to the standard anatomical template directly.

(a) Transformation of functional image to structural space: the top line shows functional data in grey and red lines correspond to structural data whereas the bottom line shows structural data in grey and red lines correspond to functional data

(b) Transformation of structural to standard space: the top line shows structural data in grey and red lines correspond to the template whereas the bottom line shows the template in grey and red lines correspond to structural data

(c) Transformation of functional to standard space: the top line shows functional data in grey and red lines correspond to the template whereas the bottom line shows the template in grey and the red lines correspond to in red

Figure 2.9: The two stage registration takes the functional image to the standard space, with an intermediate step of registering to the structural space of the subject. Using this type of registration ensures that less detail gets lost while changing spaces 14 Materials and methods

Spatial filtering

In order to smooth the data, the volumes of all subjects were spatially filtered using a Gaussian kernel of full-width half-max (FWHM) 0.8 mm. In cases where areas under investigation are small structures, the kernel size should be comparable to the voxel size. A rule of thumb is to choose a kernel size that is three times larger than the voxel size.

ICA-based cleanup

For resting state specific denoising, a technique based on ICA was used (see section A.5). ICA-AROMA is a data-driven cleaning technique that was applied on single-subject level in order to separate and remove noise components related to motion [41, 40]. ICA-AROMA classified motion related noise based on four temporal and spatial features. Classification of components is based on high-frequency content, correlation with realignment parameters, edge fraction and CSF fraction. As is the case with most software used for this project, ICA-AROMA was designed for human data, thus had to be customised for using on rodent brains. For edge and CSF fraction calculation, manually created segmentation masks were used (see section 2.2.2).

ICA_AROMA.py -in noisydata -out cleaned -mc motionpar -affmat transformationmatrice -warp warps -m aromamask

In addition to the functional data input, ICA-AROMA requires a mask that removes non- brain tissue from the functional set. This mask was created by transforming the binary mask given from skull segmentation (see section 2.2.2) into functional space. Finally, previously calculated motion parameters (see section 2.2.3) had to be used as input. Transformationma- trice and warps were previously calculated during multi-stage registration. The software returned a list of the classified noise components which were then regressed out of the data [10]. Non-aggressive denoising was chosen as cleaning strategy for preserving as much signal as possible.

Highpass temporal filtering

The final step of the preprocessing routine was highpass temporal filtering, for removing scanner drift artefacts from the data [10]. Unlike spatial filtering, temporal filtering deals with the time series of each voxel instead of each volume [6]. A filter cut-off at 100 seconds (0.01 Hz) was used [3]. High pass filtering was adapted from FEAT GUI. 2.3 Statistical analysis 15

2.3 Statistical analysis

Massive amount of data, weak signal of interest and complex noise structure make statistical analysis of fMRI data a very challenging task. Section 2.2.3 focused on the methodology followed for removing unnecessary noise, strengthen the signal of interest and account for variations between subjects and modalitied, in order to improve the validity of connectivity analysis.

2.3.1 High level design

Twelve subjects were preprocessed and cleaned from noise, each of them with two functional runs, meaning that repeated measures must be taken into account during the statistical analysis. In order to investigate differences between atresic and control group, ICA and SCA were used for generating group-specific spatial maps. Dual regression was then used for generating subject-specific spatial maps. Prior to analysis, filenames were added into a text file and a design matrix describing the experiment was created (see Fig. 2.10). The design matrix has 24 rows that correspond to the number of functional data inputs and two columns indicating the group membership. Four contrasts were created for testing specific hypotheses. Contrast 1 (C1) is responsible for finding areas that are more active in the atresic than the control group (group A>group B) whereas Contrast 2 (C2) attempts to find areas more active in the control group thanthe atresic (group B > group A). Finally, contrast 3 (C3) and contrast 4 (C4) capture the mean activation of atresic and control group respectively. Both design matrix and contrasts were created using GLM_gui from FSL. To obtain activation maps, two methodologies were used. Unlike task-based fMRI, rs-fMRI cannot be described by a temporal model, thus standard general linear model (GLM) analysis is not applicable. SCA and ICA have become the preferred analysis approaches. SCA is a model-based, hypothesis-driven, univariate approach that examines temporal correlation of a specific region while gICA is a model-free, multivariate approach that decomposes the data into spatially independent components [22]. Both methods were applied on a group level, therefore an additional step for converting the group spatial map onto the single subject level was needed. Dual regression was used to generate subject-specific maps [5, 31]. 16 Materials and methods

Figure 2.10: The design matrix describes the experiment by indicating group memberships and the defining the contrasts on which group differences are examined

2.3.2 Dual regression

The algorithm begins by regressing group-spatial maps into subject-specific temporal maps, then uses the timeseries as temporal regressors to produce subject-specific spatial maps and finally compares those across the groups to search for group differences, using FSL’s non parametric permutation-testing tool randomise [30, 52]. This algorithm has three distinct stages with various intermediate outputs. The subject- specific temporal maps of stage 1 are given as text files, one per subject, whichcontains as many timeseries as the group components. At the second stage, 3 types of outputs are generated, one of which is a 4D image file per gICA component, and, within each, having one timepoint (3D image) per subject. At the final stage , cross-subject modelling for each group-ICA component is performed by randomise.

2.3.3 SCA

SCA was originally proposed by Biswal et al. and requires the a priori definition of a seed area, in relation to which functional connectivity is examined [12, 48]. As described in section 2.2.2, 10 seed locations were drawn w.r.t the standard space template. For group level comparison, all functional data must also be in standard space.

dual_regression ROIs/selectedROI 1 designmatrix.mat designmatrix.con 5000 ROIresults.dr ‘cat inputs.txt‘ 2.3 Statistical analysis 17

The above command had to be run 10 times, each time giving a different ROI as input. By using dual regression, the mean time series of the seed region were extracted and were compared to every other brain voxel.

2.3.4 Group ICA

As opposed to SCA, which analyses the correlation of the seed region with every other voxel separately, ICA takes into account relationships between multiple data points, thus extracting all detectable networks within a subject. ICA uses multivariate decomposition to separate the BOLD signal into several independent components in the form of spatial maps, which are temporally correlated [16, 28]. The first step of gICA required temporal concatenation of all functional inputs into a 2D matrix (time x space), which was then decomposed into timeseries and associated spatial maps of the underlying signal sources. Group spatial maps were generated using FSL’s Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) tool [5,4].

melodic -i input_files.txt -o groupICA -a concat -d 15 –all –report

MELODIC is based on the FAST-ICA alorithm [21]. In human studies, it is usually sufficient to use approximately 25 components. Since rodent brains are smaller, thedesired components were defined as 15. MELODIC includes further improvements such aspre- whitening and variance normalisation.

dual_regression melodic_IC.nii.gz 1 designmatrix.mat designmatrix.con 5000 gICA.dr ‘cat inputs.txt‘

The argument melodic_IC.nii.gz corresponds to the group level ICA components that were generated by MELODIC, value 1 corresponds to variance normalisation of timecourses during the second stage and value 5000 defines the number of permutations that randomise will run.

Chapter 3

Results

In total, 24 functional data inputs were analysed using SCA and ICA techniques to examine functional connectivity. The final stage of regression gave a set of multiple comparison corrected p-value images per contrast for every component (in the SCA analysis, there is only one component whereas in gICA there are 15 components). For easier visualization, the outputs were stored as 1-p values. Using FSLeyes, the 1-p statistical maps were overlayed on the corresponding EPI images, in addition to the anatomical one, to allow the evaluation of results in combination with the effects of artefacts and blurring [34]. To visualize areas of significance, values below 0.95 were excluded.

3.1 SCA activation maps

Ten regions of interest were fed into the SCA. Four contrasts were examined for statistically significant areas of activation. Out of the ten regions, only the left auditory cortex and left cochlear nucleus returned clusters that survived the statistical thresholding. The left auditory cortex showed a statistically significant result for contrast 1, which is the comparison of patients minus controls. A second cluster appeared for the seed region of left cochlear nucleus, again for the same contrast. Both groups showed statistically significant mean activation in all auditory areas except the superior olivary complex. This area was outside the FOV of several subjects, thus no spatial maps were calculated for it. Analysis of each region required approximately 6 hours to finish. The most important activation results are presented in figures 3.1- 3.3. 20 Results

Figure 3.1: For the examined question patients-controls and with the seed placed on the left auditory cortex (yellow), an activation cluster (red) appears on the contra-lateral side of the brain, indicating higher activation of that area in patients.

Figure 3.2: The mean activation of the control group (red) and mean activation of the atresic group (blue) in combination with the seed of the left auditory cortex. 3.2 Group ICA activation maps 21

Figure 3.3: For the examined question patients-controls and with the seed placed on the left cochlear nucleus (yellow), an activation cluster (red) appears, indicating higher activation of that area in patients.

3.2 Group ICA activation maps

A fixed preprocessing routine was applied on the data; the inputs were masked fromnon brain voxels, demeaned and had variance normalisation. Following whitening, probabilistic Principal component analysis was used to project the inputs into a 15-dimensional space. Dual regression applied on the 15 spatial maps produced from gICA produced subject specific maps for each examined contrast. Following evaluation of the maps, two components presented statistically significant clusters. Figure 3.4 shows the cluster returned for comparison patients - controls (contrast 1) and figure 3.5 shows a cluster for controls - patients (contrast 2). 22 Results

Figure 3.4: For the examined contrast patients - controls, a cluster appears in the area of the left auditory cortex, indicating higher activation in the control group. The image on the top shows the cluster overlaid on the anatomical image while the image in the middle shows the cluster overlaid on the EPI. The image on the bottom shows a comparison of the cluster with the manually delineated ROI. 3.2 Group ICA activation maps 23

Figure 3.5: For the examined contrast controls - patients, a cluster appears in the area of the left cochlear nucleus, indicating higher activation in the patient group. The image on the top shows the cluster overlaid on the anatomical image while the image in the middle shows the cluster overlaid on the EPI. The image on the bottom shows a comparison of the cluster with the manually delineated ROI.

Chapter 4

Discussion

4.1 Appraisal of findings

The present study produced a pipeline for preprocessing and analysing an auditory animal model in investigation of functional connectivity impairments of monoaural canal atresia in rats. The developed pipeline succeeded in implementing all necessary steps for adequate preprocessing of the subjects and resulted in several clusters appearing following statistical analysis. We have shown that the current neuroimaging software used in human studies, can be adapted and is applicable to the animal model. While there are numerous methods in the literature on how to remove non-neural signals as well as many more techniques than those adopted for this pipeline, it was decided that it was best to adopt a strategy where few but robust methods are used rather than adopting the whole spectra of proposed methods, risking loss of signal of interest. With respect to the clinical question of whether and how monaural canal atresia affects the functional connectivity of the brain, the results do not give a definite answer. Careful evaluation of the identified clusters needs to be done by medical experts before any conclusions are drawn. The identified cluster do seem promising, especially the ones produced by ICA, as they appear near areas of interest. Unfortunately, the small size of the sample does not provide much statistical power. There is also the question of whether this age group was the appropriate one for identifying activation. The age group of 3 months old rats could be too young to exhibit any significant alterations in the brain. It remains to see what kindof information the rest of the age groups can provide us. Regarding statistical analysis, two different methods were used. Both methods have been proven valuable throughout the literature, but in the present study there is enough evidence to say that ICA provides better results than SCA. While in general the two methods should highlight the same networks [48], it could be that the small size of the structures in combination with the remaining distortions near the auditory areas has negative impact on the outcome of SCA. If we take the left cochlear nucleus (CN) as example, it is evident that ICA has identified an activation cluster related to it, as opposed to SCA. This could indicate that using SCA for data that still contain distortions does not give valuable results, as shown in figure 4.1. 26 Discussion

Figure 4.1: The distortions that appear near tissue - air interfaces negatively affect the result of a hypothesis- driven approach such as SCA. The image on the top illustrates the ROI of left cochlear nucleus as created on the anatomical image. The middle images corresponds to the EPI image after distortion correction and normalisation to the standard space. The image on the bottom shows that despite efforts, the seed location does not correspond to the actual location of theCN, due to the distortions in that area. 4.2 Limitations 27

4.2 Limitations

As with every study, certain limitations can be identified, with respect to the general approach towards resting state studies, the processing methods used as well as the animal model itself. The biggest limitation and drawback in rs-fMRI research at the moment is the lack of validity between methods proposed. Unfortunately, this limitation extends to the present project as well. No general consensus exist about when each method should be used and what kind of drawbacks might appear. While several methods have been proposed for each steps of the standard processing pipeline (FIX vs ICA-AROMA, global signal regression vs CompCor [14], volume censoring etc. ), it has still not been determined whether some methods are more efficient or which strategies could be combined [39, 13, 36]. A general evaluation of the efficacy and reliability of the existing methodologies is crucial for the maturing of rs-fMRI research. This study has attempted to use the most well-established methods of the literature, but despite this, no comparison of methods was done prior to deciding which to use. An issue related to the dataset at hand is that, despite measures taken to correct for distortions, susceptibility artefacts still remain near important interfaces of air-tissue. As shown in figure 4.1, these distortions have affected the outcome of the analysis. The combination of the small sample with the persistent geometric distortions are a big limitation of this project. There is also evidence that usage of anaesthesia in studies of functional connectivity significantly affects the activated areas [51, 33]. Lastly, despite the long usage of animal models in fMRI studies, there is lack of standardized brain templates and segmentation masks that can be used during preprocessing. Instead, one has to perform manual segmentation, as was done in this study,which introduces additional variability to the data.

4.3 Future approaches

There are three directions one can follow for improving or building on this project. To answer the clinical question of how and when monaural canal atresia affects the functional connectivity of the rodent brain, the remaining age groups need also be preprocessed and analysed, both in a cross sectional and longitudinal analysis. Additionally,applying the pipeline on non anaesthetised rats could compensate for the activation masking that happens in the presence of anaesthesia, though in this case, special care for motion must be 28 Discussion taken. What is important to keep in mind is that a larger dataset could also provide a better understanding of the pipeline’s performance, as that would mean higher statistical power. With respect to the developed routine, the next step would be to include CSF and white matter regression and migrate the entire pipeline under Nypipe framework. Special focus should be given on improving the remaining distortion artefacts, following more data driven techniques where possible and adopt a brain atlas such as the volumetric Waxholm space atlas [35]. Finally, the pipeline should also be examined in terms of validity and efficacy, by comparing each used method with others mentioned in the literature and evaluate performance.

4.4 Conclusions

The goal of this projects was the development of a processing routine to investigate functional impairments of the rodent brain. A collection of scripts was developed and combined with algorithms of existing software in order to analyse a dataset of 13 rats, showing that it is possible to adapt software intended to be used on human for analysing rodents. Statistical analysis showed interesting results which are now left to be evaluated in combination with the remaining age groups. Despite efforts, geometrical distortions have not been completely eliminated, affecting the final outcome. A larger sample could help answering the initial hypothesis of this group. The developed pipeline should be further on evaluated, as to examine whether the usage of different denoising strategies has a positive or negative impact on the outcome. The variety of denoising algorithms and preprocessing choices poses a difficult question on which techniques help preserve the most out of the signal of interest. References 29

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Appendix A

State of the art

This thesis encompasses phenomena and concepts from various fields; physics, neuroscience, engineering and image processing. To facilitate the reader’s understanding of the presented methodology as well as to provide some essential background knowledge, the most important concepts will be explained in the following sections. Although acquiring magnetic resonance data was not part of my thesis, I dedicate a section to nuclear magnetic resonance (NMR), focusing on MRI and EPI sequences. The data I am processing were acquired using spin echo EPI (SE-EPI) sequence. Following the physics of magnetic resonance, I will explain what fMRI and rs-fMRI techniques are, the phenomenon on which they are based on and how they are related to the present thesis. This thesis focuses mostly on data analysis and different methods that are used to pre- process the data before further analysis is done. Several methods used in the literature are explained, existing software tools tailored for human data analysis are examined for adopting on rodent data and lastly, important research already done in the field is described.

A.1 Magnetic resonance imaging sequences

The acquisition of magnetic resonance images of the body is based on the magnetic properties of the hydrogen nuclei present in water molecules, which are abundant in the human body. In the presence of a strong magnetic field B0, hydrogen protons align with the field. By applying a sequence of radio frequency (RF) pulses, according to a variety of different timing and amplitude parameters, the protons get excited. Following excitation, the protons attempt to return to equilibrium by emitting waves, a process called relaxation. By adjusting these parameters, we obtain images where various properties of the tissue are distinguished e.g. structure (anatomical imaging), flow (perfusion imaging) or neural activity (functional imaging) [29]. Two main relaxation processes exist, T1 and T2, which are independent but happen simultaneously. T1 happens along the Z-axis whereas T2 happens in the X-Y plane. A third relaxation process that is also important is the T2*, which is caused by local field inhomogeneities. Spatial localization of the receiving signal is accomplished by three orthogonal gradient coils i.e. the Gz gradient selects an axial slice (slice selection), the Gy gradient creates rows with different phases (phase encoding) and the Gx gradient creates columns with different frequencies (frequency encoding). In the space defined by frequency 36 State of the art and phase encoding, one line of k-space data is recorded for each phase, eachTR period, where k-space is an array of numbers representing spatial frequencies in the MRI andTR is the time between two excitation pulses. This means that for each k-space line acquisition a fresh spin excitation is required, which ultimately leads to relatively long acquisition times. This method of k-space filling is referred to as Cartesian sampling [20]. Here lies the difference of EPI sequence; although both methods employ Cartesian sampling for filling the k-space, sampling in EPI is done in a sequential, multiple-line way. In simple words, multiple lines of imaging data are acquired after a single RF excitation [33]. The long duration of MRI technique makes it prone to physiological motion artefacts such as blood flow, brain movement, respiration etc. The acquisition of multiple NMR echoes from a single spin excitation, has made it possible to shorten the previously time- consuming MRI data acquisition from minutes to much less than a second. EPI not only offers reduced imaging time, it also decreases motion artefacts and offers the ability to image rapid physiologic processes of the human body [44]. EPI is a powerful tool in medical applications that require fast acquisition times and minimization of motion artefacts. These properties have enabled us to investigate brain activity and functional connectivity, making EPI the most widely used technique in fMRI and brain imaging [46, 49].

A.2 The study of functional connectivity

The brain mapping community took advantage of the extraordinary development of magnetic resonance imaging techniques and is nowadays exploiting fMRI to widely study the brain. An increasing amount of studies using fMRI have been taking place in order to improve our knowledge regarding areas of the brain related to a certain task. Investigating the functional connectivity of the brain has been one of the most promising and interesting research areas related to the brain. When we refer to the concept of functional connectivity, we are talking about the connectivity between various brain regions that share functional properties. Existence of neuronal connections that facilitate synchronous activity between different regions indicate an interdependence between them. This synchronous activity refers to highly correlated, cerebral blood flow fluctuations that can occur even between regions divided by a substantial distance [10]. In order to understand how neuronal activity translates to interdependent regions, we must examine these fluctuations on the basis of signal processing and understand wherethey come from. The signal intensity of these fluctuations reflect BOLD mechanism and are used to examine the strength of neural connections. A.2 The study of functional connectivity 37

A.2.1 Haemodynamic response and the mechanism of BOLD signal

Ogawa et al. first observed that a causal relationship exists between neural activity and the BOLD signal [37]. Specifically, it was observed that the blood flow contrast varied with oxygen concentration in the blood. A detailed analysis of the BOLD mechanism, neural activity causation and multiple other important physiological and mathematical notions that contribute to this effect are given by Logothetis and Wandell, with the most important observation being that the coupling between neural activity and vascular response determines the amplitude and spatial resolution of the BOLD[29]. This coupling efficacy is referred to as the hemodynamic response efficiency. The BOLD mechanism is related to the magnetic properties of haemoglobin. Oxy- and deoxyhaemoglobin cause different changes in the T2* decay constant. When neuronal activity increases, the metabolic demands of the brain for oxygen increases, thus an increase in blood flow reaching the brain regions is observed. When oxygen is extracted fromthe blood, oxyhaemoglobin (diamagnetic) becomes deoxyhaemoglobin (paramagnetic); these chemical changes are responsible for variations in BOLD signal that ultimately alter the T2* contrast. It is important to keep in mind that the BOLD signal is not a direct measure of neuronal activity but is rather a complex mechanism encompassing metabolic and physiological changes that connect neuronal activity to functional connectivity.

A.2.2 Brain Networks

As Sporns states in his book, Networks of the brain, network science has found its way into the field of neuroscience by providing essential insight on brain organisation. Large scale brain networks have a central role in understanding how the brain functions and the impact various injuries can have on human behaviour and cognition [48]. The importance of studying large scale brain networks is profound in understanding the consequences of neurodegenerative diseases. Gaining knowledge of brain topology can provide neuroscientists the means to describe pathological conditions, model their spread and functional consequences [13]. Emphasising on the benefits of studying the brain’s complex network, the authors conclude that the emerging field of connectomics is a crucial framework for localising pathology and predicting behavioural changes as a result of focal pathology. Brain connectivity is divided in three modes: structural links between brain regions (anatomical connectivity), temporal correlation between remote neuronal events (functional connectivity) and direct effect of one neural element over another (effective connectivity) [48]. Various approaches for analysing brain connectivity can be used, though perhaps the 38 State of the art most commonly used comes from graph theory, as it can be applied in all three modes of connectivity. The brain regions are analogous to the nodes of the graph, while the neural axons, constituting the white matter of the brain, correspond to the edges of the graph. A variety of ex-vivo and in-vivo tools can be used for mapping the brain; Jbabdi et al. emphasizes the importance of using non-invasive techniques such a diffusion tractography and fMRI[23]. Current approaches such as the latter are a great tool for identifying networks and relating structure to function and behaviour.

Figure A.1: Fibre architecture of the human brain (left), a reconstructed structural brain network (middle) and the location of the brain’s core, its most highly and densely interconnected hub (right) [17]

A.2.3 Functional connectivity of the resting brain

Functional connectivity studies can be further divided in two subcategories; task-based fMRI, where the subject is required to perform a task, and rs-fMRI, where the subject remains relaxed with eyes closed. In this thesis, the data come from subjects in resting state, thus I will focus on rs-fMRI. fMRI appears to be the ideal neuroimaging technique for the investigation of resting- state network characteristics. The spatial resolution is superior to other methodologies such as electroencephalography (EEG) and magnetoencephalography (MEG), allowing for localization and separation of the various resting-state networks simultaneously [36]. Rs-fMRI is a method of functional brain imaging that can be used to evaluate regional interactions that occur when a subject is not performing an explicit task. There is a set of brain regions whose activity is high at rest and almost invariably goes down whenever one performs a demanding cognitive task. A.3 Rodent model and the auditory system 39

In 1995, it was suggested that correlations in resting state activity can provide insights into the function of neural systems even if they are not actively engaged [6]. Because brain activity is present even in the absence of an external task, any active brain region will have spontaneous fluctuations of the BOLD signal. It was later proposed that this activity reflects what they called the brain’s "default mode" which, according to the authors, reflects the true metabolic baseline of brain activity [45]. Resting state functional connectivity (RSFC) research has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity. One of the most studied networks of the brain is the default mode network (DMN)[16]. The DMN is a network of brain regions that shows higher level of activity when a person is at rest and lower level of activity when engaging in a certain task. Since about 2000, there has been an explosion in the use of rs-fMRI for characterizing brain function. One of the many attractive features of rs-fMRI is that it is readily translatable from humans to animals and back again. The development of rs-fMRI techniques for rodents has opened the door for the creation of new knowledge in the fields of neuroscience, psychiatry and neurology [40].

A.3 Rodent model and the auditory system

Shared characteristics between humans and animals advocate the usage of animal models for studying neurophysiology and neuropathology. There is a long history of using rodents models in brain research, as they share similar anatomical, physiological and behavioural features with humans [18]. Regarding functional connectivity changes in the human brain, it is possible to associate them with alterations seen in the rat brain by performing invasive procedures, that are otherwise not permitted in human studies. Studying rodent brains using fMRI is beneficial in understanding brain pharmacology and disease [26, 40]. In fact, a variety of neurological diseases have been under study so far, using rat brains. With respect to the significance of utilizing animal models, a group of doctors have been investigating the impact that monaural canal atresia can have on brain development. The use of rodents permitted the investigators to invasively alter the ear canal but also perform in-vivo as well as ex-vivo experiments to understand the neurological and functional alterations of the brain. DW-MRI and rs-fMRI data were acquired, where the DW data were processed in a past thesis [27]. In order to unravel possible functional differences between control and patient group, a subset of rs-fMRI data will be processed in the present project. 40 State of the art

(a) Coronal section (b) Sagittal section (c) Horizontal section

Figure A.2: Sections of the rat brain from each plane in stereotaxic coordinates. Reprinted from [42] with permission from Elsevier

Figure A.3: Anatomical positions of the five nuclei included in the auditory system and the auditory cortex. a) Coronal slices position b)3D activation c)localization of slices. Reprinted from [9] with permission from Elsevier.

Understanding the functionality of major auditory structures and the propagation of sound information from the outer ear to the primary auditory cortex, is crucial for making inferences about functional connectivity in the regions of interest. TheCN, superior olivary complex (SOC), lateral lemniscus, inferior colliculus (IC), medial geniculate body (MGB) and primary auditory cortex (AC) comprise all major auditory structures. A sound signal will travel through the ear canal (outer and middle ear) and get translated in the cochlea (inner ear). The electrical signal is then transported from the auditory nerve to theCN. From there it is send to the SOC which projects to theIC through the lateral lemniscus. TheIC integrates information from theCN and SOC before projecting to the thalamus and the cortex. Through the relay centre of the thalamus, the medial geniculate nucleus, the signal is projected to the auditory cortex, where final processing happens [32]. A.4 Developed pipelines for fMRI 41

Expanding our knowledge of the underlying mechanisms of hearing has not advanced as much as in other brain regions. The lack of research using auditory fMRI is due to the inherent acoustic noise of working in the fMRI environment. Nowadays, several research groups have began studying auditory fMRI. Cheung et al. successfully demonstrated activation of the aforementioned major nuclei before reaching the auditory cortex [9]. In a later study by the same group, Lau et al. used BOLD fMRI to measure changes in the hemodynamic responses of the rats’ auditory subcortex during binaural stimulation with different interaural level differences [28]. It has also been demonstrated that humans and rats have common robust resting state brain networks and that rs-fMRI can be used as a translational tool when validating animal models of brain disorders [47]. The studies mentioned above as well as observations that rats have the largest auditory system relative to their brain size, have made rodent models extremely valuable in fMRI investigations of auditory function [15].

A.4 Developed pipelines for fMRI

In order to study the function of the brain as well as make observations and confirm or reject hypotheses, we must examine the acquired data. Images acquired during fMRI studies must be preprocessed and then analysed in a common framework in order for investigators to reach a conclusion. This has been quite a challenging task but over the years, several software packages have made their appearance due to the extensive need of neuroimaging processing in the academic community. Though there are some differences between these packages, the general structure is quite the same. The necessity of using all of these operations depends on the set of data we have at hand. In their book, Poldrack et al. describe in detail the steps that should be followed during fMRI data analysis [43].

1. Quality control During quality control, we must ensure that the data are not corrupted by artefacts in such a degree that makes processing impossible. It is always best to control the quality after every preprocessing step.

2. Brain segmentation Brain extraction (or skull striping) is typically the first processing step of anatomical data [2, 43]. During skull stripping, any background and non-brain tissue is removed, leaving behind the brain tissue [38].

3. Bias field correction Intensity inhomogeneities are a common artefact present in most fMRI data, but they 42 State of the art

Figure A.4: Common processing stream as described by [43]

are especially pronounced in animal studies due to strong magnetic fields utilized [2]. Although high intensity scanners provide better resolution and sharper details, a trade-off high spatial intensity variation exists [38]. In literature, the term "bias field" is also used to refer to this artefact. In order to avoid any problems in subsequent steps of the pipeline, correction of these non-uniformities is required.

4. Motion correction Subject motion presents another difficulty in processing fMRI data. Even in the case of anaesthetized rats, physiological motion of the brain still exists, which requires A.4 Developed pipelines for fMRI 43

necessary steps to be taken to compensate for any motion artefacts. In literature, we often encounter this step as realignment. Head motion is assumed to be a rigid body process that can be described by 6 parameters, 3 translations and 3 rotations [8]. Resting state correlations can be altered because of motion related to head movements and physiological processes.

5. Distortion correction A known issue of EPI in fMRI studies is the appearance of geometric distortions caused by magnetic field inhomogeneities [44]. These distortions have the form of signal compression or expansion and as a result, affected voxels are represented in a wrong location and with incorrect intensity [21]. Inability to correct for these artefacts can compromise the process of registration. Distortion correction requires the calculation of a field-map that describes the field variation. This is accomplished by acquiring scans with opposing polarities of the phase encoding direction [1].

6. Co-registration To permit localization of brain activation, functional data need to be registered to their individual high resolution structural images, which then need to be normalized to a standard template [38]. Intra-subject registration is necessary for matching the different image and voxel dimensions that exist between functional and anatomical data.

7. Spatial normalization Following co-registration, spatial normalization attempts to bring all subjects to a common space. Inter-subject brain variation in shape and size requires alignment into a common spatial framework, to enable regional correspondence during group analysis [43].

8. Spatial smoothing Reducing noise in the data is essential, so it is a common practice to use a filter for smoothing. Blurring of data reduces small variance and thermal noise without losing valid activation.

9. Temporal filtering This step is necessary for removing low-frequency noise.

10. Statistical modelling After processing and removing as much artefacts as possible, it is necessary to model the data as accurately as possible. Several models can be used in this step e.g. one can 44 State of the art

use univariate analysis, such as GLM, SCA or multivariate analysis, such as ICA or principal component analysis (PCA).

11. Statistical inference Finally, we make decisions based on our data, while accounting for uncertainty due to noise in the data.

Poldrack et al. took the above states of preprocessing and implemented a pipeline designed to process various scan protocols without the need of manual intervention. The basic steps of preprocessing, as described above, are performed providing sufficient information in terms of error and output reporting.

Figure A.5: The suggested pipeline described in figure A.4 has been implemented in a state-of-art, robust, automated routine that is currently being used for processing human data.

fMRIPrep is the first pipeline to combine methods from the most well-known processing packages such as FSL, ANTs, FreeSurfer and AFNI. While the pipeline itself could not be applied on the present study, due to the specific nature of the dataset, several ofthe implemented workflows were consulted. A.5 Statistical modelling 45

Strategies more specific to resting state analysis have also been suggested, to take special care of denoising at the single subject level. Bijsterbosch et al. summarises the most common ones in figure A.6.

Figure A.6: Noise reduction strategies specifically aimed at rs-fMRI. In addition to the conventional methods depicted on the top, more denoising-oriented methods need to be applied. Both global signal regression and low pass temporal filtering are becoming less used due to controversies about their effectiveness. ICA-based cleanup and nuisance regression are two of the most used methods in recent literature

By using the steps described by Poldrack et al., the state-of-art pipeline for human data, fmriprep [12], and adopting strategies proposed by Bijsterbosch et al., a preprocessing pipeline was designed and implemented for being applied on rodent data (see figure 2.2).

A.5 Statistical modelling

After completing the pre-processing routine, it is now possible to study the brain’s functional connectivity by using methods such as GLM, SCA and ICA. The methods described below have been used extensively in the literature and each one has its merits and shortcomings.

1. General linear model (GLM) A valuable tool in statistical analysis, GLM is used to describe the BOLD response of a voxel in terms of a linear combination of contributing factors, while also accounting for errors [14]. In its most basic form, GLM is described by the equation y=Xb + e were y represents the data (or dependent variable), X is the explanatory variable (or 46 State of the art

regressor), b represents the scaling parameter and e represents the residual errors [24]. The dependent variable y can be a single subject’s BOLD dataset or a set of functional connectivity maps in the case of group analysis. In the case of group analysis, the set of regressors take the form of a design matrix, which describes the relationships between subjects. GLM can be used to perform many types of comparisons and as such, it remains the most commonly used framework for group level analysis [51,7, 5].

2. Seed based correlation analysis (SCA) SCA is a hypothesis driven, univariate method, which examines the functional connectivity of a specific brain region, determined by the a priori definition ofaseed[6]. To accomplish this, the averaged timeseries of the seed region are correlated against all other brain voxels timeseries and a connectivity map is calculated. This map describes the functional connections of the seed region with any other areas of the brain [19, 30]. SCA has a straightforward implementation but it is evident that it presents restrictions in examining network connectivity, due to the need for manual definition of the area of interest. The information given by SCA is limited to the functional connectivity of the seed region and does not indicate patterns on a whole-brain scale. Additionally, dependence on the selection of the seed makes this method vulnerable to bias.

3. Independent component analysis (ICA) ICA is an exploratory, data driven method which permits examination of functional connectivity over the whole brain, without the need of a priori knowledge as required by SCA, and does not require an explicit temporal model as is the case with GLM. This method takes a multivariate signal and decomposes it into a set of maximally independent components, which are not required to be orthogonal or Gaussian. It is described by the equation X=AS where X represents the observed data (ie. resting state BOLD signal), S represents the unknown sources and A is a matrix of unknown, mixing coefficients. Analysis of the signal can be performed either in the temporalor the spatial domain. Following unmixing, the identified components are returned in the form of spatial maps and timeseries. For the present thesis, MELODIC tool from FSL was used for gICA, which implements the FastICA algorithm [22]. The sessions were temporally concatenated to look for spatial patterns [4]. While ICA is a more flexible method, it brings forward challenges such as what is the optimal numberof components one should look for. A.6 Previous research 47

A.6 Previous research

Currently, much research is taking place, focussing on the processing of fMRI data in order to get valuable information about the brain. Some groups attempt to improve pre- existing steps of the pipeline, some focus specifically on improving the identification of non-neural noise, and others create new toolboxes or even incorporating methods from other areas of computer science, such as machine learning and deep learning. In an early study of the rodent brain, Pawela et al. showed evidence of conservation of resting state fluctuations across mammalian species, using data driven and hypothesis driven techniques [41]. In a different study, comparable acquisition protocols were used, to evaluate rs-fMRI data from mice and rats, showing that the different outcome between the two species strongly relates to the number of components used during ICA[25]. Further on, Pan et al. improve human-rodent integration by discussing best practises for animal studies and differences in rs-fMRI application in rodents and in humans [40]. Attempts on utilising human-specific software for the animal model are also taking place. In his master thesis, [35] developed a routine to analyse rs-fMRI data on a longitudinal study on a rodent model prone to developing depression. He used a variety of MATLAB-based toolboxes such as SPM, GIFT and FNC. Zerbi et al. evaluated FIX for analysing mice rs-fMRI, concluding that it performed greatly in removing non-neural originated artefacts and facilitated automatic processing [52]. Bajic et al. previously showed a similar pipeline designed for identifying RSNs using independent component analysis [2]. In an attempt to circumvent the effects of anaesthesia on the brain, more studies involving awake rodents have start emerging. Becerra et al. examined resting state networks in awake rats, using high field fMRI and ICA[3]. Paasonen et al. studied the effect of 6 anaesthesia protocols on functional connectivity, compared to the functional connectivity of awake rats, showing that each anaesthesia protocol affected functional connectivity in a unique way [39]. Ma et al. successfully characterised specialization and integration of the rat brain, using rs-fMRI on awake rats. A novel, data-driven parcellation method was used for performing graph analysis, showing that the rat brain has a similar topological organisation to humans [31]. Taking advantage of technological improvements, inexpensive graphic processing units for parallel processing are being used by BROCCOLI software, to accommodate faster analysis [11]. Suk et al. propose a methodological combination of deep learning (deep-auto encoder) and state-space modelling (Hidden Markov model) in order to investigate functional dynamics in rs-fMRI and classify subjects with mild cognitive impairment [50]. In one of the newest studies in the field, convolutional neural networks are employed for resting state 48 State of the art network (RSN) classification [34]. Connectome convolutional neural networks (CCNNs) were shown to outperform traditional neural networks in correctly classifying amnestic mild cognitive impairment. A CCNN is a class of deep, feed-forward artificial neural network that is trained to make predictions from connectomes. Although utilising neural networks is at an early stage, it shows promising results in the area of connectome classification. References 49

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