UNIVERSIDADE DE LISBOA
FACULDADE DE CIÊNCIAS
DEPARTAMENTO DE FÍSICA
Mapping the maturation pattern of monkey brain connections
using diffusion MRI
Inês Mexia Rodrigues
Mestrado Integrado em Engenharia Biomédica e Biofísica
Perfil em Biofísica Médica e Fisiologia de Sistemas
Dissertação orientada por:
Dr. Tim Dyrby
Dr. Alexandre Andrade
Acknowledgements
First of all, I would like to thank my supervisor at the Danish Research Centre for Magnetic Reso-nance (DRCMR), Dr. Tim Dyrby, for the opportunity given and for the insightful comments given not only throughout the internship but also after the end of the project. His guidance and constant supervision helped me to complete the internship without any questions. It was a pleasure to work with him. I would also like to show my gratitude to Dr. Kasper Andersen and Mr. David Romascano for their patience and immense knowledge. I can’t fail to mention DRCMR, who accepted me and provided me housing through the internship.
Furthermore, I would like to acknowledge the crucial role of Professor Alexandre Andrade, who was constantly worried with my progression and gave me several relevant advices. I would also like to thank the Erasmus+ scholarship without which my stay in Copenhagen wouldn’t have been possible. Finally, I would like to thank my family and friends for the support and willingness whenever I needed. Aos fixolas, com quem passei 5 anos extraordin´arios e que me fizeram passar mais tempo em Lisboa do que no Barreiro. Trabalhos fort´ıssimos, passagens de ano e semanas acad´emicas no corac¸˜ao. Ao meu namorado, o meu maior suporte, que sempre tentou lutar contra o meu negativismo, e me faz a pessoa mais feliz do mundo todos os dias. `A minha irm˜a, com quem fac¸o as melhores sess˜oes fotogr´aficas e as maiores parvo´ıces. Aos meus av´os, que me mostram todos os dias que conseguimos arranjar sempre energia para atingir os nossos objetivos. Ao meu avˆo Rodrigues que, `a sua maneira, sempre mostrou orgulho nas suas queridas netas. Por fim, aos meus pais, dois grandes amigos que se esforc¸am todos os dias para dar uma vida espetacular `as suas princesas e que sempre acreditaram em mim, mesmo quando disse que ia estudar c´erebros de macacos.
Abstract
The human brain undergoes a complex process of development during childhood and adolescence. During this maturation process, microstructural changes can be examined in detail using neuroimag-ing techniques. In particular, diffusion magnetic resonance imagneuroimag-ing (MRI) uses the diffusion of water molecules to generate contrast in magnetic resonance (MR) images.
Although this non-invasive medical tool currently shows great promise, allowing different recon-struction methods to quantify the neural environment, their limitations do not allow it to be fully characterized. With this said, it is relevant to incorporate in one analysis different reconstruction algorithms in order to cross different informations and draw more clarifying conclusions.
Therefore, the aim of this project is to investigate tissue microstructure during the maturation process in the Vervet monkey brains from childhood to adulthood. For that, DTI (Diffusion Tensor Imaging) and NODDI (Neurite Orientation Dispersion and Density Imaging) models were studied in ex vivo data from 25 healthy monkeys. The first model uses the orientation of white matter (WM) fibres in order to estimate diffusion related parameters as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). The latter tries to fit a MR signal to biological structures, estimating the morphology and volume of neurites. Regions of interest (ROIs) were man-ually drawn and tractography was performed in order to visualize and quantify maturation changes in specific pathways through the analysis of these parameters. Besides collecting valuable information on the microstructure features that changed during maturation, information about myelination was possible to acquire due to volume analysis.
Volume increase is shown in all fibres and global WM, demonstrating extended myelin production until early adulthood. On the other hand, volume increase in grey matter (GM) was not so abrupt, co-herent with its lack of myelin sheets. Regarding changes in the brain’s microstructure, the WM fibres get a preferred orientation, reflected on the increasing anisotropy. At the same time, fibres’ structures get denser, which might lead to a decreased rate of diffusivity. Both the intracellular and the isotropic volumes increase. Using the general linear model (GLM) from SPSS, it was shown that these mi-crostructure features appear to vary more during the first 12 months of age (5 years in humans). It was also demonstrated that anisotropy can be defined by intracellular volume and dispersion, con-cluding that NODDI and DTI should be used together as they complement each other. Lastly, it was discussed that myelination and microstructure processes might occur in the same timescale as their trends are similar.
These findings enrich the understanding of the maturation on the brain and its changing features from childhood to adulthood in monkeys.
Resumo
O c´erebro humano ´e submetido a um processo complexo de desenvolvimento durante a infˆancia e adolescˆencia. Durante este processo de maturac¸˜ao, mudanc¸as microestruturais podem ser exami-nadas em detalhe utilizando t´ecnicas de neuroimagiologia. Em particular, a imagem por ressonˆancia magn´etica (IRM) ponderada em difus˜ao usa a difus˜ao das mol´eculas de ´agua para gerar contraste nas imagens de ressonˆancia magn´etica (RM). Este m´etodo distingue, no caso dos tecidos neuron-ais, difus˜ao com obst´aculos e difus˜ao restrita. A primeira tem um padr˜ao de distribuic¸˜ao gaussiano e caracteriza a ´agua no espac¸o extracelular. O segundo tipo de difus˜ao, que j´a n˜ao tem um padr˜ao gaus-siano, caracteriza a ´agua no espac¸o intracelular. ´E tamb´em poss´ıvel distinguir difus˜ao anisotr´opica e isotr´opica. A primeira ´e caracterizada por uma difus˜ao com uma orientac¸˜ao preferida e a segunda por uma mesma difus˜ao em todas as direc¸˜oes. Por exemplo, a difus˜ao na substˆancia branca ´e mais anisotr´opica do que a difus˜ao na substˆancia cinzenta, e a difus˜ao no l´ıquido cefalorraquidiano ´e isotr´opica.
Apesar desta ferramenta m´edica n˜ao invasiva ser atualmente promissora, permitindo que diferentes m´etodos de reconstruc¸˜ao quantifiquem o tecido neuronal, as suas limitac¸˜oes n˜ao permitem uma caracterizac¸˜ao completa do mesmo. Dito isto, ´e relevante incorporar numa ´unica an´alise diferentes algoritmos de reconstruc¸˜ao de forma a cruzar v´arias informac¸˜oes e elaborar conclus˜oes mais clarifi-cantes.
Desta forma, o objetivo deste projeto ´e investigar a microestrutura dos tecidos durante o processo de maturac¸˜ao em c´erebros de macacos Vervet desde a infˆancia at´e `a idade adulta. De forma a mel-hor interpretar a IRM ponderada em difus˜ao, foram utilizados modelos matem´aticos que estimam parˆametros relacionados com a microestrutura dos tecidos biol´ogicos.
O modelo DTI modela um ´unico processo de difus˜ao com distribuic¸˜ao gaussiana, expressando assim apenas a difus˜ao extracelular. Este modelo utiliza um tensor que providencia uma caracterizac¸˜ao tridimensional da difus˜ao, definindo a sua magnitude, grau de anisotropia e orientac¸˜ao. De forma a facilitar a interpretac¸˜ao do modelo, v´arios parˆametros foram criados a partir do tensor: a anisotropia fracional, que mede qu˜ao preferencial ´e a direc¸˜ao do tensor, a difusibilidade m´edia, que quantifica a m´edia da taxa de difus˜ao, a difusibilidade axial, que mede a taxa de difus˜ao ao longo da direc¸˜ao prin-cipal de difus˜ao, e a difusibilidade radial, que mede a taxa de difus˜ao m´edia das outras duas direc¸˜oes de difus˜ao. No entanto, existem algumas limitac¸˜oes relativamente a este modelo. Os parˆametros s˜ao calculados para cada voxel numa escala milim´etrica, muito diferente da escala microm´etrica dos ax´onios. Como tal, ´e preciso precauc¸˜ao quando s˜ao desenvolvidas hip´oteses a partir destes parˆametros. Al´em disso, o modelo recupera apenas uma orientac¸˜ao de um conjunto de fibras num voxel, n˜ao fornecendo uma informac¸˜ao correta quando se est´a numa situac¸˜ao em que as fibras se cruzam. Devido a esta ´ultima limitac¸˜ao do modelo, foram criados outros que consideram diversos processos de difus˜ao num mesmo voxel. Estes modelos reconstroem a distribuic¸˜ao da orientac¸˜ao das fibras (DOF). No projeto em quest˜ao foi utilizada a desconvoluc¸˜ao esf´erica, que recupera a DOF a partir da desconvoluc¸˜ao do sinal de IRM ponderada em difus˜ao com o respetivo sinal de cada populac¸˜ao de fibras.
Apesar do modelo DTI fornecer dados relevantes em relac¸˜ao `a quantificac¸˜ao do desenvolvimento cerebral, ´e um modelo limitado, tal como mencionado, faltando especificidade para distinguir carac-ter´ısticas particulares microestruturais dos tecidos. Dito isto, foi tamb´em utilizado o modelo NODDI, que utiliza o processo de difus˜ao para estimar a morfologia das neurites. Resumidamente, o sinal medido na IRM ponderada em difus˜ao pode ser definido pela soma da contribuic¸˜ao dos sinais de diferentes compartimentos. Contrariamente ao modelo DTI, o NODDI diferencia o espac¸o intra e extracelular, a base para quantificar a dispers˜ao e densidade das neurites. Para isso, diferentes valores bs˜ao adquiridos para garantir contraste dos v´arios compartimentos. A partir da an´alise do modelo NODDI, dois parˆametros s˜ao obtidos: a densidade neur´ıtica e o ´ındice de dispers˜ao da orientac¸˜ao das neurites. Apesar de existir uma boa correspondˆencia entre os parˆametros estimados e a histologia, os resultados requerem procedimentos muito lentos de forma a ajustar o modelo `as medidas. Como tal, foi usado o m´etodo AMICO (Accelerated Microstructure Imaging via Convex Optimization) que ajusta o modelo de forma linear, fornecendo resultados mais r´apidos com igual rigor. Este m´etodo calcula o ´ındice de dispers˜ao da orientac¸˜ao das neurites, a frac¸˜ao de volume intracelular e a frac¸˜ao de volume isotr´opica.
De forma a quantificar a maturac¸˜ao do c´erebro em fibras espec´ıficas, ´e utilizada uma t´ecnica, de nome tratografia, que permite visualizar representac¸˜oes tridimensionais dos tratos neuronais. Para este estudo, foram escolhidos o giro cingulado, o trato corticoespinhal, o espl´enio, joelho e corpo do corpo caloso, o fasc´ıculo occipitofrontal inferior, e os fasc´ıculos longitudinais superior e inferior.
Para al´em destas fibras, foram estudadas duas estruturas da substˆancia cinzenta, o n´ucleo caudado e o putamen. Primeiro, as regi˜oes de interesse foram desenhadas e tratografia probabil´ıstica foi aplicada, obtendo as regi˜oes envolvidas no projeto. Estas foram otimizadas, escolhendo apenas as secc¸˜oes mais alinhadas e densas. Nestas foi ent˜ao feita a an´alise dos modelos DTI e NODDI. Para al´em da obtenc¸˜ao de informac¸˜ao acerca das mudanc¸as na microestrutura do c´erebro, foi tamb´em poss´ıvel adquirir informac¸˜oes sobre o processo de mielinizac¸˜ao a partir de medidas de volume das regi˜oes. Os resultados mostram um aumento de volume em todas as fibras e substˆancia branca em geral, demonstrando que a produc¸˜ao de mielina se estende, pelo menos, at´e ao in´ıcio da idade adulta. Por outro lado, o aumento de volume na substˆancia cinzenta n˜ao foi t˜ao abrupto, o que ´e coerente com a sua falta de mielina.
Em relac¸˜ao `as mudanc¸as na microestrutura do c´erebro, as fibras comec¸am a ficar cada vez mais direcionadas, o que se reflete no aumento da anisotropia. Ao mesmo tempo, as fibras ficam mais densas e a difusibilidade diminui. Tanto o volume intracelular como o volume isotr´opico aumentam. Usando o modelo geral linear do programa SPSS, foi poss´ıvel investigar em espec´ıfico a variac¸˜ao dos parˆametros dos modelos. Foi demonstrado que estas caracter´ısticas da microestrutura cerebral variam mais nos primeiros 12 meses de vida, o que corresponde a 5 anos para um humano. Foi tamb´em confirmado que a anisotropia pode ser definida pela dispers˜ao das fibras e o seu volume intracelular. Por ´ultimo, foi discutida a possibilidade dos processos de mielinizac¸˜ao e microestrutura ocorrerem ao mesmo tempo uma vez que as variac¸˜oes s˜ao semelhantes.
Estas conclus˜oes enriquecem o conhecimento acerca da maturac¸˜ao do c´erebro e as suas carac-ter´ısticas desde rec´em-nascidos at´e `a idade adulta em macacos. O estudo n˜ao s´o mostrou como a mielinizac¸˜ao e microestrutura variam mas tamb´em comparou estes resultados com estudos em hu-manos. Foi demonstrado que os c´erebros de macacos parecem maturar mais r´apido, atingido o pico por volta dos dois anos de idade, quando est˜ao na adolescˆencia, enquanto os c´erebros humanos con-tinuam a sofrer alterac¸˜oes at´e ao in´ıcio da idade adulta. No entanto, os processos apresentam o mesmo comportamento, indicando que podem ser semelhantes entre as esp´ecies.
Um detalhe importante deste projeto ´e a alta qualidade dos dados de IRM devido `a aquisic¸˜ao de imagens de dados ex vivo. Para al´em disso, ´e poss´ıvel depois fazer testes histol´ogicos nos mesmos dados, uma grande vantagem para uma futura an´alise mais completa. Seria tamb´em importante estu-dar a espessura do c´ortex e comparar a sua variac¸˜ao com as variac¸˜oes na microestrutura, de forma a aprofundar o conhecimento acerca da relac¸˜ao entre substˆancia branca e cinzenta.
Contents
1 Introduction 1 1.1 Diffusion MRI . . . 1 1.1.1 Principles of Diffusion . . . 1 1.1.2 Imaging . . . 1 1.1.3 Reconstruction Algorithms . . . 3 1.1.4 Tractography . . . 8 1.2 Brain Maturation . . . 9 1.2.1 Studies of Maturation . . . 91.2.2 Maturation Analysis using DTI . . . 10
1.2.3 Maturation Analysis using NODDI . . . 11
1.2.4 Tractography Implementation . . . 11 1.2.5 Goals . . . 12 2 Methods 13 2.1 Subjects . . . 13 2.2 Tissue Fixation . . . 13 2.3 Image Acquisition . . . 13 2.4 DWI Processing . . . 14 2.4.1 FLIRT . . . 14
2.4.2 DTI parameters extraction . . . 15
2.4.3 AMICO parameters extraction . . . 15
2.5 Tractography . . . 16
2.5.1 Fixel-based analysis . . . 16
2.5.2 WM fibres . . . 16
2.5.3 Fibre tract extraction and optimisation . . . 16
2.6 GM Structures . . . 18
2.7 Volume Measurements . . . 19
3 Statistical Analysis and Results 20 3.1 Age effects on volume . . . 20
3.2 Age effects on DTI/NODDI parameters . . . 22
3.3 Correlation between parameters . . . 27
3.4 Influence of volume on DTI/NODDI parameters . . . 29
4 Discussion 33
5 Conclusions 35
A Figures 44
List of Figures
1.1 PGSE sequence introduced by Stejskal and Tanner. The time between the onset of the two gradient pulses is represented by ∆ and the gradient pulse duration by δ . G is the amplitude of the magnetic field gradients [8]. . . 2 1.2 Abstract visualization of the diffusion tensor. The principal axes of diffusion are given
by the eigenvectors, ˆε1, ˆε2 and ˆε3, and their respective diffusion displacements over a
given diffusion time given by the square root of the eigenvalues λ1, λ2and λ3[8]. . . 4
1.3 The challenge in crossing fibres. With a single fibre population, the diffusion tensor is an ellipsoid and FA is high. With two fibres crossing, the diffusion tensor becomes more spherical resulting in a reduced FA [1]. . . 5 1.4 Reconstruction method calculates the dispersion and intracellular volume fraction
con-tribution at each voxel by expressing the DWI signal as a linear combination of these parameters. . . 7 1.5 a) Abstract representation of the diffusion tensor at each voxel; b) Deterministic
tractog-raphy; c) Likelihood map of probabilistic tractography [30]. . . 8 1.6 Three-dimensional reconstruction of the CC fibre tracts of a (a) rhesus monkey and a (b)
human, overlaid with an axial view of a T1-weighted image. Adapted from [45]. . . 9 1.7 Fractional anisotropy maps obtained from a healthy male. It is possible to detect low FA
values inside the dashed circle. In fact, that region has an high FA value, being the low value caused by crossing fibres [59]. . . 10 1.8 (a) - Deterministic and (b) - probabilistic approaches to extract the cingulum. Adapted
from [88]. . . 11 2.1 A comprehensive processing pipeline of the different methods applied throughtout the
course of the project. (a) DWI images were registered in their correspondent T1 im-ages. (b) Masks and FOD maps of the brains were computed, as well as the DTI and NODDI parameters maps. (c) FOD images were registered in a template. (d) ROIs were drawn in template space in order to compute fibres of interest. (e) Warps estimated from registration were used to warp those fibres to each monkey’s DWI-T1 image. . . 14 2.2 Averaged coefficient of variation as function of the number of streamlines created by
probabilistic tractography. . . 17 2.3 (a) - Placed ROIs for tracking the body of CC and related (b) - extracted tract from
probabilistic tractography, both overlaid on a coronal direction FOD template map. The yellow ROI is the seed, from where the streamlines are generated, the blue ROIs are inclusive areas, where selected streamlines should go through, and the red ROI is an exclusive area, where selected streamlines should not go. . . 17 2.4 (a) - Placed ROIs for tracking the cingulum and related (b) - extracted tract from
proba-bilistic tractography, both overlaid on a sagittal direction FOD template map. The yellow ROI is the seed, from where the streamlines are generated, and the blue ROIs are inclu-sive areas, where selected streamlines should go through. . . 18 2.5 (a) - Placed ROIs for tracking the CST and related (b) - extracted tract from probabilistic
tractography, both overlaid on a coronal direction FOD template map. The yellow ROI is the seed, from where the streamlines are generated, and the blue ROIs are inclusive areas, where selected streamlines should go through. . . 18 2.6 (a) - mask of the body of CC and related (b) - optimised mask, both overlaid on a coronal
direction FOD template map. . . 18 3.1 Age related tracts’ volume effects. A significant quadratic trend was seen in all fibres:
the first 12 months of age showed volume increases whereas after 42 months decreases in volume were shown. . . 21
3.2 Age related GM structures’ volume effects. A significant quadratic trend was seen in the two regions: the first 12 months of age showed volume increases whereas after 42 months decreases in volume were shown. . . 21 3.3 Age related brain tissue volume effects. Total deep and cortical GM seemed to have
the same quadratic trend, with volume increase until 25 months, whereas WM volume seemed to continue until no less than 42 months. . . 21 3.4 Correlation between total WM volume and each WM fibre’s volume. All fibres showed
a strong linear relationship between the volumes. . . 22 3.5 Age related FA effects of the body of CC, cingulum, SLF 3 and IFOF. FA increases were
observed for all fibres. The rate of increase was initially high, then slowed down and slightly decreased after the 40 months mark. . . 24 3.6 Age related FA effects of the two GM structures of interest. Initially FA decrease and
further increase was observed on both putamen and caudate. . . 24 3.7 Age related MD effects of the body of CC, cingulum, SLF 3 and IFOF. These showed MD
decreases on the last three fibres until the 40 months mark, when MD slightly increased. For the body of CC, a small increase was shown throughout the age interval in study. . . 24 3.8 Age related MD effects of the two GM structures of interest. Putamen showed decreased
MD whereas caudate maintained this parameter constant as age progressed. . . 25 3.9 Age related OD effects of the body of CC, cingulum, SLF 3 and IFOF. The first two
showed decreased OD through the age spectrum, SLF 3 was constant and IFOF showed opposite behaviours in its hemispheres. . . 25 3.10 Age related OD effects of the two GM structures of interest. Putamen showed increases
of this parameter whereas caudate maintained it constant. . . 25 3.11 Age related ICVF effects of the body of CC, cingulum, SLF 3 and IFOF. All fibres
showed increasing ICVF with increasing age followed by a minor decrease after the 40 months. . . 25 3.12 Age related ICVF effects of the two GM structures of interest. Putamen showed increases
of this parameter whereas caudate maintained it constant. . . 26 3.13 Age related ISOVF effects of the body of CC, cingulum, SLF 3 and IFOF. The four fibres
showed increasing ISOVF as the age increases. . . 26 3.14 Age related ISOVF effects of putamen and caudate, both showing increasing ISOVF as
the age increases. . . 26 3.15 Correlation between FA and OD for the body of CC, cingulum, SLF 3 and IFOF. The
four fibres showed a negative linear relationship between the parameters. It was observed a strong relationship in the body of CC and cingulum and a moderate one in SLF 3 and IFOF. . . 27 3.16 Correlation between FA and OD for the two GM structures of interest. It was observed a
strong negative linear relationship between the parameters in both. . . 27 3.17 Correlation between FA and ICVF for the body of CC, cingulum, SLF 3 and IFOF.
Cingulum and IFOF showed a weak relationship between the parameters whereas the body of CC and SLF 3 showed a moderate relationship. . . 29 3.18 Correlation between FA and ICVF for GM structures. The two regions of interest showed
a weak linear relationship between the parameters. . . 29 3.19 Correlation between volume and FA for the body of CC, cingulum, SLF 3 and IFOF. The
first showed a moderate linear relationship between volume and FA, unlike cingulum and SLF 3, which relationship between these variables was weak. IFOF showed a similiar behaviour as the body of CC and a big divergence between hemispheres. . . 29 3.20 Correlation between volume and FA for the two GM structures of interest. In both, a
3.21 Correlation between volume and MD for the body of CC, cingulum, SLF 3 and IFOF. Apart from the body of CC, that had a weak linear relationship between these variables, the other fibres showed a moderate negative linear trend. . . 30 3.22 Correlation between volume and MD for the two GM structures of interest. In both, a
moderate relationship between these variables was observed. . . 30 3.23 Correlation between volume and OD for the body of CC, cingulum, SLF 3 and IFOF.
Both the body of CC and IFOF had a weak relationship between the variables whereas cingulum and SLF 3 had a moderate one. It was possible to distinguish the two hemi-spheres in all fibres with significant hemispheric differences, specially in IFOF. . . 30 3.24 Correlation between volume and OD for the two GM structures of interest. Putamen
seemed to have a stronger positive relationship between the parameters than caudate. . . 31 3.25 Correlation between volume and ICVF for the body of CC, cingulum, SLF 3 and IFOF.
All the fibres showed a strong relationship between the variables. . . 31 3.26 Correlation between volume and ICVF for the two GM structures of interest. Both
showed a moderate relationship between the variables. . . 31 3.27 Correlation between volume and ISOVF for the body of CC, cingulum, SLF 3 and IFOF.
All fibres showed a moderate to strong relationship between the variables. Moreover, IFOF showed a big discriptancy between hemispheres. . . 31 3.28 Correlation between volume and ISOVF for the two GM structures of interest. The two
of them showed a moderate relationship between variables. . . 32 A.1 Age related tracts’ volume effects. A significant quadratic trend was seen in all ten fibres:
the first 12 months of age showed volume increases whereas after 42 months decreases in volume were shown. . . 44 A.2 Correlation between total WM volume and each WM fibre’s volume. All fibres showed
a strong linear relationship between the volumes. . . 44 A.3 Age related FA effects of the ten WM fibres. FA increases were observed for all fibres.
The rate of increase was initially high, then slowed down and slightly decreased after the 40 months. . . 45 A.4 Age related MD effects of the ten WM fibres. MD showed age-related decreases and
followed increases for most tracts except for the body of CC, genu and CST, where MD seemed to increase. . . 45 A.5 Age related OD effects regarding the ten WM tracts in study. OD was not very consistent
as it showed decreases in the body of CC, splenium, cingulum, ILF, SLF 2 and in the right hemisphere of IFOF, increases in the left hemisphere of CST and IFOF, and constant behaviour in the genu, SLF 3 and right hemisphere of CST. . . 46 A.6 Age related ICVF effects measured on WM tracts. ICVF showed increases with ageing
followed by a minor decrease after the 40 months in almost all WM tracts, with only CST demonstrating a decrease. . . 46 A.7 Age related effects of ISOVF. All the WM fibres in study showed increases in ISOVF in
all fibres, with the exception of splenium, where a decrease was observed. . . 47 A.8 Correlation between FA and OD for the ten WM fibres in study, where they all showed
a negative linear relationship between the parameters. Almost all fibres showed a strong relationship, with the exceptions of CST and SLF 2, which showed a moderate relation, and SLF 1, SLF 3 and IFOF, that had a weak relation between FA and OD. . . 47 A.9 Correlation between FA and ICVF. Besides SLF 1, all the ten fibres showed a linear
relationship between the parameters. The exceptional fibre showed a negative linear relationship. It was observed a strong relation in SLF 2, genu and splenium, a moderate one in the body of CC, SLF 1, SLF 3 and ILF, and a weak one in CST, cingulum and IFOF. 48
A.10 Correlation between volume and FA for all the ten fibres in study. Almost all of them showed a linear relationship between the variables. Although it was observed a weak relationship for most the fibres, SLF 2, the body of CC, splenium and IFOF presented a moderate linear relationship. For the left hemisphere of ILF and right hemisphere of SLF 1, a negative linear relationship was shown. Moreover, for all fibres with significant hemispheric differences, the hemispheres had very much distinguishable volumes for the same FA values, specially IFOF and ILF. . . 48 A.11 Correlation between volume and MD for all the ten fibres in study. Excepting for the
body of CC and genu, all of the fibres showed a negative linear relationship between the variables. Moreover, for all fibres with significant hemispheric differences, the hemi-spheres had very much distinguishable volumes for the same MD values, specially ILF and SLF 2. . . 49 A.12 Correlation between volume and OD for all the ten fibres in study. Most fibres showed a
linear relationship between thd variables. The body of CC, splenium and SLF 2 showed a negative linear relationship and, in IFOF, a constant trend was signalized. The discrep-ancy between right and left hemispheric volumes in some fibres continued to occur. The difference was very evident in IFOF. . . 49 A.13 Correlation between volume and ICVF for all the ten fibres in study. All fibres showed
a linear relationship between the variables. Once more, it was noticed a discrepancy between right and left hemispheres. . . 50 A.14 Correlation between volume and ISOVF for all the ten fibres in study. It was observed
a linear relationship between the variables, as for the exception of splenium, showing a negative trend. The discrepancy between right and left hemispheres continued to occur. . 50
List of Abbreviations
AD - Axial Diffusivity
ADC - Apparent Diffusion Coefficient
AMICO - Accelerated Microstructure Imaging via Convex Optimization b0 - Non-diffusion weighted image
B0 - Main magnetic field strength CC - Corpus Callosum
CSF - Cerebrospinal Fluid CST - Corticospinal Tract CV - Coefficient of Variation
dMRI - Diffusion Magnetic Resonance Imaging DOD - Diffusion Orientation Distribution DSI - Diffusion Spectrum Imaging DT - Diffusion Tensor
DTI - Diffusion Tensor Imaging DWI - Diffusion-Weighted Imaging FA - Fractional Anisotropy
FBA - Fixel-Based Analysis
FLIRT - FMRIB’s Linear Image Registration Tool FOD - Fibre Orientation Distribution
FSL - FMRIB Software Library GM - Grey Matter
ICVF - Intracellular Volume Fraction IFOF - Inferior Fronto-Occipital Fasciculus ILF - Inferior Longitudinal Fasciculus ISOVF - Isotropic Volume Fraction MD - Mean Diffusivity
MR - Magnetic Resonance
MRI - Magnetic Resonance Imaging NDI - Neurite Density Index
NODDI - Neurite Orientation Dispersion and Density Imaging ODI - Orientation Dispersion Index
PAS - Persistent Angular Structure PFA - Paraformaldehyde
PGSE - Pulsed-Gradient Spin-Echo QBI - QBall Imaging
RD - Radial Diffusivity RF - Radiofrequency ROI - Region Of Interest
SLF - Superior Longitudinal Fasciculus SD - Spherical Deconvolution
SDM - Signal Decay Metric TR - Time of Repetition WM - White Matter
1
Introduction
The human brain undergoes a period of development after birth, continuing to grow and specialize until adulthood. Many psychiatric diseases arise during that time, being relevant to study the processes by which normal brain maturation goes through so as to understand the causes of abnormal development.
1.1 Diffusion MRI
Since diffusion magnetic ressonance imaging (dMRI) techniques are sensitive to microstructural tissue changes, they play an important role in understanding human brain development. In particular, DTI (Dif-fusion Tensor Imaging) and NODDI (Neurite Orientation Dispersion and Density Imaging) are methods that provide insights on the characteristics of the different biological tissues.
Moreover, using a method called tractography, it is possible to visualise and quantify the microstructural changes related to specific pathways.
1.1.1 Principles of Diffusion
Diffusion is a mass transport process in which particles flow from regions of high concentration to regions of low concentration. Einstein described the motion of particles undergoing diffusion, introducing an explicit relationship between their mean-squared displacement and the diffusion coefficient, D (m2· s−1), given by
hx2i = 2Dt
d (1)
where hx2i (m2) is the mean-squared displacement of the particle during a diffusion time t
d (s) [1].
Diffusion in biological tissues
Two thirds of the human body consists of water. Considering that the water molecules are in constant exchange between the intracellular and extracellular environments, interacting with various tissue ele-ments, the diffusion can not be considered free, as required to apply equation 1.
Diffusion MRI distinguishes, in the case of neuronal tissues, hindered and restricted diffusion [2]. Hin-dered diffusion characterizes the water in the extracellular space. It defines the diffusion of water with a Gaussian displacement pattern where the diffusion coefficient is reduced. Therefore, D is replaced by the apparent diffusion coefficient (ADC) for each measured direction, that can be defined using D and adding a parameter that reflects the degree to which the diffusion is slower relative to free water. Re-stricted diffusion describes the water in the intracellular space. It refers to the diffusion of water within an enclosed compartment, where its displacement is characterized by a non-Gaussian distribution, not obeying equation (1).
1.1.2 Imaging
MRI is a noninvasive medical tool that can provide information about the structure and function of any part of the body. Specifically in the brain, it distinguishes white matter (WM) and grey matter (GM), identifying major WM structures. However, most WM appears homogeneous as MRI does not provide enough contrast in order to distinguish the myriad of tracts that form the WM.
With this said, an imaging technique capable of investigating the microstructure of the WM, named dMRI, was developed. It uses the diffusion of water molecules to generate contrast in magnetic resso-nance (MR) images, providing important information about tissue microstructure [3]. During the last decade, diffusion-weighted imaging (DWI) has been continuously improving, being nowadays a routine clinical application in ischemia [4] and other pathologies. In this section, the principles and methodology of this technique will be reviewed.
Measurement of diffusion
To produce a typical image, an MRI scanner aligns the hydrogen protons in water molecules to the magnetic field of the equipment. Then, the excitation of the nuclei occurs with a 90 degree radiofre-quency (RF) pulse that rotates the magnetization vector into the plane normal to the main magnetic field. This is a state of disequilibrium and, while returning to equilibrium, the proton releases energy received from the RF pulse and its recovery time will be characteristic for the tissue it is part of. The relaxation can be described with two time constants: T1 and T2. The first one is the time required for a certain percentage of the tissue nuclei to recover the z component of the magnetization. The second accounts for the lost of the transverse magnetization. Moreover, by varying the excitation and dephasing of the nuclei, it is possible to measure other tissue properties such as the local change in oxygenation (functional MRI) or the diffusion of water molecules (diffusion MRI).
As proposed by Edwin Hahn [5], and later by Carr and Purcell [6], the decay of transverse magnetization is caused by molecular motion and local inhomogeneities. The latter decreases T2 relaxation times, then obtaining a T2* relaxation, worsening the diffusion experiments, since T2* signals typically decay faster compared to the needed diffusion time. Therefore, the spin echo sequence, with a 180 degree RF pulse following a 90 degree pulse, was used as a means to negate the effects of local inhomogeneites. The diffu-sion encoding is based on the formulation of Stejskal and Tanner [7], that introduced the pulsed-gradient spin-echo (PGSE) sequence. This sequence consists in applying two identical diffusion-weighted gradi-ent pulses inserted before and after the refocusing pulse of a convgradi-entional spin-echo sequence (Figure 1.1).
Figure 1.1: PGSE sequence introduced by Stejskal and Tanner. The time between the onset of the two gradient pulses is represented by ∆ and the gradient pulse duration by δ . G is the amplitude of the magnetic field gradients [8].
The first diffusion gradient induces an additional phase change and the second gradient reverses that change. Consequently, if molecules do not displace during the diffusion time, then the second gradient pulse will reverse the first phase change, thus there will be no signal loss. However, if the molecules have displaced, then there is a phase loss corresponding to their displacement. This phase dispersion characterizes the degree of diffusion weighting, the often called b-value (s · m−2), which, for the PGSE experiment, it is given by the expression:
b= γ2G2δ2 ∆ −δ 3 (2)
where γ (s−1· T−1) is the gyromagnetic ratio of the nucleus, G (T · m−1) the amplitude of the magnetic field gradients, δ (s) their duration, and ∆ (s) the time interval between them [9]. It is also relevant to define the q-value (1/m), given by
q= γGδ (3)
Diffusion Weighted Imaging (DWI)
When the displacement of water behaves according to equation (1), the attenuation of the MR signal due to diffusion is a exponential signal decay expressed as
S
S0 = exp(−b.D) (4)
where S and S0are the signal intensities measured in the presence and absence of the diffusion gradients,
respectively.
Thus, it is possible to determine D along one direction in each voxel of the image [9]. However, depend-ing on the tissue, it might be necessary to replace D with ADC in order to better fit the signal as stated in section 1.1.1. In neuronal tissues, if the water diffuses equally in all directions, the ADC will be the same regardless of direction, suggesting an isotropic diffusion. However, if the water molecules have the tendency to diffuse preferentially in certain directions, the ADC will vary depending on the mea-sured direction, and this is called anisotropic diffusion. Diffusion in WM tends to be more directionally dependent than diffusion in GM and diffusion in the cerebrospinal fluid (CSF) is isotropic [10].
1.1.3 Reconstruction Algorithms
Diffusion MRI allows the analysis of the properties of water diffusion. Mathematical modelling becomes useful when interpreting the diffusion weighted signal. The reconstruction algorithms model the water displacement distribution in order to determine the principal WM fibre direction. They compute specific parameters that offer insights into the microstructure of the biological tissues and that are used in fibre tracking.
For the aim of this project, it is relevant to further explain the DTI model, that models a unique diffusion process at each voxel, and about the multi-fibre models that take into account various diffusion processes. Moreover, it is important to make a reference to the NODDI model, that uses the diffusion process to estimate neurite morphology.
Single fibre model
DTI provides the three-dimensional characterization of the water diffusion [11]. This method consid-ers gaussian diffusion that, as mentioned above, only expresses the extracellular diffusion. Moreover, it recognizes that water molecules diffuse differently along the tissues depending on its type, structure and presence of barriers, quantifying the diffusion as isotropic or anisotropic. Based on this premise, the dif-fusion process was modelled by a tensor, which can mathematically be represented by a 3 × 3 symmetric matrix able to define the magnitude, degree of anisotropy and orientation of the water diffusion, i.e.,
Dxx Dxy Dxz Dxy Dyy Dyz Dxz Dyz Dzz (5)
This diffusion tensor (DT) can be represented by an ellipsoid (see Figure 1.2), a surface showing the probability of the molecules’ displacement due to diffusion. The principal axes of diffusion are given by the eigenvectors’ ellipsoid, and their diffusion displacements by the square root of the eigenvalues, which represent the diffusivities along the principal axes of the tensor.
Figure 1.2: Abstract visualization of the diffusion tensor. The principal axes of diffusion are given by the eigenvectors, ˆε1, ˆε2
and ˆε3, and their respective diffusion displacements over a given diffusion time given by the square root of the eigenvalues λ1,
λ2and λ3[8].
Considering a three-dimensional version of equation (4), it is possible to estimate the tensor, which is given by S S0 = exp −bxxDxx −byyDyy −bzzDzz −2bxyDxy −2bxzDxz −2byzDyz (6) Note that, as the tensor is symmetric (i.e., Dxy= Dyx, Dxz= Dzx and Dyz= Dzy), there is only a
min-imum of six diffusion-weighted images required for estimating the elements of the tensor (plus one non-diffusion-weighted image) [8]. Nevertheless, if time permits, acquiring more images is beneficial since it increases the precision of the experiment. The optimal number of sampling orientations is 30 [12], wherein fewer samples introduce variations in estimates of MD. Naturally, in a clinical setting where it is necessary to scan patients fast, it may be impractical.
The DT, however, does not provide an easy interpretation of the data. In order to improve its visualiza-tion, simpler scalar maps are created from the DT map. There are four common properties that can be inferred from DTI analysis: the anisotropy of the diffusion tensor, i.e., its sharpness, the mean diffusivity (MD), the axial diffusivity (AD) and the radial diffusivity (RD).
The fractional anisotropy (FA) measures the directional preference of the diffusion and it is given by
FA= r 3 2 p(λ1− hλ i)2+ (λ2− hλ i)2+ (λ3− hλ i)2 q λ12+ λ22+ λ32 (7)
The FA index normalizes the variance of the three eigenvalues about their mean by the magnitude of the tensor as a whole. Thus, FA measures, at each voxel, how anisotropic is the tensor [13].
Having the FA map, it is possible to create a colour coded FA map. This map describes the tensor orientation from the major eigenvector direction. For that, the major eigenvector direction is assumed to be the one parallel to the direction of the tract and it is represented using an RGB colour. If the direction of the major eigenvector is left-right, it is represented by red; anterior-posterior fibres are shown in green and superior-inferior fibres in blue. The map is weighted by FA, which means the colour intensity of the voxel will depend on its FA value.
Another parameter is the average of the eigenvalues, also known as MD. It measures the average of the rate of diffusion at each voxel, infering about the diffusion type and the geometry of the tissue microstructure. It is given by
MD= hλ i = λ1+ λ2+ λ3
The AD reflects the diffusion rate along the principle direction of diffusion, thus it can be represented by
AD= λ1 (9)
Although it is not accepted by the scientific community as a whole, AD may be sensitive to axonal degeneration, decreasing in its presence [15].
The RD is the average diffusion of the second and third eigenvalues. Once more, it is not fully accepted but RD may increase due to myelin damage [16]. It is defined as
RD=λ2+ λ3
2 (10)
Nevertheless, there are some limitations in interpreting DTI data. The diffusion effects measured are averaged over a voxel in a milimetric scale, which is very different from the size of individual axons, measured in a micrometric scale. With this said, making assumptions based on DTI parameters needs a fair amount of precaution. For instance, FA is often associated with WM integrity. However, in addition to changes in myelination, changes in FA can be related with cell death or increase in extracellular or intracellular water.
Another limitation of DTI is that it can only recover a single fibre orientation in each voxel. If there are two or more fibres crossing in a voxel, this technique will compute a tensor with an averaged direction of the fibres. Moreover, the FA value will be misleading, assuming an absence of preferred direction when, in reality, the fibres could have two different preferred directions. An illustration of the problem can been seen in Figure 1.3. This becomes a significant constraint when trying to map brain areas with a complex internal structure [1].
Figure 1.3: The challenge in crossing fibres. With a single fibre population, the diffusion tensor is an ellipsoid and FA is high. With two fibres crossing, the diffusion tensor becomes more spherical resulting in a reduced FA [1].
Multiple fibres models
There are some approaches that try to overcome the limitations of DTI regarding crossing fibres. DTI is based on the assumption that within each voxel there is a single diffusing process that follows a Gaussian distribution. In the multi-tensor model, that is replaced by a mixture of n Gaussian densities, where n is the number of different fibre populations. However, it requires knowledge of the number of fibres crossing at each voxel [8].
With this said, other algorithms were created in order to reconstruct the fibre orientation distribution (FOD) without placing modelling constraints. For instance, the spherical deconvolution (SD) [17] method aims to recover the FOD by deconvolving the diffusion weighted signal with its response func-tion, which represents the signal of a single population of fibres. It uses one or more b-values acquired in many directions. Other methods reconstruct different functions of the distribution of the water displace-ment and use them as estimates of the FOD. For instance, diffusion spectrum imaging (DSI) [18] and QBall imaging (QBI) [19] reconstruct the diffusion orientation distribution (DOD). This function pro-vides the probability that a diffusing water molecule moves in a particular direction, whereas the FOD returns the fraction of fibres that move in that direction. An alternative approach is to sample the water
displacement distribution, given by the persistent angular structure (PAS) method [20]. NODDI
Although DTI may be helpful to quantify brain development, it remains limited, as mentioned above, lacking specificity to distinguish individual tissue microstructural characteristics [21]. With this said, Zhang et. al. [22] proposed a biophysical model of the brain microstructure called NODDI. The concept behind this technique relies on neurites, projections from the cell body of a neuron, and its morphology. In fact, insights of the brain’s architecture can be provided from quantifying the density and orientation distribution of axons and dendrites. Besides, neurites may be an indicator of brain development and ageing as the first is related with increases in the dispersion of neurite orientation distribution [23], and the second with decreases in the dendritic density [24].
Distinct from DTI, this model differentiates the intra and extracellular water diffusion, the basis for measuring neurite morphology via diffusion MRI. The measured signal is a weighted sum of signal contributions from the different compartment diffusivities and it can be defined as
A= (1 − viso)(vicAic+ (1 − vic)Aec) + visoAiso (11)
where Aicand vicare the normalized signal and volume fraction of the intracellular compartment, Aecthe
normalized signal of the extracellular compartment, and Aisoand viso the normalized signal and volume
fraction of the CSF compartment. To be noted that, for ex vivo experiments, a stationary compartment should be added [25]. Diffusion studies on fixed tissue [26] [27] demonstrated deviations from Gaussian displacements along the fibre direction that may happen due to stationary water trapped in glial cells and other small compartments.
Diffusion techniques can only resolve structures down to approximately 5 µm. Since axons have diam-eters lower than that, the intracellular space is modeled as a set of sticks (cylinders of zero radius). This space is characterized by the restricted diffusion and it can be written as
Aic=
ˆ
S
f(n)e−bdk(q·n)2dn (12)
where f (n) dn gives the probability of finding sticks along orientation n, and e−bdk(q·n)2 gives the signal
attenuation due to unhindered diffusion along a stick with diffusivity dkand orientation n, for given
gra-dient direction q and b-value b.
The extracellular space is characterized by hindered diffusion, hence it is modeled as anisotropic Gaus-sian diffusion, such that
logAec= −bqT ˆ S f(n)D(n) dn q (13)
where D(n) is a cylindrically symmetric tensor with the principal direction of diffusion n. Finally, the CSF compartment is modeled as isotropic Gaussian diffusion as in equation 4.
In order to estimate these different environments, different b-values must be acquired to ensure signal contrast from the different compartments. With this said, from equation (2), if one fixes the q-value and increases the diffusion time, the measured signal will have contributions from more different compart-ments. On the other hand, if one establishes a diffusion time and increases the q-value, the amplitude of the magnetic field gradients will also increase, meaning a bigger shift of the particles and, consequently, more dephasing and a faster signal loss. This implies less sensitivity to long distances, meaning that the measured signal will have contributions from less compartments.
Summarizing, NODDI uses different q-values and diffusion times in order to relate the diffusion distri-bution with the size of the microstructure. From that, the different volumes are estimated.
The vic represents the neurite density index (NDI) that is typically high in WM and low in GM. The
degree of dispersion of the fibre orientations at each voxel, ranging from 0 for oriented structures to 1 for structures with full dispersion, and it is typically high in GM and low in WM. The ODI is calculated after an iterative process of the measured signal, giving back the extent of orientation dispersion, κ. Then:
ODI= 2
πarctan(1/κ) (14)
Although there is a good correspondence between the estimated microstructure parameters (NDI, ODI) and the histology, the results require computationally slow non-linear procedures in order to fit the model to the measurements. With this said, the AMICO [28] method was introduced. This method uses a lin-earization to fit the model, providing faster results and preserving the veracity of the estimated parame-ters. It outputs the intracellular volume fraction (ICVF), the isotropic volume fraction (ISOVF), the OD and the stationary compartment.
Summarily, AMICO is based on the fact that the microstructural properties of the tissue can be expressed as a system of linear equations:
y= ΦNx+ η (15)
where y is the vector containing the dMRI signal acquired at each voxel, η accounts for the acquisition noise, ΦN is the linear operator, also called dictionary, that relates the tissue properties in study (fibre
density and dispersion) with the measurements, and x are the tissue properties contributions. The dictionary is divided into the anisotropic, isotropic and stationary compartments:
ΦN= [ΦtN|Φ i N|Φ
d
N] (16)
The first corresponds to the signal attenuation originated by a specific axon density and dispersion; in AMICO, 12 different values of dispersion and density are combined, with the Nt = 144 correspondent
signal profiles being estimated according to equations 12 and 13. The isotropic compartment is modeled as in equation 4 and the stationary compartment is constant since it is defined as trapped water, i.e., the signal will be always high due to non-existent diffusivity. With this said, the final dictionary ΦN size is
Nk= Nt+ Ni+ Nd= 146.
In order to know how much each micro-environment contributes to y, AMICO uses DTI to obtain the main direction of the fibres at each voxel and calculate the values of density and dispersion that compose the dictionary. Then, the contributions of each combination of a specific density and dispersion are computed by minimising y − ΦNx. An analogy is depicted in Figure 1.4.
Figure 1.4: Reconstruction method calculates the dispersion and intracellular volume fraction contribution at each voxel by expressing the DWI signal as a linear combination of these parameters.
Finally, the parameters can be computed for each voxel as
vic= ∑Nj=1t fjxtj ∑Nj=1t xtj (17) κ =∑ Nt j=1kjxtj ∑Nj=1t xtj (18)
viso= Ni
∑
j=1 xij (19) vdot= Nd∑
j=1 xdj (20)where fjand kjare the ICVF and κ, respectively, of the j-th position in ΦtN.
Even though NODDI/AMICO are valuable models on their own, and an insightful addition to DTI, these two techniques themselves are not fault free. Lampinen et al. [29] demonstrated that the NODDI premisses may not be valid for all circumstances. As stated before, NODDI tries to fit MRI signal to the different biological structures to infer specific microstructural features. However, these assumptions may not be valid, as it was shown that NODDI estimates higher levels of water in GM and gliomas than it should.
Nevertheless, NODDI can still be used if its limitiations are considered and the conclusions of the study are not entirely based on this model.
1.1.4 Tractography
Using data collected by the diffusion-weighted images, it is possible to visualize three-dimensional macroscopic representations of the neural tracts, a method called tractography. It assumes that the diffu-sion of water is the least hindered in the direction in which the axons are aligned, being its objective to find the pathways of those brain connections. This technique is performed by reconstructing streamlines parallel with the fibre orientation and, for that, there are two possible approaches, the deterministic and the probabilistic tractography.
Tracking algorithms
Deterministic tractography reconstructs the streamlines by starting at a seed point and following the local vector information. Since there is only one measurement per voxel, it will interpolate at the path end-point, until a stopping criteria is met. However, this approach does not have a mechanism for determining confidence in the next step and, consequently, no mechanism to estimate the uncertainty associated with each streamline.
The objective of probabilistic tractography is to develop a full representation of the uncertainty. It pro-duces a likelihood map of the diffusion path, tracking in regions of high uncertainty where deterministic tractography would stop. This map is calculated using the diffusion tensor and it shows the probability of the fibre being at different regions. By analysing these probabilities, one can infer the direction the fibre is more likely to take. Each streamline starts its path at a seed point and then it randomly selects a direction taking into account the previously mentioned probabilities. It takes a step in that direction and repeats until a stopping criteria is met. Then it restarts at the seed point and the process is repeated the times necessary until every voxel and its uncertainty are considered. Note that the time of computation can be a disadvantage of this technique [8].
Figure 1.5 shows the differences between the two tracking techniques.
Figure 1.5: a) Abstract representation of the diffusion tensor at each voxel; b) Deterministic tractography; c) Likelihood map of probabilistic tractography [30].
1.2 Brain Maturation
White matter fibres analysis using DTI to study maturation is well established. However, due to the introduction of NODDI, it is now relevant to use the two methods complementarily in order to map microstructural changes along brain connections from childhood to adulthood, which is still poorly doc-umented.
1.2.1 Studies of Maturation
As already mentioned, the human brain isn’t fully developed at birth. With stimulation and experience, there is an increase in the dendritic branching of neurons followed by synaptic ”pruning”, which leads to a more efficient set of connections. These changes are part of a process called maturation and they can interfere in the brain connectivity, resulting in pathologies [31]. In a clinical setting, quantitative information about the WM architecture can help identifying the cause of a disease and evaluating its status [32].
Histologic studies showed that this process continues through adult life [33] [34]. Moreover, four land-mark studies (two cross-sectional [35] [36], two longitudinal [37] [38]) demonstrated significant brain maturation during adolescence and early adulthood using conventional MRI. Results showed increases in WM and decreases in GM, consistent with myelination and decreased synaptic density.
The nonhuman primates’ brain has a myriad of similarities with the human brain, including the myeli-nation process and the number and density of cortical neurons [39]. Having that into account, monkeys are widely used in order to understand the mechanisms of the human brain since human studies will always be restricted for ethical and practical reasons. Although the process of myelination is faster in the monkey brain than in the human brain, the development itself goes through the same steps, meaning that the different characteristics over different periods of life are comparable [40]. The state of myelination in the human brain at the age of 3 months is similar to a monkey brain at birth, at 8-12 months it resembles a monkey brain with 3 months, and at 6-8 years it is similar to a 2 year old monkey brain. Therefore, studies of maturation using monkey models are common [41] [42] [43].
Overall, the anatomy of the monkey and human brain WM is qualitatively similar [44]. For instance, Hofer et al. [45] showed that the corpus callosum (CC) of primates and humans is very similar, as it can been seen in Figure 1.6.
Figure 1.6: Three-dimensional reconstruction of the CC fibre tracts of a (a) rhesus monkey and a (b) human, overlaid with an axial view of a T1-weighted image. Adapted from [45].
However, their proportions are different [46] [47]. Taking that into account, Zakszewsk et al. [48] cre-ated a WM atlas for the Rhesus monkey, confirming the similarities. This species, as well as the Vervet monkey and the Macaque monkey, are widely used models due to the identical brain structure and func-tion.
Monkey studies usually use an ex vivo diffusion-weighted MRI dataset. Following the animal death, fix-ation is carried out in order to prevent autolysis or putrefaction and to preserve the cells in the condition they were before the process of cell death starts. The fixation of the tissue can be ensured by perfusion or
immersion. The first injects the fixative into the blood flow via the heart while the second is executed by immersing the tissue within a volume of fixative. Another advantage of an ex vivo dataset is that does not add bias regarding physiological noise or head motion. Since these factors are no longer an issue, higher quality dMRI data can be acquired simply by increasing scan time. However, the T1 and T2 relaxation times decrease after fixation. T1 cannot be restored as it is related with the unrecoverable energy loss due to the interactions between nuclei and environment. For the other hand, as T2 is related with interactions between nuclei it can be restored when the fixative is removed.
With this said, postmortem DWI images reflect the celular structures without the bias introduced in in vivoscanning. Moreover, they provide a neuroanatomical environment as realistic as the one in conven-tional in vivo DWI, exceeding the typical resolution used in it.
1.2.2 Maturation Analysis using DTI
Recently, the understanding of human brain development has increased due to diffusion MRI techniques, that have provided results where is it possible to visualize widespread changes.
In particular, Mukherjee et al. [49] suggested that the scalar parameters (FA, MD, RD, AD) derived from DTI could be useful for studying the development of brain maturation. Schmithorst et al. [50] sustained that by relating WM maturation with increases in WM density, a measurement related to FA. Later, regions of maturational changes were identified during childhood and adolescence through the increase of FA and decrease of MD, RD and AD [51] [52] [53] [54]. These changes are thought to reflect, at least in part, progressive myelination. Longitudinal data was also processed [55] for commissural, projection, and association WM tracts, showing the same results. Changes of FA showed no pattern in the oldest age groups, suggesting differences between subjects in the progression of brain structure.
However, DTI is not sensitive to multiple fibre orientations within a voxel [56] [57] [58]. With this said, its parameters can give incorrect information, such as Jones et al. [59] stated (Figure 1.7).
Figure 1.7: Fractional anisotropy maps obtained from a healthy male. It is possible to detect low FA values inside the dashed circle. In fact, that region has an high FA value, being the low value caused by crossing fibres [59].
Behrens et al. [60] estimated that a third of the WM voxels have a complex fibre structure. This also has implications of orientation reliability in fibre tractography, where misleading direction of the tensor may cause false negatives, in which tracking terminates earlier, or false positives, in which tracking continues to areas where it does not belong [61].
Therefore, other modelling techniques have been proposed to account for complex fibre architectures. The DSI method of Wedeen et al. can solve the orientations of crossing fibres. However, the amount of measurements required makes the acquisition time too long to use as a regular method [8]. Moreover, a study from Alexander DC [62] showed that it remains unclear to what extent the DOD or PAS functions reflect the FOD. On the other side, SD methods estimate the FOD more directly [63], not requiring con-siderable computation time since it is a linear estimation [64]. Whichever is the chosen method, Fillard et al. [65] and Jeurissen et al. [66] showed that tractography can be used to track regions of multiple fibre
populations using the peak orientation obtained from the FOD instead of the first eigenvector recovered from DTI.
1.2.3 Maturation Analysis using NODDI
The challenges of DTI were presented [67], as well as an alternative to this method - NODDI, which extracts more specific parameters regarding tissue microstructure. There are already some studies which apply this model, focusing on newborn [68], infants [69] or adolescents [70], but mostly in the character-ization of neurologic diseases using NODDI parameters [71] [72] [73]. Abnormalities in the morphology of neurites seem to be a characteristic of pathologies. For instance, reductions in axonal density were found in multiple sclerosis, suggesting axonal loss [74]. Moreover, the correlation between FA and ax-onal density seems to be relatively weak, which may indicate NDI as a more specific estimate of density and, therefore, a more sensitive marker of axon pathology. There is also one study from Nazeri et al. [75] that uses NODDI to study changes in GM throughout the process of ageing.
Also, the introduction of AMICO in the study of the WM must be considered. This method was used in a study which aim was the characterization of the WM microstructure, with a cohort between 40 and 70 years old [76]. It was also applied in order to study axon diameter estimation [77] and structure networks [78]. However, to one’s knowledge, there are no studies regarding the application of NODDI/AMICO in a broad cohort from newborn to adulthood in order to study the brain WM fibres maturation.
Furthermore, few studies made comparisons between DTI and NODDI. Chang et al. [79] suggested that, during the first two decades of life, the increase of FA is a result of the increases in the NDI, and de-creases in FA after the fourth decade are due to inde-creases in the ODI. Besides, the NDI inde-creases slower in adulthood than in childhood whereas fibre ODI increases more slowly in childhood. Same results were described by Timmers et al. [80], showing that changes in NDI and ODI could explain much of the FA results, giving more specific results.
1.2.4 Tractography Implementation
WM fibres can be reconstructed using deterministic or probabilistic tractography [81] [82] [83]. As the study of Descoteaux et al. [84] or Behrens et al. [85] concludes, the deterministic approach will not often represent branching of the fibres into areas of low anisotropy or high curvature. Probabilistic tractogra-phy may be more appropriate to track crossing fibres because it allows more possibilities regarding the pathway direction at each sample point [86] [87]. From the work of Dhollander et al. [88], it is possible to visualize in Figure 1.8 the differences between the two methods.
(a) (b)
Figure 1.8: (a) - Deterministic and (b) - probabilistic approaches to extract the cingulum. Adapted from [88].
However, it sometimes accumulates errors, generally when computing long-range fibre pathways, plac-ing connections in the wrong locations [86]. Therefore, interpretations of the results should be always treated with caution.
Independently from the chosen approach, the success of the tract’s reconstruction is also dependent on the neuroanatomical knowledge of the one who has to ensure that the regions of interest (ROIs) are placed correctly. Despite this, the method had already produced proper reconstructions of WM fasciculi, such as the work of Catani et al. [89] and Mori et al. [90].
This project of fibre extraction is regularly used to obtain informations about different fibres specifically, as in the work of Epelbaum et al. [91], Doron et al. [92] and Lebel et al. [54] [55].
1.2.5 Goals
As mentioned, a change in the brain maturation can be the cause of multiple diseases, being its under-standing very important so as to identify different pathologies. To study this process, different methods were developed in order to get quantitative information regarding the WM tracts.
Having gathered information about the existent facts regarding brain maturation, and the results obtained from different diffusion MRI techniques, the overall aim of this project was then to map the maturation pattern of monkey brain connections using diffusion MRI. For that, a tract-based cross sectional analysis was performed so as to extract anatomical features from DTI and NODDI and use them as indicators for detecting development changes in the brain.
2
Methods
Given the broadness of the project, this section will be divided in two different topics, presented in a chronological order of the executed tasks. It begins with a description of the DWI dataset processing before the extraction of the DTI and NODDI parameters. It is followed by a detailed description of the fibres computation and all the post-processing methods utilized to speculate from the results.
2.1 Subjects
Twenty five monkeys were included in this study, aged 1 day to 48 months (mean age 25.6 ± 14.8 months, 14 females, 11 males). They are Vervet monkeys from the old world monkey family, as Rhesus and Macaque monkeys. One monkey (female, 30 months) has a fissure in CC, being excluded from the study of that fibre.
The animals were collected from the Behavioral Science Foundation, in Saint Kitts, Federation of Saint Kitts and Nevis. They were socially housed in enriched environments. The experimental protocol was reviewed and approved by the institutional review board of the foundation, acting under the auspices of the Canadian Council on Animal Care.
2.2 Tissue Fixation
The monkeys were anesthetized using a Zoletile mixture. Then fixation by perfusion was performed in 4% paraformaldehyde (PFA). This fixation procedure was chosen in order to ensure minimal autolysis in the tissue, resulting in a tissue with properties closer to in vivo tissues.
The brains were removed and postfixed for at least 12 hours in 1% PFA in phosphate-buffered saline (0.1 mol/l and pH 7.4) at room temperature. Finally, they were cooled to 5◦C for longterm storage.
This procedure was performed before the animals arrived to the DRCMR.
2.3 Image Acquisition
Ex vivo DWI scans of the fixed monkey brains were performed on an experimental horizontal 4.7 T Varian Inova MR scanner with a bore size of 154 mm and gradients with a slew rate of 560 mT/m/ms and a maximum strength of 140 mT/m. The main magnetic field strength (B0) was highly stable over time with a B0 drift of about 0.1 Hz/h. A single-channel volume RF coil, driven in quadrature, a conventional PGSE sequence with single-line read-out, and a typical bandwidth of 50 kHz for an image matrix of 128 × 128 were used. A three-shell DWI dataset was acquired with b-values: 2160 s/mm2(15 b0s, 84 directions, Gmax= 300 mT/m, ∆ = 12 ms, δ = 6 ms, TE = 36 ms, TR = 4200 ms), 3151 s/mm2(16 b0s,
87 directions, Gmax= 219 mT/m, ∆ = 20 ms, δ = 7 ms, TE = 36 ms, TR = 4200 ms)), and 9686 s/mm2
(13 b0s, 68 directions, Gmax= 300 mT/m, ∆ = 17 ms, δ = 10 ms, TE = 36 ms, TR = 7200 ms)), for a total
scan time of approximately 3 days.
In order to avoid instabilities in the dataset due to physical handling of the fixated tissue, a set of several dummy scans were acquired to allow the tissue to stabilise. Also, perturbations in the diffusion signal due to temperature changes were minimised by maintaining constant airflow around the tissue. Because of the stable B0 field and the optimized spin echo DWI sequence, it was not necessary to correct for spatial image distortions during data preprocessing.
A high resolution structural 3D T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) MRI was also acquired (echo spacing = 5.6 ms, TE = 3 ms, T1 = 800 ms, flip angle = 9, image matrix = 256×256×256, voxel size of 0.27×0.27×0.27 mm3, NEX = 12).
2.4 DWI Processing
In order not to bring unexpected bias to the final results, all DWI images were checked before any analysis. First, artifacts were searched for - since it is an ex vivo dataset, it is not a great concern. Initially the study had 29 monkey brains but three were discarded due to intensity problems and one due to a fissure between the hemispheres. One brain with shifting and intensity problems was accepted after FLIRT (FMRIB’s Linear Image Registration Tool) [93] [94] and the removal of some volumes. Two brains with the skull included were accepted after their removal. Then, MRtrix3 [95] was used to check for incorrect fibre orientation. MRtrix3 is a program designed for the analysis of diffusion MRI images. Twenty brains had their x axis fliped, which was corrected by changing the gradient scheme of the DWI images.
After checking the dataset quality, DTI and NODDI parameters were extracted. 2.4.1 FLIRT
FLIRT from FSL (FMRIB Software Library) is a robust and accurate program that performs linear (affine) registration of brain images. The purpose of the use of this tool was to register all the DWI images of the Vervet monkeys in their corresponding T1 images (Figure 2.1a), where cortical thickness analysis will be made. Although not part of this project, this assessment was performed.
With this said, FLIRT was used to do a 3D to 3D registration using a 6 parameter model (rigid body model), with equal orientation, inter-modal cost function using normalized mutual information algo-rithm (very robust), and default sinc interpolation. For each brain, it uses the low b0 of the DWI image as input in order to align it with the T1 image. Then, it uses the estimated transform to align the DWI image with the T1 image. Between these two operations, the T1 image resolution was changed from 0.25 mm to 0.5 mm. This happened since the T1 is used as reference to specify the voxel size and FOV of the output DWI image and it was decided to lower its resolution so that later procedures were computed faster.
Afterwards, all the output images were checked in order to ensure a perfect overlay between the T1 image and the output DWI image in T1 space, referred as DWI-T1 image from now on.
Figure 2.1: A comprehensive processing pipeline of the different methods applied throughtout the course of the project. (a) DWI images were registered in their correspondent T1 images. (b) Masks and FOD maps of the brains were computed, as well as the DTI and NODDI parameters maps. (c) FOD images were registered in a template. (d) ROIs were drawn in template space in order to compute fibres of interest. (e) Warps estimated from registration were used to warp those fibres to each monkey’s DWI-T1 image.
