Building an analysis pipeline to explore at ultra-high field (7T) MRI the laminar distribution of myelin in the human cortex in vivo

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2022

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE FÍSICA

Building an analysis pipeline to explore at ultra-high field (7T) MRI the laminar distribution of myelin in the human cortex in

vivo

Márcia Cláudia Guimarães Nunes

Mestrado Bolonha em Engenharia Biomédica e Biofísica

Dissertação orientada por:

Prof. Dr. Alexandre Andrade

Prof. Dr. Olivier Collignon

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Márcia Nunes i

Acknowledgements

I would like to start by thanking everyone who welcomed me to the CPP Lab. It was motivating throughout my staying in Belgium to see a huge group of people unravelling the brain. It was certainly a challenge from which I developed new ways of thinking. Especially to Professor Olivier Collignon for accepting me in the group right away, for the support, pertinent questions and for his kindness.

There are not enough words to thank Marco Barilari for all the time, effort, knowledge and pa- tience he had with me during this internship. You were a great teacher and without your support it wouldn’t have been possible for sure.

I want to thank Remi for helping me with a thousand technical problems, clarifying ideas, and most importantly for being a living toolbox. I want to extend the acknowledgment to Jacek, for his presence in discussion meetings which was fundamental to help clarify crucial points for the development of this thesis.

To the Erasmus + grant providers, I would like to thank for the financial support during my stay in Belgium, without it would not be possible.

To everyone who made my stay in Belgium go by in a flash, for all the good times, help and support, a big thank you, specially to Mariana, Anas and Ying.

To Alexandre Andrade, a special thank you for the guidance, support and helpful clarifications during this dissertation and throughout my education in FCUL.

I would also like to extend this acknowledgement to all the professors I had the pleasure to learn from during the last 5 years. Year after year you were impeccable and the will of seeing us succeed is shown with your constant commitment.

Um agradecimento especial à minha mãe, por ser o meu modelo de resiliência e de força de von- tade que me apoia incondicionalmente. Estendo o agradecimento à minha irmã e avós, não há palavras para agradecer o vosso apoio desde sempre.

Um agradecimento aos meus amigos que trouxe para esta jornada, em especial ao Rui por ser a verdadeira definição de amigo que eu sei que poderei sempre contar. Aos amigos que levo desta expe- riência, em especial à Diana, Tatiana e Beatriz, às pessoas espetaculares que conheci no BEST durante os últimos quatro anos. Juntamo-nos pelo conhecimento, mas ficamos pelas pessoas. Um obrigado especial aos queridos membros do Dartboard, esta experiência só faria sentido convosco.

Finalmente, ao Miguel, obrigada por me fazeres acreditar nas minhas capacidades, pela motivação e apoio constante, sem ti teria sido impossível.

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Márcia Nunes ii

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Márcia Nunes iii

Resumo

O mundo está repleto de informação visual dinâmica, percecionada e processada hierarquica- mente ao nível do sistema visual, mais concretamente, nas regiões anatómicas do cérebro especiali- zadas em processar diferentes aspetos de um objeto visual. O córtex occipitotemporal ventral (do inglês, VOTC) constitui uma das áreas de alto nível no córtex visual, crucial na categorização de informação visual, traduzindo-se em partes específicas do VOTC categoricamente seletivas ao tipo de estímulo visual tais como faces, locais, casas ou ferramentas. No entanto, um estudo com uma população de cegos congénitos demonstrou que a experiência visual não era necessária para o desenvolvimento da arquite- tura funcional existente no VOTC. Em particular, foi demonstrada uma remodelação do mapa topográ- fico em resposta a estímulos auditivos na população com cegueira, que reflete muito aproximadamente o mapa topográfico de categorias a estímulos visuais na população com visão normal. Estudos anteriores demonstraram que o desenvolvimento de regiões que categorizam faces no VOTC levam a alterações na densidade de mielina, mas não em regiões que categorizam locais. Segundo a literatura, ainda não foi estudado se é a experiência visual que leva ao aumento de densidade de mielina destas regiões do VOTC ou se é um processo inato. Adicionalmente, ainda não foi investigado se a remodelação do mapa topográfico do VOTC na ausência de inputs visuais bottom-up durante o desenvolvimento também se reflete na distribuição laminar de mielina em regiões do VOTC.

Atualmente, avanços em técnicas de neuroimagiologia permitem o estudo in vivo do perfil de mielina à mesoescala através de parâmetros quantitativos e de campos magnéticos ultra-altos (do inglês, UHF). A Ressonância Magnética (do inglês, MRI), é altamente sensível à mielina e permite o seu mape- amento quantitativo - técnica denominada de MRI quantitativa (do inglês, qMRI).

O objetivo desta dissertação consiste em selecionar a sequência de MRI a 7T disponível no Cyclo- tron Research Center da Universidade de Liège, que melhor permita a quantificação da densidade de mielina no VOTC das populações com visão e com cegueira, assim como desenvolver uma pipeline open-source para caracterizar a distribuição laminar da mielina nas regiões de interesse do VOTC em ambos os grupos.

O Cyclotron Research Center, que é onde as aquisições futuras no âmbito deste projeto serão adquiridas, permite MRI a UHF (7T) e dispõe de um protocolo para aquisições anatómicas de múltiplos mapas (do inglês, MPM) baseados em relaxometria: densidade de protões (do inglês, PD), tempo de relaxação transversal efetivo (T2^*), transferência de magnetização (do inglês, MT) e tempo de relaxação longitudinal (T1). A mielina contribui em diferentes percentagens para estes mapas, o que é útil para complementar e corroborar os resultados de distribuição cortical de mielina. No entanto, um estudo realizado pelo grupo que gere a sequência de MPM neste centro de aquisição mostrou que existe uma variabilidade muito elevada entre scans, em particular nos córtices temporal e occipital, regiões que são importantes para o estudo da distribuição de mielina no VOTC. Neste projeto a sequência MPM foi comparada à sequência de aquisição anatómica Magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE), que estava a ser adquirida para um projeto de MRI funcional a UHF a ser desen- volvido no grupo onde esta dissertação foi desenvolvida, pelo que poderia ser utilizada para o desenvolvimento desta pipeline caso demonstrasse devolver imagens com melhor qualidade comparativamente à sequência de MPM. A sequência MP2RAGE foi construída para originar imagens uniformes, maiori- tariamente ponderadas no tempo de relaxação longitudinal (T1), denominadas UNIT1, através das quais é possível criar mapas quantitativos de T1, os quais são dominados principalmente pela concentração de mielina. Por esta razão, a presença de ruído e variabilidade entre scans foi estudada para a sequência MP2RAGE, de forma a comparar com os resultados publicados para a sequência de MPM.

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Márcia Nunes iv Para avaliar a qualidade das imagens UNIT1 e os mapas T1, três sujeitos foram submetidos a duas aquisições anatómicas de MRI a 7T com a sequência MP2RAGE na Universidade de Liège. As estima- tivas do rácio entre o sinal e ruído (do inglês, SNR) mostraram bons resultados em regiões do VOTC para as imagens UNIT1 e mapas T1 quando comparado com o SNR de volumes mais abrangentes do cérebro. Os resultados de diferença percentual relativa (do inglês, RPD) entre scan e rescan foram muito promissores quando comparados com os resultados do protocolo de MPM, e sugerem que não existe introdução pronunciada de ruído e inomogeneidade pela sequência MP2RAGE. A avaliação da qualidade das imagens UNIT1 e mapas T1 mostrou que a qMRI baseada numa aquisição MP2RAGE oferece mapas de T1 de elevada resolução, quantitativos e reprodutíveis.

Seguiu-se para a construção de uma pipeline open-source que permitiu extrair um perfil laminar cortical de T1, de forma a estudar a distribuição laminar de mielina em regiões do VOTC. A pipeline foi construída a partir da imagem UNIT1, adquirida na primeira sessão da avaliação da qualidade das imagens da sequência MP2RAGE. Os principais passos da pipeline consistiram na criação de mapas T1 a partir das imagens UNIT1 com resolução isotrópica de 0.75 mm, o aumento da resolução das imagens UNIT1 e mapas T1 para uma resolução isotrópica de 0.375 mm, o pré-processamento espacial das imagens UNIT1 que inclui a segmentação e remoção do crânio e background das imagens UNIT1, a criação de camadas corticais equidistantes, criação de máscaras binárias das regiões de interesse (do inglês, ROI) para o estudo, retiradas de atlas funcionais probabilísticos em espaço normalizado e movidas para o espaço nativo de cada sujeito. Por fim, os valores de T1 foram extraídos dos mapas T1 para cada uma das seis camadas corticais de cada ROI. Estes passos foram aplicados inicialmente para o córtex visual primário (do inglês, V1) como controlo e de seguida para regiões categoricamente seletivas do VOTC a estímulos de locais, no sulco colateral (do inglês, CoS) e a faces, nos giros fusiforme lateral médio (do inglês, mFus) e fusiforme lateral posterior (do inglês, pFus).

Os resultados obtidos com a sequência MP2RAGE demonstraram que a correção de inomogeneidade introduzida pela aquisição é essencial para obter uma boa segmentação dos tecidos cerebrais.

Desta forma, foi possível obter uma segmentação da matéria cinzenta confiável, permitindo a computa- ção de camadas corticais e estudo da distribuição de mielina em V1 e posteriormente nas ROI do VOTC.

Apesar das limitações atuais existentes na computação de camadas corticais, o aumento da resolução é crucial para que um vóxel não contenha várias camadas corticais, melhorando o entendimento sobre a distribuição de mielina.

Os perfis laminares de T1 para V1 e mFus, pFus e CoS mostraram uma diminuição nos valores de T1 da camada mais próxima do fluido cefalorraquidiano para a camada vizinha da matéria branca, o que sugere um aumento na densidade de mielina do fluido cefalorraquidiano para a matéria branca. Estes resultados estão de acordo com estudos anteriores ex vivo e in vivo em V1 e no VOTC, o que demonstra que a sequência MP2RAGE providencia imagens que permitem extrair perfis corticais laminares de mielina em regiões do VOTC, através de aquisições mais rápidas comparativamente à sequência MPM.

Estes resultados incentivam futuras comparações entre as populações com cegueira e com visão normal.

Comparando visualmente os perfis laminares de T1 para mFus, pFus e CoS, parece existir uma diferença numérica nos valores de T1 entre CoS e mFus das camadas intermédias no hemisfério es- querdo. No entanto, a comparação visual dos perfis laminares de T1 encontrados para os hemisférios sugerem resultados distintos entre hemisférios.

Seria relevante explorar no futuro ainda maiores resoluções, assim como maiores números de camadas, já que estudos histológicos mostraram que a mesma camada, das seis camadas corticais principais, pode conter subcamadas com diferentes composições.

A pipeline desenvolvida nesta dissertação corresponde às primeiras etapas deste projeto, mas no futuro, será possível entender se a remodelação do perfil funcional cortical de projeções devido à

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Márcia Nunes v plasticidade intermodal, causada pela cegueira, leva a alterações da distribuição cortical de mielina em regiões do VOTC.

Palavras-chave: pipeline, plasticidade da mielina, camadas corticais, in vivo, ressonância mag- nética de campo ultra-alto

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Abstract

The ventral occipital-temporal cortex (VOTC) is a high-level area in the visual cortex where specific regions are exclusively category-selective for certain visual stimuli. Some studies have demonstrated that absence of visual experience can reshape this functional architecture. In particular, they demonstrated that crossmodal plasticity due to blindness causes a remodeling of the category topographic map in response to auditory stimuli, reflecting closely the visual category topographic map in the sighted population. To our knowledge, it has not yet been investigated if this topographic map remodeling in the absence of bottom-up visual inputs during development is also reflected in the myelin distribution laminar profile within VOTC regions. Nowadays, advancements in neuroimaging techniques allow the mesoscale (submillimeter) in vivo myelin profiling using quantitative markers and 7T ultra-high field (UHF) magnetic resonance imaging (MRI). The aim of this dissertation is to select the MRI sequence available in the 7T MRI at the Cyclotron Research Center (ULiège) that would best allow the quantification of myelin density in the VOTC of sighted and blind individuals and to develop an open-source pipeline to characterize the myelin laminar distribution in the regions of interest in both groups. The assessment of noise and variability between scans demonstrated that compared to the multiparametric mapping sequences, MP2RAGE provides quantitative and reproducible high-resolution myelin maps. The myelin laminar profiles obtained in the regions of interest are in line with previous ex vivo and in vivo studies, demonstrating that MP2RAGE is a measure allowing myelin cortical profiling in VOTC with faster data acquisition. The pipeline here developed corresponds to the first stages of the above-mentioned project and will allow in the future comparative studies of the cortical laminar organization between the blind and sighted populations, improving our understanding about the role of crossmodal plasticity in reshaping cortex microstructure.

Keywords: analysis pipeline, myelin plasticity, cortical layers, in vivo, ultra-high field magnetic resonance imaging

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List of abbreviations

B0 External magnetic field 𝐵₁⁻ Receive magnetic field 𝐵₁⁺ Transmit magnetic field BIDS Brain imaging data structure

CNR Contrast-to-noise ratio CNS Central nervous system

CoS Collateral sulcus CoV Coefficient of variation CSF Cerebrospinal fluid

FA Flip angle

FFA Fusiform face area FID Free induction decay

fMRI Functional MRI

GM Grey matter

GRE Gradient recalled echo hMT Human middletemporal cortex

IOG Inferior occipital gyrus ITG Inferior temporal gyrus LOS Lateral occipital sulcus

𝑀₀ Net magnetization

𝑀_𝑥𝑦 Transverse magnetization 𝑀_𝑧 Longitudinal magnetization mFus Mid-lateral fusiform gyrus

MPRAGE Magnetization prepared rapid gradient echo

MP2RAGE Magnetization prepared 2 rapid acquisition gradient echoes MPM Multiparametric mapping

MR Magnetic resonance

MRI Magnetic resonance imaging MT Magnetization transfer MTG Middle temporal gyrus NMR Nuclear magnetic resonance

OL Oligodendrocyte

OPCs Oligodendrocyte precursive cells OTS Occipital temporal sulcus

PD Proton density

PFC Prefrontal cortex

pFus Posterior lateral fusiform gyrus PPA Parahippocampal place area

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Márcia Nunes xii

qMRI Quantitative MRI

R1 Longitudinal relaxation rate R2^* Effective transverse relaxation rate

RF Radiofrequency

RPD Relative percentage difference SNR Signal-to-noise ratio

T1 Longitudinal relaxation time T2^* Effective transverse relaxation time

T2 Transverse relaxation time

TA, TB, TC Delays introduced in the MP2RAGE sequence

TE Echo time

TI Inversion time

OTS Occipital transverse sulcus

TR Repetition time

UHF Ultra-high field

UNIT1 Uniform T1-weighted image V1 Primary visual cortex V2 Secondary visual cortex V3 Third visual cortex

VOTC Ventral occipito-temporal cortex

WM White matter

α Flip angle

γ Gyromagnetic ratio

𝜔₀ Larmor frequency

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Márcia Nunes xiii

List of Figures

Figure 1.1 Generalized scheme from Zilles et al. (2015)¹⁹ comparing cytoarchitectonic and myeloarchitectonic lamination patterns in the cortex. Roman numbers and Arabic numbers identify cytoarchitectonic and myeloarchitectonic layers, respectively. Layers I and 1 are close to cerebrospinal fluid (CSF) and layers VI and 6 are close to white matter (WM). This is the bistriate type, characterized by two horizontal myelin-rich bands, called band of Baillarger, found in layers IV and V...3

Figure 1.2: Topographical selectivity maps of VOTC for the for the visual stimulation in sighted controls (top), the auditory stimulation in blind (center) and the auditory stimulation sighted controls (bottom) participants. These selectivity maps show the voxel-wise preferred stimulus condition to the main four categories in each group: animals, humans, small objects and places. This figure is taken from Mattioni et al. (2020)¹¹. ...7

Figure 1.3: Protons possess a positive charge and are constantly spinning around their own axes (a). The production of a magnetic field parallel to the axis of rotation makes this arrangement analogous to a bar magnet in which the magnetic field is considered oriented from the south to the north pole (b).

Precession explained by the perpendicular coordinates (x and y) and the parallel component to 𝐵0, z axis (c). Scheme taken from Dale et al. (2015)¹¹⁴. ...9 Figure 1.4: The decay of transverse magnetization and growth of longitudinal magnetization during the relaxation process following saturation. Figure taken from McRobbie et al. (2017)¹¹⁷. ... 10

Figure 1.5: Saturation of the broad resonance of the bound pool. The lower free pool peak is the result of the MT process from the free pool to the bound pool. Figure taken from Boer et al. (1995)¹³². ... 13

Figure 1.6: Scheme explaining MP2RAGE acquisition sequence with inversion times TI1 and TI2

of the first and second GRE blocks, respectively. MP2RAGETR is the time between two successive inversion pulses and TR is the time between successive pulse sequences in each GRE block, which is composed of n excitations. TA, TB and TC are delays before the first block, between blocks and after the second block, respectively. 𝛼1 and 𝛼2 are the flip angles of the first and second GRE block, respectively. Figure taken from Marques et al. (2010)¹⁴³. ... 15

Figure 1.7: Representative transverse slices obtained with the MP2RAGE protocol at 7T.

Representation of the magnitude and phase images of both (a) 𝐺𝑅𝐸𝑇𝐼1, (𝑏) 𝐺𝑅𝐸𝑇𝐼2, (c) their combination through the expression Equation 1.4. and (d) the T1 map. Figure adapted from Marques et al. (2010)¹⁴³... 15

Figure 2.1: Flowchart diagram of the quality controls estimation. This diagram provides a detailed overview of all inputs, processing steps and outputs for the Part 1 of the Pipeline. The rectangle shapes represent data (inputs or outputs), the stadium shapes represent processes (orange and green for workflows implemented respectively with bidspm and CPP ROI) and bellow the stadium shapes is a transparent rectangle representing the chosen software or toolbox to perform the process. The pipeline is performed with each subject. ... 20 Figure 2.2: Maximum probability map of 25 topographic visual regions displayed in both (a) surface and (b) volume. The primary visual cortex dorsal and ventral (V1d and V1v, respectively) are respectively represented in red and brown. Figure taken from Wang et al. (2015)¹⁶⁵. ... 22

Figure 2.3: Maximum probability map of occipito-temporal cortex functional regions of interest in volume representation in MNI space. Figure taken from Rosenke et al. (2021)¹⁰¹... 23 Figure 2.4 Flowchart diagram of the T1 relaxation profile extraction in the primary visual cortex and category-selective regions of the VOTC: mFus, pFus and CoS. This diagram provides a detailed overview of all inputs, processing steps and outputs for the Part 1 of the Pipeline. The rectangle shapes

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Márcia Nunes xiv represent data (inputs or outputs), the stadium shapes represent processes (orange and green for workflows implemented respectively with bidspm and CPP ROI) and bellow the stadium shapes is a transparent rectangle representing the chosen software or toolbox to perform the process. The pipeline is performed with each subject. ... 26 Figure 3.1: Relevant outputs of the segmentation step performed with bidspm and bidsNighres workflows for UNIT1 and T1 map, respectively. Sagittal view of (a) the bias corrected UNIT1, (b) UNIT1 brain mask, (c) T1 map with the skull and background removed and (d) T1 map brain mask (sub- 001, session 1). ... 27

Figure 3.2: Sagittal view of the (a) bias corrected UNIT1, (b) no bias corrected UNIT1, both without skull and background, and (c) voxel count of the intensity values for both images showing different voxel counts for the image intensities as a result of the bias correction (sub-001, session 1). 28 Figure 3.3: Sagittal, coronal and axial views of the spatial distribution of the regions of interest (ROIs) extracted from the Wang atlas: primary visual cortex dorsal and ventral (V1d and V1v, respectively) and from the Visfatlas: secondary visual cortex dorsal and ventral (V2d and V2v, respectively), third visual cortex dorsal and ventral (V3d and V3v, respectively), inferior occipital gyrus (IOG), inferior temporal gyrus (ITG), middle temporal gyrus (MTG), lateral occipital sulcus (LOS), human middle temporal cortex (hMT), posterior lateral fusiform gyrus (pFus), occipital transverse sulcus (OTS), mid-lateral fusiform gyrus (mFus) and collateral sulcus (CoS). The ROIs are represented in individual space at 0.75 mm resolution (sub-001, session 1). ... 28 Figure 3.4: (a) Sagittal, (b) axial and (c) coronal views of the UNIT1 segmented in 47 labels with FreeSurfer at the resolution of 0.75 mm. The segmentation includes the cerebral white matter and grey matter tissues from both the left and right hemispheres (sub-001, session 1). ... 29

Figure 3.5: Axial view of the (a) white matter (WM), (b) grey matter (GM) and (c) GM and GM binary masks combined, created from the output of FreeSurfer’s segmentation at 0.75 mm resolution (sub-001, session 1). ... 29 Figure 3.6: SNR estimation in the UNIT1 acquired in session 1 in occipital-temporal regions of interest (ROIs) extracted from the Wang atlas (V1d and V1v) and VisfAtlas (V2d, V2v, V3d, V3v, IOG, ITG, MTG, LOS, hMT, pFus, OTS, mFus and CoS), ordered from posterior to anterior, and for whole- brain WM, GM and combination of WM and GM. Each participant's SNR estimates can be identified by their distinct colors. ... 30 Figure 3.7: SNR estimation in the T1 map acquired in session 1 in occipital-temporal ROIs extracted from the Wang atlas (V1d and V1v) and VisfAtlas (V2d, V2v, V3d, V3v, IOG, ITG, MTG, LOS, hMT, pFus, OTS, mFus and CoS), ordered from posterior to anterior, and for whole-brain WM, GM and combination of WM and GM. Each participant's SNR estimates can be identified by their distinct colors. ... 30 Figure 3.8: SNR estimation in the UNIT1 acquired in session 1 in the grey matter (GM) within each occipital-temporal ROI extracted from the Wang atlas (V1d and V1v) and VisfAtlas (V2d, V2v, V3d, V3v, IOG, ITG, MTG, LOS, hMT, pFus, OTS, mFus and CoS), ordered from posterior to anterior.

The SNR was plotted for both hemispheres. Each participant's SNR estimates can be identified by their distinct colors. ... 31

Figure 3.9: SNR estimation in the T1 map acquired in session 1 in the grey matter (GM) within each occipital-temporal ROI extracted from the Wang atlas (V1d and V1v) and VisfAtlas (V2d, V2v, V3d, V3v, IOG, ITG, MTG, LOS, hMT, pFus, OTS, mFus and CoS), ordered from posterior to anterior.

The SNR was plotted for both hemispheres. Each participant's SNR estimates can be identified by their distinct colors. ... 31

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Márcia Nunes xv Figure 3.10: Representation of the voxel-wise relative percentage difference (RPD) between sessions 1 and 2 for UNIT1. RPD is represented for sub-001, sub-002 and sub-003, and ranges from 0

% (dark red) to 202 % (yellow). ... 32 Figure 3.11: Representation of the voxel-wise relative percentage difference (RPD) between sessions 1 and 2 for T1 map. RPD is represented for sub-001, sub-002 and sub-003, and ranges from 0

% (dark red) to 202 % (yellow). ... 32 Figure 3.12: Median RPD estimation in the UNIT1 in occipital-temporal ROIs extracted from the Wang atlas (V1d and V1v) and VisfAtlas (V2d, V2v, V3d, V3v, IOG, ITG, MTG, LOS, hMT, pFus, OTS, mFus and CoS), ordered from posterior to anterior. Each participant's median RPD can be identified by their distinct colors. ... 32 Figure 3.13: Median RPD estimation in the T1 map in occipital-temporal ROIs extracted from the Wang atlas (V1d and V1v) and VisfAtlas (V2d, V2v, V3d, V3v, IOG, ITG, MTG, LOS, hMT, pFus, OTS, mFus and CoS), ordered from posterior to anterior. Each participant's median RPD can be identified by their distinct colors. ... 33 Figure 3.14: Sagittal view of the (a) UNIT1 and (b) T1 map at 0.375 mm isotropic resolution; (c) UNIT1 and (d) T1 map without background and skull at 0.375 mm isotropic resolution. The last column shows the gained spatial resolution with the up-sampling step, using T1 map as an example. The T1 map at 0.75 mm isotropic resolution and up-sampled T1 map to 0.375 mm isotropic resolution in the region identified in (d) T1 map with a red rectangle are respectively identified in (e) and (f) (sub-001, session 1). ... 34 Figure 3.15: Sagittal view of the six cortical layers equally distanced for (a) whole-brain and (b) magnified in the primary visual cortex (sub-001, session 1). ... 34

Figure 3.16: Sagittal, axial and coronal view of V1 dorsal (V1d) and V1 ventral (V1v) extracted from the Wang atlas and moved to the individual space (sub-001, session 1). V1d and V1v are represented in orange and red, respectively. ... 35 Figure 3.17: Sagittal, axial and coronal view of the cortical layers in V1d and V1v (Wang atlas) in the individual space (sub-001, session 1). The six equidistant cortical layers are represented in different shades of orange (V1d cortical layers) and red (V1v cortical layers). The lighter shades of orange and red represent cortical layer I, which is closer to the cerebrospinal fluid (CSF), becoming darker towards cortical layer VI, which is closer to the white matter (WM). ... 35 Figure 3.18: T1 relaxation laminar profile in the primary visual cortex (V1) for left and right hemisphere, from the CSF neighbor layer to the WM neighbor layer. The mean of the T1 values in each layer is represented for each participant with distinct shapes in blue. The means of the three participants T1 values and standard deviation are represented in red. ... 35 Figure 3.19: Sagittal, coronal and axial views of pFus, mFus and CoS binary masks in individual space (sub-001, session 1) in the first row. In the second row, the sagittal, coronal and axial views of the cortical layers in pFus, mFus and CoS are represented in the individual space (sub-001, session 1). The lighter shades of the assigned color to each region represents cortical layer I, which is closer to the CSF, becoming darker towards cortical layer VI, which is closer to the WM. ... 36 Figure 3.20: T1 relaxation laminar profile in the posterior lateral fusiform gyrus (pFus), mid- lateral fusiform gyrus (mFus) and collateral sulcus (CoS), from the CSF neighbor layer to the WM neighbor layer. The mean of the T1 values in each layer is represented for each participant with distinct shapes in blue. The means of the three participants T1 values and standard deviation are represented in red. The results for both hemispheres are plotted in the figure with the left hemispheres in the first column and the right hemispheres on the second column. ... 37

Figure 3.21: T1 relaxation laminar profile in the primary visual cortex (V1), posterior lateral fusiform gyrus (pFus), mid-lateral fusiform gyrus (mFus) and collateral sulcus (CoS), from the CSF

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Márcia Nunes xvi neighbor layer to the WM neighbor layer. The average of the subjects T1 values in each layer is represented for each of the four regions of interest with distinct colours. The results for both hemispheres are plotted in the figure. ... 38 Figure 4.1: Comparison of myelin distribution found in ex vivo and in vivo studies: (a) Myelo- and cytoarchitectonic images from myelin and Nissl staining in the primary visual cortex, taken from San Román and Bidmon (2018)¹⁷⁷. This figure shows an increase of myelin density from layer I (closer to the pial surface) to layer VI (closer to white matter); (b) Plot taken from Marques et al. (2017)¹⁷⁶ showing the longitudinal relaxation rate (R1 = 1/T1) fitted results (solid line) for different depths (from white matter to pial surface) for different Brodmann regions: Br.2 somatosensory; Br.4 primary motor cortex; Br.17 primary visual cortex; Br. 41 auditory cortex; Br. 44 Broca's area and Br. 32 cingulate region. R1 shows an increase from the pial surface to the white matter, suggesting an increase of myelin density towards white matter. ... 42 Figure 4.2: Figures taken from Natu et al. (2019)¹³ showing the validation of in vivo data using adult postmortem myeloarchitecture. (a) A sample histological section stained for myelin. Dashed red, midfusiform sulcus (MFS), fusiform gyrus (FG) and dashed green, collateral sulcus (CoS); (b) Measurements of optical density across cortical depths in 5 postmortem brains along the CoS and FG/MFS; (c) Development of T1 in grey matter as a function of cortical depth. T1 curves across equidistant intracortical depths from the pial surface (pial) to grey matter (GM) and into adjacent white matter (WM) in bilateral mFus-faces (red) and bilateral CoS-places (green) across age groups; (d) In vivo measurement of relaxation rate (R1) showing greater R1 in Right pFus-faces than CoS-places in middle and deep cortical layers across 27 adults. ... 43

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Introduction

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1 Introduction

The isocortex of the human brain consists of six layers with distinct roles which differ in their myelin organization pattern and the arrangement and density of neuronal cells^1–4. These differences led to the study of myeloarchitecture and cytoarchitecture, discovering that the two architectures are correlated and that it is possible to predict aspects of one through the other⁵.

Axonal myelination emerged to increase the velocity and efficiency of conduction of neural impulses in each axon, leading to increasing complex nervous systems⁶. However, differences found in different cortical regions and in the cortical architecture itself lead to questioning the role of myelin and an understanding of the factors that regulate its content in the human brain, such as experience⁷. One of the most striking demonstrations of experience-dependent plasticity comes from studies of blind individuals showing that the occipital cortex extends considerably its response to non-visual information processing, despite traditionally considered as visual^8–10. The ventral occipito-temporal cortex (VOTC) is a high-level area in the visual cortex and is especially crucial for the neural processing underlying visual categorization¹¹. In order to understand if visual input is necessary for the development of the functional architecture in the VOTC, a recent study by Mattioni et al. (2020) involving congenitally blind people demonstrated that large-scale categorical layout in VOTC shares similarities between sighted and blind people while categorizing auditory inputs¹¹. This finding suggests that the development of category selectivity in VOTC does not require visual experience¹². These results by Mattioni et al. (2020) demonstrate that early visual deprivation leads to crossmodal plasticity, altering the functional cortical input profile in VOTC. Nonetheless, it is still unknown if this remodeling is reflected in the myelin distribution in the cortical layers of VOTC regions.

It has been shown how the development through the life span from childhood to adulthood shapes the microstructure of the VOTC, for instance modifying the myelin content of the faces-selective regions^13,14. Nonetheless, it is not clear yet if the increase in myelin density in face-selective regions hap- pens because of faces visual stimuli or independently of visual experience. To our knowledge, no study investigated how sensory experience influences the laminar cortical myelin density of these regions in VOTC with an intrinsic capacity to categorize inputs.

The rise of non-invasive methods that allow the characterization of intracortical anatomy in vivo, such as the Magnetic Resonance Imaging (MRI), are much needed since histological methods are so far largely applied ex vivo. Additionally, MRI is highly sensitive to myelin and it allows the quantification of variations in the underlying microstructure by providing multiple images that reflect distinct contri- butions of myelin in the human brain – a technique referred to as multiparametric mapping (MPM)^15,16.

Advancements in neuroimaging allowed the study of the human brain at higher resolutions using ultra-high field MRI (UHF MRI). Despite the increase in signal-to-noise ratio (SNR) with increased magnetic field strengths, there is also an introduction of inhomogeneity during the acquisition. The presence of noise and variability between scans can highly affect the obtained results and consequently invalidate the conclusions taken in the context of any project. Therefore, it is necessary to evaluate the quality of the images offered by the existing quantitative sequences in the acquisition centers, the relevance of the parameters offered and to take into account the speed of acquisition. Within the scope of this dissertation, it is relevant to make a comparison of the MPM protocol available at the Cyclotron Research Center in University of Liège and the MP2RAGE sequence, which is an anatomical sequence that is being acquired as part of another project of the research group where this dissertation was developed.

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Introduction

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These topics motivated the development of this dissertation and therefore will be further detailed throughout this section.

1.1 Myeloarchitecture

The first time the cortex of the human brain was visualized in the XIX century it was described as alternating black and white layers¹⁷. The intracortical layers were described as containing densely packed myelinated fibers running parallel to the cortical surface¹⁷. Once observed microscopically, the isocortex of the human brain was described as composed by six layers which differ in their myelination pattern and the density and arrangement of neuronal cells^2,18. Figure 1.1 compares the cytoarchitectonic (left) and myeloarchitectonic (right) laminar organization in the isocortex¹⁹.

The supragranular layers (I, II and III layers from Figure 1.1) tend to be more lightly myelinated than the infragranular layers (V and VI layers)²⁰. Differences in the proportion of the cortical thickness devoted to supragranular compared to infragranular layers may influence the myelin content measurement²⁰. Heavily myelinated cortex is often thinner²¹, with larger numbers of neurons²², which may have a larger number of inputs and outputs, but it also has smaller cells with simpler dendritic arbors^23,24, taking up less space²⁰. These factors increase the fraction of volume filled with myelin compared to non- myelinated structures, leading to an increase in the relative myelin content in these cortical areas²⁰.

Studies suggest that there is a strong link between cytoarchitecture and the laminar pattern of myelin in the isocortex^5,25. An interesting study from Hellwig et al. (1993)⁵ demonstrated with a com- puter simulation that cytoarchitecture predicted myelin content profiles in many cortical areas, matching major features in histological myelin stains of the same areas suggesting that laminar myeloarchitecture and cytoarchitecture are strongly linked^5,20. Recent studies have also observed that neuronal density is correlated with myelin maps. Areas with higher neuronal density tend to have higher myelin content while more lightly myelinated areas tended to have lower neuronal densities^20–22, larger neuronal cell bodies, and more complex intracortical circuitry, including larger dendritic field sizes, larger dendritic arbors, and more dendritic spines^20,23,24. These findings demonstrate the relevance of studying the myeloarchitecture, understanding what leads to these distinct densities of myelin in different cortical regions and distinct myelin distributions in cortical architecture, as well as studying how this translates into the functional organization of the human cortex.

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Introduction

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Figure 1.1 Generalized scheme from Zilles et al. (2015)¹⁹ comparing cytoarchitectonic and myeloarchitectonic lamination patterns in the cortex. Roman numbers and Arabic numbers identify cytoarchitectonic and myeloarchitectonic layers, respectively. Layers I and 1 are close to cerebrospinal fluid (CSF) and layers VI and 6 are close to white matter (WM). This is the

bistriate type, characterized by two horizontal myelin-rich bands, called band of Baillarger, found in layers IV and V.

Variations in myeloarchitecture across cortical regions started raising the question about the role of myelin. Although myelin emerged to increase the velocity and efficiency of action potentials⁶, in more complex circuits this well-established role of myelin may differ⁷. It was observed that inhibitory basket cell axons in the cortex and descending axons of excitatory pyramidal cells contained an inter- mittent myelin pattern, indicating that rapid saltatory conduction velocity was not the function in these cells⁷. Considering that precision of spike timing is a pivotal parameter in neuronal network function⁷, it was suggested that long unmyelinated axonal segments may be essential to fine-tune and coordinate both conduction speed and transmission between their unmyelinated segments^7,26. Other studies hypoth- esize that myelin in the cerebral cortex may be an inhibitor of intra-cortical circuit plasticity⁷. Regions with higher brain functions are usually associated with lower myelin contents but more complex intracortical circuits and are composed of neurons with large dendritic arbors and more synapses⁷. Despite these identified myelin differences that suggest different roles for myelin, a recent study demonstrated that myelin variations may be explained with genetic flaws that cause defective migration of neuronal populations, leading to an increase in myelin density in intermediate layers of the auditory cortex, trans- lating into dyslexia²⁷.

These studies demonstrate that the roles of different factors that can regulate the distribution of myelin in the human brain are still unclear, one of them being experience. For this reason, a special attention to the regulation of myelin content will be provided in the next section.

1.2 Myelin plasticity

The nervous system has the capacity to reorganize the function and structure of its connections in response to environmental inputs, which is named neuroplasticity^28–30. The root of this reorganization is the formation and refinement of neural circuits, allowing our brains to develop, acquire knowledge, learn new skills, and recover from injuries²⁸. The neural basis of cognition, learning and plasticity is the

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Introduction

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dynamic relation between brain structure and brain function³¹ where experience has a tremendous influence on it.

Neural changes in response to experience can range from variations in brain size, cortical thickness, neuron size, dendritic branching, spine density, synapses per neuron and number of glial numbers³².

Since the 1960s, studies have been showing that environmental enrichment – stimulation of the brain with larger, more stimulating surroundings, with increased opportunities for socialization and physical activity³³ - can lead to macrostructural changes in mammals. For instance, it was reported an increase in brain weight and cortical thickness of rats in environments of higher complexity compared to isolated rats^34–36.

Following these macrostructural findings, researchers started looking at the microscopical level.

Since then, alterations in neuron’s dendrites, synaptogenesis and neurogenesis, have been reported in response to different developmental conditions, from birds to primates. There are studies that reported a density increase in dendritic spines in the cerebral cortex of rats exposed to enriched environments^37–

39 and some of them showed differences of about 20 %^38,39. It was also reported an increase in branching and in length of terminal segments, found in the dendritic tree of pyramidal cells in layers II and III of the visual cortex of the adult rat after exposure to enriched environments⁴⁰. An increased branching on pyramidal neurons of layers II and III in the motor cortex was also observed in rats after a motor skill task⁴¹.

The generation of neurons has been identified in adult rats under distinct conditions such as following the production of brain lesions⁴², from post-natal brains to adult brains^43–48 and with learning tasks^49,50. For example, it was reported the creation of projection neurons in the higher vocal center of the canary forebrain during and after song learning⁵⁰.

The above-mentioned studies in animals inspired the search for similar results in humans, leading to the discovery of neurogenesis in hippocampus post-mortem tissue of adult human brains, in 1998⁵¹.

Another process that evidence has been showing to contribute to neuroplasticity is carried by myelinating glial cells, the oligodendrocytes (OLs). OLs are non-neuronal cells that form myelin insu- lation around nerve fibers, increasing vastly their conduction velocity and the speed of neural processing⁵². Studies have been showing that OLs differentiate from oligodendrocyte precursive cells (OPCs) throughout life and likely serve additional roles in neural circuit formation and function which are still being investigated^53–55. In fact, Yeung et al. (2014) found evidence of substantial ongoing myelin maintenance throughout adulthood in the corpus callosum but a low rate of neurogenesis after childhood, suggesting that myelin remodulation is carried by mature OLs^56,57. OLs appear to have a relevant role in the adjustment of myelination parameters and to be responsive to individual axons, as an answer to their needs⁵⁸.

The advantage of axonal myelination was a critical evolutionary improvement that enabled the development of increasingly complex nervous systems⁵⁹. Myelination increases the velocity and efficiency of conduction of neural impulses in each underlying axon⁶. The speed of conduction is influenced by the myelin sheath thickness^6,60 and internode length and spacing^56,58. However, some studies have shown that in situations of complete development, myelin parameters do not achieve ideal proportions for the fastest action potential propagation in the optic nerve and spinal nerves^56,61,62. It now appears that dynamic regulation of myelination and the existence of suboptimal myelination profiles may regulate the precise timing of information propagation and communication across functional circuits^56,63. Alt- hough the mechanisms behind the regulation of myelin content are not yet unraveled⁵⁸, its relevance is clear, as demonstrated by a study showing that the myelin patterns variation is convenient for sound localization circuitry, since it allows the fine tuning of conduction velocities along neuronal circuits^6,56,64.

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Introduction

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Axons that better respond to low-frequency sounds have a larger diameter and shorter internodes, compared to axons that better respond to high-frequency sounds⁶⁴.

As mentioned previously, OLs exhibit the intrinsic capacity to initiate production of myelin sheaths. However, intrinsic cues alone do not regulate myelin microstructure⁵⁶. The development of myelination is a protracted process that continues until adulthood^65–67, suggesting that extrinsic factors such as experience might influence myelination⁵⁶. For this reason, the influence of neuronal activity and in particular the regulation of myelin plasticity with experience will be introduced in the next section.

1.2.1 Regulation of myelination with neuronal activity and experience

Some studies have shown that neuronal activity can regulate myelination^68–77. Experiments showed that blocking neuronal activity impedes proliferation of oligodendrocyte precursive cells (OPCs)⁶⁸, while the increase of neuronal activity promotes proliferation^78,79. These proliferation and differentiation of OPCs processes can explain previous studies of myelination regulation with experience showing accelerated myelination in the optic nerve following early eye opening^68,69 and a delay following dark rearing^68,70, whisker removal or raising mice in social isolation^71–73,80. Nonetheless, there are studies that conflict with these findings, demonstrating that ocular deprivation can lead to enhanced differentiation but shortened myelin sheaths internodes⁷⁴ or that whisker cutting increases the rate of oligodendroglial lineage cell apoptosis, reducing the total number of mature OLs^56,81.

Other experiences such as training and learning show variations in the human brain volume or density using qualitative MRI, suggesting alterations of myelin content during learning and training^35,82–

84. Following reading training⁸⁵, volume increases were observed in the grey matter of the occipital lobe⁸⁶ and white matter³⁵. Increased grey matter was also found in motor, auditory, and visual–spatial brain regions when comparing musicians and non-musicians^35,84.

The development of in vivo neuroimaging techniques allowed to unravel the mechanisms hap- pening after the influence of experience, explaining the phenomena behind volume brain changes and many others. Some of these studies reported myelin changes after learning skills using diffusion- weighted imaging by measuring water diffusivity in white matter, associated with myelin content, named fractional anisotropy⁵⁶. Structural changes in human white matter and grey matter occur with learning new tasks, such as playing the piano^87–89 or learning how to juggle^90,91. A significant correlation between practicing time and fractional anisotropy was found for all age periods, suggesting that neural activity in fiber tracts during practicing lead to increased myelination⁸⁷. During learning of a second language, language learners demonstrated significant increases in fractional anisotropy⁹². Social neglect and disturbances in social behavior in children and juvenile primates lead to decreased fractional anisotropy in the white matter of the limbic system⁹³.

Some experiments correlated macroscale findings with histological measures in animals. One of these studies found that the learning rate of a motor skill is positively correlated with fractional anisotropy in white matter regions and suggested that at least some of the changes in fractional anisotropy are explained by increases in myelin density^88,94. Other studies reported differentiation of OPCs into OLs in mices and a dependency with myelination in the motor cortex and corpus callosum after motor learning^88,95,96. The mice lacking OLs to produce myelin were less able to learn a new motor skill involving running on a wheel with unequally spaced bars⁹⁵.

The complex organization of neuronal and non-neuronal cells has a huge impact on the architecture of the entire brain, shaping physiological and cognitive functions of the cerebral cortex⁹⁷. Although much progress is being made in unraveling myelin plasticity, these studies show that our knowledge of

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Introduction

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myelin plasticity remains restricted to regional differences and that the mechanisms underlying it still need to be immensely expanded.

1.3 Visual categorization in the human brain

The world is full of rich and dynamic information and in humans the cortex shows a systematic spatial and functional organization in specific regions where each one computes a specific category of information such as faces, voices, objects, and places¹¹ to name just few. The ventral occipito-temporal cortex (VOTC) is a large cortical expanse involved in many different functions and is especially crucial for the neural processing underlying visual categorization¹¹.

In VOTC, there are some regions among the others that respond preferentially and distinctly to faces and places⁹⁸. The fusiform face area (FFA) responds more strongly to faces than to other objects or scenes and can be found in the mid-lateral fusiform gyrus (mFus) and posterior lateral fusiform gyrus (pFus)⁹⁹. The parahippocampal place area (PPA) responds more strongly to places, compared to other types of input and is located on the collateral sulcus (CoS)¹⁰⁰ and on the transverse occipital sulcus (TOS)^101,102. The specific functional region organization observed in adults has raised a lot of interest to understand innateness and the influence of experience in the organization of VOTC^98,103,104.

When trying to understand the consistency and functionality in the visual system, researchers have turned to populations with an atypical sensory experience¹⁰⁵. The study of sensory deprived individuals allows the understanding of how sensory experience interacts with intrinsic biological constraints to shape the functional organization of the brain¹¹.

One of the most striking demonstrations of experience-dependent plasticity comes from studies of blind individuals showing that the occipital cortex, despite traditionally considered as visual, extends considerably its response to non-visual information processing^8–10. Nonetheless, researchers still debate on the mechanism underlying this crossmodal plasticity¹¹.

An experiment with congenitally blind people was performed in order to understand if visual input is necessary for the development of functional architecture in the VOTC and how individuals with and without sight respond to different categories of information¹². They successfully demonstrated that large-scale categorical layout in VOTC shares similarities between sighted and blind people, suggesting that the development of visual category selectivity in VOTC does not require visual experience^11,12. This experiment demonstrated a higher correlation between category-selective maps in congenitally blind and sighted controls presenting respectively auditory and visual stimuli, concerning four categories:

animals, humans and big and manipulable objects compared to category selective maps in congenitally blind and sighted controls presenting auditory stimuli to both groups (Figure 1.2)¹¹. Altogether, their results demonstrate that early visual deprivation triggers an extension of the intrinsic, partially non- visual, categorical organization of VOTC. This finding represents an important step forward in understanding how experience and intrinsic constraints interact in reshaping the functional properties of VOTC due to crossmodal plasticity¹¹. To our knowledge, no study investigated how sensory experience influences the underlying microstructure of these regions in VOTC and their intrinsic capacity to categorize inputs. Natu et al. (2019) showed that the development from childhood to adulthood shapes the myelin density in the cortical layers of face-selective regions in VOTC, but not of place-selective regions¹³. However, it is not known if these myelin variations over the life span are innate or due to experience, making these regions interesting to study in visually deprived people. Crossmodal plasticity due to the absence of visual inputs reshapes the auditory topographic map in the blind population to become significantly close to the visual topographic map in the sighted population in the VOTC and it may also have a role in reshaping the functional cortical input profile, and as a result the myelin distribution in

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Introduction

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the cortical layers of VOTC regions. Although it is not possible to predict the differences that will be found in the future because these questions have not yet been addressed either in vivo or ex vivo, the comparison of the myelin laminar profile in VOTC regions between the sighted and the blind populations could improve our understanding about the role of crossmodal plasticity in reshaping the VOTC microstructure.

Figure 1.2: Topographical selectivity maps of VOTC for the visual stimulation in sighted controls (top), the auditory stimula- tion in blind (center) and the auditory stimulation sighted controls (bottom) participants. These selectivity maps show the voxel-wise preferred stimulus condition to the main four categories in each group: animals, humans, small objects and places.

This figure is taken from Mattioni et al. (2020)¹¹.

To facilitate studies of the human brain in large populations and longitudinally, non-invasive methods that allow the visualization of intracortical anatomy in vivo and the quantification of variations in the underlying microstructure are much needed since histological methods are mostly applied ex vivo². The major obstacle to imaging cortical microstructural organization in living humans is the com- parable difference between the cortical layers and imaging resolution². Considering standard MRI resolutions of around 1x1x1 mm³, it is not possible to visualize myelin bands. Human MRI scanners at ultra- high magnetic field strengths are becoming more available to the neuroscience community, bringing the key advantage of higher spatial resolutions at which data is acquired¹⁰⁶. Although it is not yet possible to visualize the cortical layers, it is possible to examine the laminar distribution of myelin in vivo and compare myelin profiles of different populations². In fact, Skeide et al. (2018) studied with UHF MRI whether it was possible to detect anatomical anomalies in vivo in dyslexic patients. They found a higher myelin density in intermediate layers for dyslexic patients, hypothesizing that increased myelin is associated with genetic abnormalities that cause defective neuronal migration in the auditory cortex²⁷.

The use of neuroimaging techniques such as MRI requires its understanding so that it can be used efficiently to characterize the human brain tissue. The next section will explain the principles of magnetic resonance that allow the generation of human brain images traditionally weighted and its applica- tion to provide non-invasive histology.

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1.4 Quantitative measures in vivo

1.4.1 Magnetic Resonance Imaging

Nuclear magnetic resonance (NMR) is a method that allows studying the interaction of particles in a material placed in a strong magnetic field¹⁰⁷. The first reports of observation of NMR was in the 1940s¹⁰⁷, in solids^108,109. It was firstly explored with animals to discriminate between malignant tumors and normal tissue¹¹⁰ and later with humans¹¹¹. Since then, its medical benefits have been recognized, promoting the development and exploration by an enormous number of researchers to retrieve in vivo images of bodies^107,112.

The use of MRI demands an understanding of the elementary magnetic resonance (MR) principles. The basic principles of MR physics and image generation will be explained in this section, reflecting the reason behind the success of MRI to examine anatomical and physiological properties of non- human and human brains.

1.4.1.1 Principles of Magnetic Resonance

MRI is an imaging modality based on NMR that provides a spatial map of the hydrogen nuclei in different tissues¹¹³. Some of the main advantages of MRI in comparison to other imaging modalities is the fact that it possesses an excellent contrast between tissues, as well as an outstanding spatial resolution, commonly achieving resolutions of less than 1 mm, nowadays¹¹³. Additionally, MRI is considered a safe imaging method for the patients, considering it uses nonionizing radiation^107,113.

MR is a measurement technique used to examine atoms and molecules ¹¹⁴. The foundation of MR comes from the interaction between an applied magnetic field and the nucleus of an atom. An atom is composed of three types of particles: protons, neutrons and electrons. Protons (positively charged) and neutrons compose the nucleus and as a result, it becomes positively charged. Protons are constantly spinning around an internal axis of rotation^114,115, producing a magnetic moment oriented parallel to the axis of rotation (Figure 1.3 (a))¹¹⁴. Since protons have magnetization associated with them, they may be compared with a small bar magnet (Figure 1.3 (b)) ^112–115.

The magnetic moment of protons is normally randomly oriented. However, in the presence of a strong external magnetic field (𝐵₀), the magnetic moments line up parallel or antiparallel to 𝐵₀. Particles have the tendency to prefer the state of alignment that requires least energy. Therefore, more protons will align to 𝐵₀ than against it107,112,115 and the precession movement will continue around the z axis, the direction of 𝐵₀¹¹⁵. The stronger 𝐵₀, the more protons will be aligned with 𝐵₀, producing a stronger signal during the MRI acquisition. The motion of each proton can be described by the perpendicular coordinates, x and y, and the parallel coordinate to 𝐵₀, the z axis (Figure 1.3 (c))¹¹⁴.

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Figure 1.3: Protons possess a positive charge and are constantly spinning around their own axes (a). The production of a magnetic field parallel to the axis of rotation makes this arrangement analogous to a bar magnet in which the magnetic field

is considered oriented from the south to the north pole (b). Precession explained by the perpendicular coordinates (x and y) and the parallel component to 𝐵₀, z axis (c). Scheme taken from Dale et al. (2015)¹¹⁴.

The Larmor equation determines the precessional angular frequency (named as the Larmor Fre- quency, 𝜔₀) and indicates its dependency to the strength of the magnetic field, 𝐵₀¹¹⁵:

𝜔₀ = 𝛾𝐵₀ Equation 1.1

where 𝛾 is the gyromagnetic ratio which is specific for a particular element, such as hydrogen¹¹⁵. When energy in the form of a short radiofrequency (RF) pulse at frequency 𝜔₀ is applied with a magnetic component perpendicular to the direction of 𝐵₀, termed 𝐵₁, the net magnetization (𝑀₀) will rotate towards the xy-plane¹¹³. At this point, the precessing nuclear spins are synchronous (phase coher- ency). As the protons diphase and go back to its equilibrium state, reemitting the energy previously absorbed, the MR signal is produced^107,114. This relaxation process occurs in two ways: transversely (z axis) and longitudinally (xy-plane). As the protons start to diphase with each other and return to a lower energy state, the transverse magnetization (T2) decreases and the longitudinal magnetization (T1) is recovered¹¹⁵. The relaxation processes are schematized in Figure 1.4.

The T2 relaxation is the process in which there is a loss of phase coherence of the individual spins, due to interactions between neighboring protons (spin-spin relaxation). This dephasing results in an exponential decay of the signal to zero and can be explained by the following mathematical expression¹⁰⁷:

𝑀_𝑥𝑦(𝑡) = 𝑀₀ 𝑒⁻^𝑇2^𝑡 Equation 1.2

where 𝑀_𝑥𝑦 is the transverse magnetic moment after time 𝑡 for a 𝑀₀ transverse magnetization at 𝑡 = 0.

Water molecules bound to large molecules are more likely to interact, leading to a more rapid dephasing and as a consequence shorter T2 relaxation times¹¹⁶. On the other hand, free water molecules are relatively far apart and move rapidly, making spin-spin interactions less frequent and as a consequence, long T2 relaxation times¹¹⁶. Another cause for the dephasing of spins are the inhomogeneities in the applied magnetic field. The combination of both causes is known as effective transverse magnetization (T2^*)¹¹⁶. The dephasing of spins produces a Free induction decay (FID) signal¹⁰⁷.

T1 relaxation is the process in which excited protons return to their normal state by releasing energy to the local tissue (spin-lattice relaxation)¹⁰⁷. As a result, longitudinal magnetization, 𝑀_𝑧, returns

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to its original value. The recovered 𝑀_𝑧 after a time t in a material with a relaxation constant T1 follows the mathematical expression¹⁰⁷:

𝑀_𝑧(𝑡) = 𝑀₀(1 − 𝑒⁻^𝑇1^𝑡 ) Equation 1.3

where T1 relaxation constant is the time needed to recover 63 % of 𝑀_𝑧.

The relaxation time is dependent on the composition of tissues¹⁰⁷ since different molecules have different rates of molecular motion. Therefore, T1 relaxation will be affected by it¹¹⁵. Moreover, the value of T1 depends on the surrounding tissues, as the transfer of energy will be more efficient if the frequency of the excited protons corresponds to that of the neighboring molecules¹¹⁶. Water-based tissues with a high macromolecular content tend to have shorter T1 values¹¹⁶. Compared with T2, T1 is significantly longer.

Figure 1.4: The decay of transverse magnetization and growth of longitudinal magnetization during the relaxation process following saturation. Figure taken from McRobbie et al. (2017)¹¹⁷.

The relaxation process following saturation is schematized in Figure 1.4. In order to apply these MR basic principles to create an image is its necessary to distinguish between signals coming from protons located at different spatial positions within the body¹¹³. Not until recently the concept of making MR an imaging modality was applied¹¹³. In order to distinguish signals from particles in different spatial locations, it is necessary to vary the magnetic field spatially within the subject, causing a spatial variation in resonant frequencies that could be exploited to form an image¹¹³. For this reason, a magnetic field gradient was introduced to produce a linear variation of the magnetic field intensity in a direction in space. With the recorded signal, the k-space is defined by the space covered by the frequency and phase encoding data. The k-space creation is based on representing spatial frequencies in the x and y directions in the chosen slice (z direction)¹¹³. The MRI image is reconstructed by a three-dimensional inverse Fou- rier Transformation, determining the location of each signal component^113,118.

A good contrast sensitivity in MRI is achieved by emphasizing the differences among spin density, T1 and T2 relaxation time constants¹⁰⁷. This interest led to the tailoring of pulse sequences. One of the most commonly used pulse sequence is the gradient recalled echo (GRE)¹⁰⁷. The GRE technique uses a magnetic field gradient to induce the formation of an echo following the RF excitation pulse, instead of 180º pulses¹⁰⁷. However, the signal and contrast are going to be influenced by mechanisms such as field inhomogeneity or susceptibility since these are not refocused with the gradient echo at echo time TE¹¹⁹. Therefore, the echo amplitudes are determined by T2^* decay, making GRE signal intensity

Building an analysis pipeline to explore at ultra-high field (7T) MRI the laminar distribution of myelin in the human cortex in vivo

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