New metods for the investigation of dynamics of functional magnetic resonance imaging data

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UNIVERSIDADE DE LISBOA FACULDADE DE CI ˆENCIAS DEPARTAMENTO DE F´ISICA

NEW METHODS FOR THE INVESTIGATION OF DYNAMICS OF

FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA

Isabel Francisca Sota Machado Barradas

MESTRADO INTEGRADO EM ENGENHARIA BIOM ÉDICA E BIOFÍSICA Especialização em Sinais e Imagens Médicas

Dissertac¸˜ao orientada por:

Prof. Doutor Alexandre da Rocha Freire de Andrade e Prof. Doutor Dimitri Nestor Alice Van De Ville

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The important thing is not to stop questioning. Curiosity has its own reason for existing.

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Acknowledgements

This thesis is the result of my work done at the Medical Image Processing Lab (MIP:Lab) during the academic year 2016/2017, to complete the Master of Science degree of Biomedical and Biophysics Engineering at the Faculty of Sciences of the University of Lisbon. It is a pleasure to thank those who made this work possible.

First of all, I must thank the scholarship under the Swiss-European Mobility Program, provided via the University of Geneva.

I wish to acknowledge Prof. Alexandre Andrade, my internal supervisor, who has always made available his support through all the steps of this thesis. Even though we were not in the same country, Prof. Alexandre was always concerned, supportive, and gentle.

I would like to express my profound gratitude to Prof. Dimitri Van de Ville, my external supervisor and head of MIP:Lab, for this amazing opportunity. His guidance and his insightful approach in all scientific matters were crucial in several moments of this project.

My heartfelt appreciation goes to Thomas Bolton. Although words are not enough to describe the honor of working under his direct supervision, I am deeply grateful for all the patience, teachings, help, and friendship (and Swiss chocolates) during the last past months.

I would like to offer my thanks to all of those I had the chance to meet in the MIP:Lab. A special thanks for my Master’s colleagues, Merel, Yasaman, and Stefano, who shared a great part of my days, always offering me their friendliness and support. For Daniela, Lorena, Anjali, and Nico, with whom I discussed several aspects of this work. For Manuela da Silva, a very special person, who is always willing to help and share her “magic”. And finally, for Maria Giulia Preti (who also makes the best tiramisu) and Younes Farouj, for having the patience and the care in reading my work as if it were their own. I am certain that it is impossible to find an environment as special as MIP:Lab’s and, for this reason, I wish to recognize the valuable help of all provided during this project.

I want to thank my friends from “the Best Floor”, in particular Isha, Mohamed, Konsta, Valeria, Jorge, and Belen. During this demanding period, it was always good to know that I would arrive home and have dinner with such adorable people. They were also amazing partners when exploring one of the most beautiful countries in the world! Thank you, guys.

I owe a very important debt to my friends from FCUL, since I am particularly privileged to have met such charming, smart, integer, and good people, providing me joyous moments during the last past years. It is impossible to name them all, so I would like to thank Iara Almeida, Inˆes Rodrigues, Inˆes Ver´ıssimo, Joana Boita, and Tiago Toste for visiting me in the mountain country. Their warm encouragement and insightful discussions were also invaluable. Thanks!

I am deeply grateful to Lu´ıs, who has always been by my side. His love, joy, and understanding were crucial in the hardest periods, but also very important and unique to share the good moments. Thanks for everything.

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Switzerland and whilst writing this thesis, but also over the past six years) would not have been possible without their support and encouragement. A minha gratidão mais profunda vai para os meus pais, por tudo o que fizeram por mim e por serem umas grandes referências na minha vida. Ao meu pai, com quem sempre partilhei o gosto pela matemática e pelas ciências em geral e que nunca permitiu que eu me contentasse. À minha mãe, pela prova de amor infinito que me deu ao enfrentar os seus medos para me apoiar. I am truly blessed for having them as my parents, thanks.

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Resumo

O estudo in vivo das diversas regi˝oes cerebrais e da forma como est˝ao conectadas tem vindo a beneficiar dos avanços da neuroimagiologia. Este trabalho foca-se na análise da actividade cerebral, raz˝ao pela qual é utilizada a ressonância magnética funcional (fMRI, do inglês functional magnetic resonance imaging), uma técnica n˝ao-invasiva que permite o mapeamento indirecto da actividade cerebral. Em consequência da actividade neuronal, a resposta hemodinâmica entra em funcionamento, o que provoca alteraç˝oes no fluxo sangu´ıneo e no n´ıvel de oxigénio e está na base da criaç˝ao do sinal BOLD (do inglês Blood Oxygenation Level-Dependent).

Sendo um sistema de incomensurável complexidade, o cérebro envolve a conex˝ao entre diversas redes; isto dá origem à noç˝ao de conectividade cerebral, que pode ser funcional ou estrutural. A co-nectividade funcional, definida como a associaç˝ao estat´ıstica entre eventos neurofisiológicos remotos, foi durante muitos anos vista como estacionária ao longo do scan. Contudo, estudos recentes vieram revolucionar este campo, ao provar que o cérebro é altamente dinâmico em escalas temporais pass´ıveis de ser medidas com fMRI.

Surgiu assim o conceito de “conectividade cerebral dinâmica” (dFC, do inglês dynamic functional connectivity), cuja análise se tem revelado muito promissora para a percepç˝ao do funcionamento cere-bral. A dFC pode ser explorada tanto durante a realizaç˝ao duma tarefa, como em estado de repouso. Apesar de recente, já conta com resultados auspiciosos em contexto cl´ınico, incluindo em pacientes do espectro autista (ASD, do inglês autism spectrum disorder). N˝ao obstante, continua a haver necessidade de explorar novas técnicas para analisar as alteraç˝oes da dinâmica cerebral.

Os innovation-driven co-activation patterns (iCAPs) foram recentemente propostos para estu-dar padr˝oes cerebrais de co-activaç˝ao ou de co-desactivaç˝ao, cujo princ´ıpio está relacionado com as alteraç˝oes transitórias nos volumes de fMRI. Para tal, é necessário empregar a total activation (TA), um método de desconvoluç˝ao baseado em conhecimentos prévios sobre a funç˝ao da resposta hemodinâmica (HRF, do inglês hemodynamic response function). Obtêm-se assim sinais que reflectem a actividade transitória para todos os voxels do cérebro, que sofrem posteriormente um processo de clustering.

Os referidos iCAPs foram anteriormente obtidos com indiv´ıduos saudáveis em repouso, revelando zonas cerebrais que alteram a sua actividade em conjunto. Neste trabalho, esta metodologia inovadora foi utilizada para captar as redes neuronais transitórias presentes na visualizaç˝ao dum filme (MW, do inglês movie-watching) e em estado de repouso (RS, do inglês resting-state) e como s˝ao afectadas na doença de ASD, em comparaç˝ao com indiv´ıduos saudáveis.

Para tal, recorreu-se a dados de fMRI duma experiência realizada em autistas e em indiv´ıduos com desenvolvimento t´ıpico (TD, do inglês typically developing). Primeiramente, os participantes assistiram a um filme por um per´ıodo de 5,8 minutos; em seguida, foi-lhes solicitado que repousassem com os olhos fechados durante 5 minutos. Esta experiência compreendeu três sess˝oes, em que as primeiras duas inclu´ıram MW seguido de RS e a última conteve apenas RS. Após o pré-processamento dos volumes de fMRI (realinhamento, co-registo, normalizaç˝ao e uma avaliaç˝ao do movimento da cabeça, etc.), foram

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consideradas 28 sess˝oes de MW e 43 de RS, provenientes de 8 sujeitos TD e de 10 ASD.

Para estudar a dinâmica cerebral, usou-se a técnica de TA, cujo objectivo é reconstruir o activity-related signal(x) a partir do sinal de fMRI medido (y), que contém ru´ıdo. Tal é conseguido através duma regularizaç˝ao espaciotemporal que tenciona encontrar o argumento x que minimize o ru´ıdo. Sabendo que este sinal é o resultado da convoluç˝ao entre o activity-inducing signal (u) e a HRF (h), procede-se à desconvoluç˝ao de x e obtém-se u. Em seguida, o sinal u é derivado, originando o innovation signal us,

cujos picos contêm informaç˝ao sobre os momentos em que o voxel em quest˝ao altera a sua actividade (picos positivos indicam um aumento da actividade, picos negativos uma diminuiç˝ao). Este último sinal representa a contribuiç˝ao principal para a inovaç˝ao desta técnica, pois permite a obtenç˝ao dos iCAPs – padr˝oes que mostram áreas que alteram a sua actividade em conjunto e n˝ao áreas que s˝ao activadas ou desactivadas ao mesmo tempo.

Após obter os innovation signals para todos os voxels, estes sofrem um processo de limiar espaci-otemporal. Primeiro, a TA é aplicada nas séries temporais dos dados corrompidos (cuja fase original é aleatória) e, com base nisso, é definido um limiar para cada sujeito (intervalo de confiança de 1%) acima do qual mantemos os pontos. Desta forma, apenas as “transiç˝oes” proeminentes s˝ao mantidas. Em seguida, para excluir ru´ıdo espacial, só s˝ao considerados pontos temporais nos quais pelo menos 5% dos voxels est˝ao activos. Finalmente, o método k-means é executado nas “inovaç˝oes” sobreviventes ao processo, o que leva à obtenç˝ao dos iCAPs.

Neste projecto, os iCAPs foram gerados separadamente para as condiç˝oes de RS e de MW, tendo-se escolhido um k de 20 clusters. Após a aquisiç˝ao destes padr˝oes, os marcadores correspondentes (identi-ficativos de sujeitos e pontos temporais), bem como os respectivos cursos temporais, foram recuperados. A análise entre grupos foi ent˝ao conduzida, usando o teste t com correcç˝ao de Bonferroni.

No que diz respeito aos resultados provenientes da análise de RS, apenas 15 dos 20 resting-state innovation-driven co-activation patterns (RS-iCAPs) foram considerados n˝ao ruidosos. Nestes, foram identificadas regi˝oes referentes a diversas redes, como a default mode network (DMN), o córtex visual primário e secundário, o córtex auditivo primário e redes motoras.

A fim de compreender de que forma as populaç˝oes contribu´ıram para cada um dos RS-iCAPs, a quantidade de frames provenientes de cada sujeito foi tida em conta. Foi encontrada uma diferença significativa entre os dois grupos no RS-iCAP 13, que reflecte regi˝oes do lado direito da rede executiva. Isto significa que esta rede, responsável por vários processos cognitivos (e.g., capacidade para lidar com a novidade, processos de decis˝ao com factor emocional, controlo inibitório, etc.), está mais presente no grupo TD.

Da análise dos cursos temporais, concluiu-se que a rede executiva do RS-iCAP 13, a rede visual do RS-iCAP 12, a rede cognitiva do RS-iCAP 4, a DMN presente no RS-iCAP 10 e a rede audi-tiva/somatossensorial do RS-iCAP 11 s˝ao, respectivamente, os RS-iCAPs de maior duraç˝ao. Já em relaç˝ao ao número de ocorrências, os RS-iCAPs 13, 12 e 4 foram também os principais, mas seguidos pela DMN do RS-iCAP 8 e pelo padr˝ao pré-frontal do RS-iCAP 14. No entanto, n˝ao houve diferenças significativas entre grupos para nenhuma destas medidas.

Por fim, avaliaram-se as diferenças espaciais entre as duas populaç˝oes. Estimou-se, para cada sujeito, o mapa espacial com a média das frames que entraram na formaç˝ao de cada RS-iCAP, permitindo a comparaç˝ao. Foram obtidas diferenças significativas (maior intensidade no grupo TD) para os RS-iCAPs 1 e 5, nas zonas do giro fusiforme e do cúneo, respectivamente.

Já em relaç˝ao ao estudo durante o filme, 17 movie-watching innovation-driven co-activation patterns (MW-iCAPs) revelaram-se n˝ao ruidosos e foram considerados para análise. Entre eles, encontram-se diversas redes visuais, relacionadas com a atenç˝ao e dos córtices motor e pré-motor.

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Neste caso, n˝ao foram encontradas diferenças significativas em relaç˝ao à contribuiç˝ao de cada populaç˝ao para os vários MW-iCAPs. Em relaç˝ao à análise dos cursos temporais, os padr˝oes de maior duraç˝ao foram referentes ao córtex pré-frontal do MW-iCAP 17, à rede auditiva do MW-iCAP 13, ao mapa lateral presente no iCAP 16, ao padr˝ao orbitofrontal do iCAP 8 e ao motor do MW-iCAP 12. Os mapas com mais ocorrências foram os MW-MW-iCAPs 17 e 8, seguidos do padr˝ao temporal do MW-iCAP 13, da rede motora do MW-iCAP 12 e do MW-iCAP 15, de natureza visual.

As diferenças espaciais foram significativas para o MW-iCAP 17, um padr˝ao com actividade frontal. Neste caso, as disparidades mostraram uma maior intensidade no grupo ASD, em três centróides: córtex pré-frontal dorsolateral (DLPFC, do inglês dorsolateral prefrontal cortex) direito e esquerdo, bem como córtex pré-frontal anterior. O DLPFC é conhecido pelo seu papel na working memory e na capacidade de alternar conceitos diferentes e inesperados, pelo que esta “hiperactivaç˝ao” pode estar relacionada com a regulaç˝ao anormal deste tipo de funç˝oes em ASD.

Devido à sincronizaç˝ao entre os sujeitos alcançada pela visualizaç˝ao do filme, foi poss´ıvel notar que os indiv´ıduos autistas apresentam uma maior activaç˝ao deste MW-iCAP do que os saudáveis no fim de uma cena espec´ıfica. O MW-iCAP 17 contém o córtex pré-frontal medial, uma regi˝ao conhecida por, entre outras funç˝oes, estar associada à consolidaç˝ao de memórias e à percepç˝ao do contexto. Sabendo que o cenário e os figurantes presentes já tinham surgido em pontos prévios do filme, esta aberraç˝ao do MW-iCAP 17 pode traduzir defeitos nestes processos.

Em última instância, tentou-se ultrapassar a ausência de diferenças significativas entre as populaç˝oes na contribuiç˝ao das frames de cada MW-iCAP. Neste sentido, a evoluç˝ao temporal da probabilidade de ser expressa uma inovaç˝ao foi calculada e comparada entre as duas populaç˝oes. Os resultados apontam para uma “hipersensibilidade” a mudanças da parte do grupo ASD, verificada na transiç˝ao de cenas e em alteraç˝oes dentro do mesmo cenário.

Assim, conclui-se que a metodologia de TA/iCAPs trouxe resultados relevantes no âmbito da dinâmica cerebral, para indiv´ıduos saudáveis e autistas. Além disso, o seu potencial n˝ao se limita apenas ao estudo da actividade cerebral intr´ınseca, podendo ser utilizada para desvendar redes neuronais envolvidas em diferentes tarefas.

Palavras-chave: Ressonância magnética funcional; Regularizaç˝ao espaciotemporal; Clustering; Dinâmica cerebral; Espectro autista

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Abstract

Functional magnetic resonance imaging (fMRI) has been shown to be fruitful as a tool to scrutinize the functioning of the brain, becoming widely used in the study of brain dynamics. Over the last few years, several studies with different approaches were conducted to analyze the dynamic functional connectivity (dFC). This concept can be assessed during the execution of a task, revealing the brain areas connected in correspondence of it, or upon resting state, showing the intrinsic brain activity. Even though we are in front of a recent field, the dFC clinical applications are already being exploited, as in the case of autism spectrum disorder (ASD) – a very complex neurodevelopmental disease. In this work, a state-of-the-art method, that includes a point process analysis followed by a clustering approach, was applied. Our goal was to retrieve transitory brain networks characterizing two states – movie-watching (MW) and resting-state (RS) – for two populations – typically developing (TD) and autistic participants – that could be compared.

In order to study brain dynamics, total activation (TA) – a fMRI deconvolution method based on prior knowledge of hemodynamic response function (HRF) – was applied. In TA, a spatiotemporal regularization of the fMRI signal is applied to obtain the activity-related signal and followed by the inverse of the HRF, leading to the activity-related signal. The derivative of this last signal results into the innovation signal – a sparse representation of the brain changes. A k-means clustering is then applied to the whole-brain innovation signals for obtaining the innovation-driven co-activation patterns (iCAPs). In this work, these maps were generated for both RS and MW separately and employed to investigate the differences across groups. Several measurements were defined to allow these comparisons, not only in a temporal domain, but also regarding the spatial expression of the respective iCAPs.

With respect to the resting-state iCAPs (RS-iCAPs), several regions were disentangled, such as the default mode network, primary and secondary visual cortical areas, and motor networks. A group-difference was found in the right central executive network, that is more TD-representative. Moreover, two visual patterns that were linked to visual processing and attention presented a lower intensity in ASD. Regarding the movie-watching iCAPs (MW-iCAPs), vision, attention, and motor networks were obtained, among others. Moreover, a prefrontal patterns exhibited a greater intensity in ASD, which may explain an abnormality in some cognitive processes (e.g., working memory). A “hipersensitivity” to movie changes was also discovered in ASD.

This study broadened this approach potential for brain dynamics’ investigation, as iCAPs are also capable to analyze task-based paradigms and be applied to neurological disorders.

Keywords: Functional magnetic resonance imaging; Spatiotemporal regularization; Clustering; Network dynamics; Autism spectrum disorder

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List of Abbreviations

A1 Primary Auditory Cortex ACC Anterior Cingulate Cortex AQ Autism-spectrum Quotient ASD Autism Spectrum Disorders

BOLD Blood Oxygenation Level-Dependent CAPs Co-activation Pattern

CEN Central Executive Network CT Computerized Tomography dFC Dynamic Functional Connectivity dHb Deoxyhemoglobin

DLPFC Dorsolateral Prefrontal Cortex DMN Default Mode Network

DTI Diffusion Tensor Imaging EEG Electroencephalography FC Functional Connectivity

fMRI Functional Magnetic Resonance Imaging FPN Frontoparietal Network

FWHM Full-width Half-maximum GLM General Linear Model GM Grey Matter

gTV Generalization of Total Variation HbO2 Oxyhemoglobin

HRF Hemodynamic Response Function ICA Independent Component Analysis iCAP Innovation-driven Co-activation Pattern IFC Inferior Frontal Cortex

IFG Inferior Frontal Gyrus IPL Inferior Parietal Lobule IPS Intraparietal Sulcus IQ Intelligence Quotient

MDD Major Depressive Disorder min minutes

MIP:Lab Medical Imaging Processing Laboratory MNI Montreal Neurological Institute

MNS Mirror Neuron System MPFC Medial Prefrontal Cortex

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MR Magnetic Resonance

MRI Magnetic Resonance Imaging MW Movie-watching

MW-iCAPs Movie-watching Innovation-driven Co-activation Patterns OFC Orbitofrontal Cortex

PCC Posterior Cingulate Cortex PET Positron Emission Tomography PFC Prefrontal Cortex

PPA Point Process Analysis ReHo Regional Homogeneity RS Resting-state

RS Spatial Regularizer

RS-fMRI Resting-state Functional Magnetic Resonance Imaging RS-iCAPs Resting-state Innovation-driven Co-activation Patterns RSN Resting-state Networks

RSS Residual Sum of Squares RT Temporal Regularizer

s seconds

S2 Secondary Somatosensory Cortex SAT Social Attribution Task

SC Structural Connectivity

sICA Spatial Independent Component Analysis SMA Supplementary Motor Area

STS Superior Temporal Sulcus TA Total Activation

TD Typically Developing

TFM Temporal Functional Mode

tICA Temporal Independent Component Analysis TPJ Temporoparietal Junction

TPN Task-positive Network TR Repetition Time

UV Ultraviolet

V1 Primary Visual Cortex V2 Secondary Visual Cortex V5 Middle Temporal Visual Area VLPFC Ventrolateral Prefrontal Cortex VMPFC Ventral Medial Prefrontal Cortex WTC Wavelet Transform Coherence

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List of Figures

2.1 Example of a structural T1-weighted image (left) and of T2*-weighted functional images (right) under two different conditions (A and B). These functional images are a series of three-dimensional volumes, separated by a repetition time (TR), which results in four-dimensional data (from [22]). . . 4 2.2 Representation of the predicted BOLD response (bottom) modeled as the convolution

of the stimulus function (top) and the HRF (middle) for block (left) and event-related (right) designs. In a block design, two or more conditions are alternated into extended time intervals (or blocks), allowing the comparison between them. By contrast, event-related stimuli are short discrete events, and their presentation can be randomized (from [22]). . . 5 2.3 At the top, the graph representation of brain connectivity is obtained by computing FC

over the entire scanning time (thus assuming its stationarity). In contrast, the three rows at the bottom illustrate possible changes over time in FC, computed over subsequent temporal windows: in terms of magnitude (time window 1), sign (time windows 2 and 3), and lost/gained connections (time window 3). In all schemes, positive and negative FC are denoted by red and blue edges, respectively (from [39]). . . 6 2.4 Examples of spatial distributions of independent components with abnormal connectivity

in autistic subjects. Top: left superior middle frontal gyrus (red) and precuneus/posterior cingulate gyrus (blue); bottom: right fusiform gyrus (red) and left middle frontal gyrus (blue) (adapted from [106]). . . 10 2.5 Representation of the sliding window analysis. The connectivity between two networks

(“A” and “B”) is computed using Pearson correlation coefficient between each pair of BOLD time courses, over a defined temporal interval (window). This window is charac-terized by its temporal length (W ) and is shifted by a fixed number of data points (T ), repeating the process throughout the scan duration and generating a connectivity time course (adapted from [39]). . . 12 2.6 Representation of a point process extracted from the normalized BOLD signal. The red

filled points are set when the BOLD signal crosses a given threshold (dashed line) from below (adapted from [7]). . . 15 2.7 Example of the application of total activation (TA) for a voxel in posterior cingulate

cor-tex (PCC). Top: measured fMRI signal (green) and the denoised activity-related signal obtained after TA regularization; middle: the activity-inducing signal without the hemo-dynamic effects; and bottom: sparse innovation signal, derivative of activity-inducing signal, peaking upon increases/decreases in neural signal (adapted from [9]). . . 16

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2.8 Representation of two different innovation-driven co-activation patterns (iCAPs). iCAP 8 exhibits DMN regions (PCC, angular gyrus, parts of precuneus, IPL, middle frontal, and medial prefrontal cortex) and also a part of medial temporal lobe, while iCAP 10 encompasses the posterior DMN (PCC, IPL, angular, and precuneus) and visual regions (cuneus) (adapted from [9]). . . 17 3.1 Experimental paradigm conducted by all subjects (TD and ASD). In this experiment,

the participants had to pass through 3 distinct sessions: in the first two, they watched a scientific television program for young people during 5.8 minutes, and then were asked to rest with eyes closed for a period of 4.9 minutes; in the third one, a resting-state scan of 5.1 minutes was recorded. The naturalistic task involved in this experiment (movie-watching) contained different types of scenes and interactions, such as scientific explanations (1stcolumn of movie features), games and interactions between one of the presenters and children (2nd column), sunny landscapes (3rd column), and emotional responses (4thcolumn). . . 21 3.2 Functional magnetic resonance imaging model. These signals include the input

(mea-sured fMRI signal) and the outputs (activity-related signal, activity-inducing signal, and innovation signal) of total activation. The activity-inducing signal is block-type and, in this sense, its derivative – the innovation signal – will be sparse. The activity-related signal is then the result of the convolution between the activity-inducing signal with the impulse response of the hemodynamic system. The fMRI signal corresponds to the activity-related signal corrupted with noise and signal artifacts. Adapted from Kara-hano˘glu et al. [8]. . . 23 3.3 Generalized forward-backward splitting algorithm, as applied in this framework, to solve

Equation 3.6. Copied from Farouj et al. [157]. . . 25 3.4 Diagram of the groups’ contribution analysis. For a specific iCAP (RS or MW), the

retrieval of the number of frames (# frames) from each subject that led to the formation of this pattern can be used for a group comparison. . . 28 3.5 Illustrative scheme of spatial comparison across groups. For a specific iCAP (RS or

MW), the frames from each subject are averaged, resulting in the subject-specifics maps. These maps are obtained for all subjects, TD and ASD, allowing a group comparison. . . 29 3.6 Illustrative example of contrast and design matrix in SPM12. In this case, the contrast at

hand was related to a decrease in the patients group (ASD) compared to the control sam-ple (TD), to detect brain areas with intensity reductions. Each group of participants were considered as a different “condition”, resulting in this design matrix. White represents 1 and black 0. . . 30 4.1 Representation of the input and outputs of total activation (TA) in a voxel located in the

posterior cingulate cortex during resting-state (a) and movie-watching (b). By applying TA to the BOLD signal (brick red), the activity-related signal (burnt orange) is obtained, as well as the activity-inducing signal (lime green), without the effect of the hemody-namic response, and its derivative, the innovation signal (olive green). Positive peaks of the innovation signal in red, negative peaks in blue. . . 32

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4.2 Resting-state innovation-driven co-activation patterns (RS-iCAPs). These 15 maps (within the 20 clusters) were considered to be robust and ordered according to their occurrence rate (by descending order). . . 35 4.3 Groups’ contribution (TD in green, ASD in yellow) for each RS-iCAP, regarding the

number of innovations (positive and negative together). In each retained cluster, the number of innovation frames was computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the number of frames of each subject are also displayed. A two-sample t-test was performed for all RS-iCAPs to understand if there is a difference across groups on the contribution of frames. A difference was found for RS-iCAP 13 (p = 0.013), signalling a higher contribution of TD subjects for this map. . . 37 4.4 Total duration of the RS-iCAPs in each group (TD in green, ASD in yellow), after

com-puting the normalized iCAPs’ time courses. In each retained cluster, the total durations of the iCAPs were computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the total durations of each subject are also displayed. A two-sample t-test was performed for all RS-iCAPs to understand if there is a difference across groups on the RS-iCAPs’ total durations. No significant differences were found. . . 39 4.5 Number of occurrences of the RS-iCAPs in each group (TD in green, ASD in yellow),

after computing the normalized iCAPs’ time courses. In each retained cluster, the num-ber of occurrences of the iCAPs were computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the number of oc-currences of each subject are also displayed. A two-sample t-test was performed for all RS-iCAPs to understand if there is a difference across groups on the RS-iCAPs’ number of occurrences. No significant differences were found. . . 40 4.6 Average duration of the RS-iCAPs in each group (TD in green, ASD in yellow), after

computing the respective total duration and number of occurrences. In each retained cluster, the average durations of the iCAPs were computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the average durations of each subject are also displayed. A two-sample t-test was performed for all RS-iCAPs to understand if there is a difference across groups on the RS-iCAPs’ average durations. No significant differences were found. . . 40 4.7 Spatial comparison across groups in RS-iCAP 1 by means of the t-map resulting from

SPM12 (uncorrected analysis with p < 0.001, clusters with 20 or more voxels were kept). This map suggests a difference around the centroid (-39,-67,-14) (MNI space), with the contrast “TD > ASD” that implies a decrease in the patient group (ASD) com-pared to the control group (TD). This area belongs to the left fusiform gyrus. . . 41 4.8 Axial view (z = −14) of the group-specific RS-iCAPs 1 (z-normalized), with TD in the

top and ASD in the bottom. These maps were generated with the retained innovation frames from each group, for RS-iCAP 1. The extension of the TD-specific RS-iCAP 1 is greater than the ASD-specific RS-iCAP 1 in the area of the left fusiform gyrus. . . 42

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4.9 Spatial comparison across groups in RS-iCAP 5 by means of the t-map resulting from SPM12 (uncorrected analysis with p < 0.001, clusters with 20 or more voxels were kept). This map suggests a difference around the centroid (-29,-94,+31) (MNI space), with the contrast “TD > ASD” that implies a decrease in the patient group (ASD) com-pared to the control group (TD). This area belongs to the left cuneus. . . 43 4.10 Axial (z = +31) and sagittal views (x = −24) of the group-specific RS-iCAPs 5

(z-normalized), with TD in the top and ASD in the bottom. These maps were generated with the retained innovation frames from each group, for RS-iCAP 5. The extension of the TD-specific RS-iCAP 5 is greater and more intense than the ASD-specific RS-iCAP 5 in the area of the left cuneus. . . 43 4.11 Movie-watching innovation-driven co-activation patterns (MW-iCAPs). These 17 maps

(within the 20 clusters) were considered to be robust and ordered according to their occurrence rate (by descending order). . . 46 4.12 Groups’ contribution (TD in green, ASD in yellow) for each MW-iCAP, regarding the

number of innovations (positive and negative together). In each retained cluster, the number of innovation frames was computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the number of frames of each subject are also displayed. A two-sample t-test was performed for all MW-iCAPs to understand if there is a difference across groups on the contribution of frames. No significant differences were found. . . 50 4.13 Total duration of the MW-iCAPs in each group (TD in green, ASD in yellow), after

com-puting the normalized iCAPs’ time courses. In each retained cluster, the total durations of the iCAPs were computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the total durations of each subject are also displayed. A two-sample t-test was performed for all MW-iCAPs to understand if there is a difference across groups with regards to the MW-iCAPs’ total durations. No significant differences were found. . . 51 4.14 Number of occurrences of the MW-iCAPs in each group (TD in green, ASD in yellow),

after computing the normalized iCAPs’ time courses. In each retained cluster, the num-ber of occurrences of the iCAPs were computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the number of oc-currences of each subject are also displayed. A two-sample t-test was performed for all MW-iCAPs to understand if there is a difference across groups on the MW-iCAPs’ number of occurrences. No significant differences were found. . . 51 4.15 Average duration of the MW-iCAPs in each group (TD in green, ASD in yellow), after

computing the respective total duration and number of occurrences. In each retained cluster, the average durations of the iCAPs were computed for every subject, and the mean (seen in the bars) and the standard deviation (represented in the error bars) of the groups were obtained. In order to better assess the variability, the data points for the average durations of each subject are also displayed. A two-sample t-test was performed for all MW-iCAPs to understand if there is a difference across groups on the MW-iCAPs’ average durations. No significant differences were found. . . 52

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4.16 Spatial comparison across groups in MW-iCAP 17 by means of the t-maps resulting from SPM12 (uncorrected analysis with p < 0.001, clusters with 20 or more voxels were kept). This map suggests a difference around three centroids: top - (-45,+20,+37); middle - (+42,+23,+25); bottom - (+36,+29,+46) (MNI space), with the contrast “ASD > TD” that implies an increase in the patient group (ASD) compared to the control group (TD). These three areas belong to the left dorsolateral prefrontal cortex (DLPFC), right DLPFC, and anterior prefrontal cortex, respectively. . . 55 4.17 Axial (z = +23) and sagittal (x = +25) views of the group-specific MW-iCAPs 17

(z-normalized), with TD in the top and ASD in the bottom. These maps were generated with the retained innovation frames from each group, for MW-iCAP 17. The extension of the ASD-specific MW-iCAP 17 is larger and more intense than the TD-specific MW-iCAP 17 in this frontal area. . . 56 4.18 Cross-subject average activity in MW-iCAP 17 for the two populations (TD in green,

ASD in yellow). This was computed by considering the three stages of the time courses as baseline, activation, and deactivations, defining them as 0, 1, and -1, respectively, with the aid of a k-means clustering, for all subjects’ time courses and averaging these values for the two groups. The dashed red line sets the value 0.5, above which more than 50% of the individuals express the same behavior. The vertical lines determine the moments in the movie for which the ASD activity is greater than the TD activity and are above 0.5. 57 4.19 Probability to express an innovation, as a function of the movie time, for both groups

(TD in green, ASD in yellow). Thresholds of 9 TD participants and 10 ASD subjects were established, above which this chance was considered to be significant. The vertical lines point to the periods in the movie when the ASD behavior was atypical, comparing to the TD evolution. . . 59

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List of Tables

3.1 Information of the enrolled subjects, in particular their characteristics (group, age, and IQ) and the sessions of the experiment that were considered in this study. The groups are age-matched (p = 0.48). The first two sessions (S1 and S2) included movie-watching followed by resting-state and the last session (S3) consisted of merely resting-state. . . . 20 4.1 Estimation of the percentage of subjects who contributed for each iCAP (cluster) during

resting-state, as a measurement of its robustness. Rates lower than 50% were associated to noisy patterns. . . 34 4.2 Total duration (in seconds) and number of occurrences of the robust RS-iCAPs (clusters).

For each measurement, the mean (µ) and the standard deviation (σ) are presented. . . 39 4.3 Estimation of the percentage of subjects who contributed for each iCAP (cluster)

dur-ing movie-watchdur-ing, as a measurement of its robustness. Rates lower than 50% were associated to noisy patterns. . . 45 4.4 Total duration (in seconds) and number of occurrences of the robust MW-iCAPs

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Chapter 1

Introduction

The complexity of the human brain has been widely investigated thanks to many advances in computa-tional neuroscience. Until very recently, funccomputa-tional connectivity (FC), the statistical dependence between temporal activity profiles of distinct brain regions, was assumed stationary both at rest or when perform-ing a cognitive task [1, 2, 3]; accordperform-ingly, studies conducted about brain networks would discuss average correlations across the recording period.

However, the brain is a dynamic system that presents changes even at rapid time scales, as per some of the latest developments in the neuroimaging field [4]. This time-varying nature of regional interactions has risen alert to the concept of dynamic functional connectivity (dFC).

In fact, changes in brain activity can be studied at the level of various time scales (from milliseconds to months or years), but this project will focus on functional magnetic resonance imaging (fMRI) data, which typically enable analyses at the order of seconds. Due to its good trade-off between spatial and temporal resolutions, fMRI is particularly tailored for whole-brain assessments of dFC.

Despite being a novel field, dFC has led to promising advances in the inspection of healthy individ-uals and neurological patients. A predominant part of these studies applies a sliding-window approach, considering FC measurements (such as the Pearson correlation coefficient) over consecutive windowed segments of data and resulting in a time-varying FC [5, 4, 6]. However, a distinct approach is now being exploited, where only the fMRI signal peaks that exceed a certain threshold are taken into account for the analysis – the relevant information becomes only a subset of the original data, assessed through point process analysis(PPA) [7].

Inspired by PPA and deconvolution methods, a recent sparsity-promoting framework, named total activation (TA), was proposed to deconvolve the fMRI signal based on prior knowledge of the hemody-namic response function [8]. One of the outputs of the TA framework is the innovation signal, which describes activity changes over time (transients, or innovations) in the brain. Upon subsequent cluster-ing, innovation-driven co-activation patterns (iCAPs) [9] can be extracted, and stand for sets of regions that jointly change in activity level over time. This technique has already shown its potential to uncover particularly accurate resting-state networks (RSNs) in healthy individuals, but remains to be applied in task-based settings, and to decipher dFC alterations in disease.

Currently, the TA/iCAPs pipeline undergoes continuous methodological developments at the Medical Image Processing Laboratory (MIP:Lab). Our project, conducted in MIP:Lab over a period of nine months, aimed to complement those extensions by applying the current framework to a dataset combining a naturalistic paradigm – movie-watching (MW) – and resting-state (RS) periods. This was done on both typically developing controls (TD) and individuals diagnosed with autism spectrum disorders (ASD). Thus, our goal was a joint investigation of dynamic activity changes across states (RS/MW) and groups

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(TD/ASD).

Through this dissertation, which is divided into 5 chapters, a better explanation of this project will be provided. The structure of this thesis is organised as follows: In Chapter II, a theoretical overview on fMRI and brain dynamics is provided, as well as the relevant literature relating to the analysis of dFC. In Chapter III, more details about the procedure applied to scrutinise our dataset are given, ranging from participants, fMRI acquisition or preprocessing, to the main methodology – TA and iCAPs, as well as subsequent analyses. In Chapter IV, we present and discuss our results. Finally, in Chapter V, we draw the main conclusions from our work, and suggest some avenues for future research.

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Chapter 2

Background

One of the most exciting prospects about the human brain is our growing ability to probe and characterize its functions. To accomplish it, many efforts and advances in medical signal and imaging techniques have been made during the last decades and, with the developments in modern neuroscience, new insights into the organization of the in vivo human brain were enabled.

Neuroimaging techniques mainly fall into two broad categories: structural imaging, where the anatomical architecture of the brain is investigated, and functional imaging, where its temporal patterns of activity are recorded to gain understanding about how functional brain centers operate and influence each other. The first case encompasses, for instance, computerized tomography (CT) [10] and magnetic resonance imaging (MRI) [1]; regarding functional methods, functional magnetic resonance imaging (fMRI) [1], positron emission tomography (PET) [11] and electroencephalography (EEG) [1] are some examples.

When one talks about the nervous system in general, network theory can be applied, since our brain is a very large and efficient network. From a structural point of view, the human cerebral cortex contains approximately 1010neurons, organized into a complex mesh of local circuits and long-range pathways [12]. Within this network, each region sustains specific functions, and all areas continuously process, transport, and share information between each other.

This interaction between brain areas that work in parallel is termed brain connectivity [13]. One can distinguish structural (or anatomical) and functional connectivity, two related notions [14]. Structural connectivity (SC) refers to white matter physical connections, i.e., axonal bundles linking two remote brain areas to enable synaptic communication [13]. In general, connection patterns measured by this mean are considered static at shorter time scales (seconds to minutes) [15, 16]. Nonetheless, SC can also be understood dynamically at longer time scales (hours to days), when one refers to learning or development [17]. With the emergence of diffusion tensor imaging (DTI), researchers and clinicians could characterize SC of a living human brain [15, 16, 18]. Functional connectivity (FC) [1, 2, 3] denotes the statistical dependence (correlation/covariance, spectral coherence, or phase-locking) between the functional activity of different regions of the brain, which might also be remotely located. FC measures statistical interdependence without explicit reference to causal effects and was recently shown to be varying in time, even at short time scales (second to minutes) [4]. As already mentioned, one of the main ways to assess FC is the use of fMRI, either in resting-state (RS), i.e., while the subject is not doing anything in particular, or in task-based studies [19, 20, 21].

The present work is based on the study of brain activity, and so revolves around the use of functional connectivity methods. More specifically, we employ fMRI, described in details below.

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2.1 Principles of functional magnetic resonance imaging

2.1.1 Basics of fMRI data

Functional magnetic resonance imaging (fMRI) is a non-invasive method that indirectly measures neu-ral activity. As the designation suggests, fMRI uses a magnetic resonance imaging (MRI) scanner for detecting dynamic patterns of activity in the brain. Thanks to neurovascular coupling, neural activations induce the amount of cerebral blood flow and oxygenated hemoglobin concentration (see more details in paragraph 2.1.2). This is recorded as a signal increase in the T2*-weighted fMRI images. For these reasons, fMRI represents an indirect measure (or proxy) of brain activation.

Further, while the standard structural MRI acquisition provides one three-dimensional brain volume, fMRI yields a series of brain volumes in time, to investigate how brain activity temporally evolves. In this sense, the fMRI technique offers a way of acquiring T2*-weighted images repeatedly, separated by a repetition time (TR) of typically a few seconds. This is represented in Figure 2.1, which shows an example of a T1-weighted structural scan and of T2*-weighted functional images.

With this method, a large amount of data is acquired, which represents a possible concern for pro-cessing and analytical tools. However, it can also be considered a benefit, enabling the achievement of a satisfying trade-off between spatial and temporal resolution [14].

Figure 2.1: Example of a structural T1-weighted image (left) and of T2*-weighted functional images (right) under two different conditions (A and B). These functional images are a series of three-dimensional volumes, separated by a repetition time (TR), which results in four-dimensional data (from [22]).

2.1.2 BOLD hemodynamic response

The Blood Oxygenation Level-Dependent (BOLD) contrast is the most popular approach in fMRI studies [23]. This signal, which was introduced in the early 1990s [24, 25], is measured through T2*-weighted images.

It is known that neuronal activity is accompanied by changes in regional cerebral blood flow [26, 27]. When neurons are highly activated in a certain brain region, a rise in energy demand occurs and, consequently, blood oxygenation is altered as well. The net effect is a decrease of the deoxyhemoglobin

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(dHb) over oxyhemoglobin (HbO2) ratio. Since dHb possesses paramagnetic properties and T2* is sensitive to local inhomogeneities of the magnetic field (the T2* signal intensity increases as the dHb decreases), these activity-related changes can be measured with T2*-weighted imaging. The fact that a functional brain characterization, even if indirect, can be obtained non-invasively thanks to the properties of dHb – an endogenous contrast agent – represents a major advantage of this technique.

The slight change in the magnetic resonance (MR) signal after a brief period of neural activity de-pends on the hemodynamic response [28]. Studies by Friston et al. [29] and Buxton et al. [30] are landmark attempts at modelling this response. The outcome is known as the hemodynamic response function(HRF), and can be mathematically understood as the transfer function of the system transform-ing a pattern of neural activity into its related BOLD response [31]. Figure 2.2 shows this relationship between the stimulus and the HRF for both block and event-related designs.

Figure 2.2: Representation of the predicted BOLD response (bottom) modeled as the convolution of the stimulus function (top) and the HRF (middle) for block (left) and event-related (right) designs. In a block design, two or more conditions are alternated into extended time intervals (or blocks), allowing the comparison between them. By contrast, event-related stimuli are short discrete events, and their presentation can be randomized (from [22]).

2.2 Analysis of brain dynamics

In the last two decades of the 20th century, the human brain mapping field has undergone enormous growth, mostly made possible by pioneering fMRI studies [32, 33]. In particular, the neuronal underpin-nings of a wide array of cognitive tasks could be determined [34, 35, 36], and the brain networks at play during RS could be unraveled [37, 4, 38].

Most of the referred studies made the limiting assumption that FC during the entire scan is stationary (i.e., the strength of interactions between regions is constant across time), which provides a convenient framework to interpret results [39, 40, 41]. Although this stationary approach is suitable when it comes to large-scale properties, recent investigations have demonstrated that the brain is highly dynamic, not only throughout development [42] and aging [43], but also with cognitive processes that occur on time scales of a typical fMRI scan (with attention tasks [44, 45], for instance, and upon RS [4]). With these new studies challenging the stationary assumption, different approaches for studying time-varying patterns of connectivity across the whole brain appeared (see Figure 2.3 for an illustration). This so-called dynamic

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functional connectivity(dFC) can be assessed both during task-based and resting-state fMRI, which are separately detailed below.

Figure 2.3: At the top, the graph representation of brain connectivity is obtained by computing FC over the entire scanning time (thus assuming its stationarity). In contrast, the three rows at the bottom illustrate possible changes over time in FC, computed over subsequent temporal windows: in terms of magnitude (time window 1), sign (time windows 2 and 3), and lost/gained connections (time window 3). In all schemes, positive and negative FC are denoted by red and blue edges, respectively (from [39]).

2.2.1 Task-based functional magnetic resonance imaging

Most of what is known about brain function involves studies that analyze the neuronal response to a certain task or stimulus. With respect to the allocation of different functions to the different human brain compartments, task-based fMRI is an extremely useful approach, in which individuals are asked to (i) process different types of information (e.g. words [46], letters [47], music [48], and movies [49]), (ii) use different kinds of cognitive skills (e.g. working memory [47] and decision making [50]), or (iii) respond in different ways (e.g. button press [47]).

In a typical setting, the BOLD signal is compared between a condition in which a subject performs the assessed paradigm, and a control baseline state [51]. While locating the specific foci of activation for a given paradigm clearly enhances our knowledge of human brain organization [52, 53, 44, 54], there are some caveats to this approach: first, subjects may all perform a given task differently, leading to difficulties in interpreting the obtained results [55]. Second, it may be too limiting to consider that only one pattern of activation occurs during a task: rather, interactions between brain regions are expected to constantly change.

Building upon the assumption that even a simple task involves complex and transient interactions, several groups have recently explored such dynamic changes. For instance, a working memory paradigm was assessed in 344 healthy individuals, showing that the involved networks are within frontoparietal

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and frontotemporal systems, and that the working memory condition is related to a reconfiguration in frontal and frontal-related areas [56]. A rearrangement over time was also demonstrated during narrative comprehension, but within the default mode network (DMN) [57] (a network composed by the posterior cingulate cortex, the precuneus, and the medial frontal and inferior parietal regions, functionally linked): using an inter-subject functional correlation approach, transitory configurations of DMN connectivity occurring along the narrated story could be isolated. Lastly, brain dynamics was also investigated in the context of a simple attention task: for a prolonged period, subjects were asked to continuously tap their finger at a constant rate, which requires continued attention [58, 59]. By this mean, attentional behavior could be related to an increase in DMN activity [45].

2.2.2 Resting-state functional magnetic resonance imaging

Despite the existence of task-based studies, the brain is already very active without the presence of any external stimulus [60, 61], which calls for the study of spontaneous brain activity. An exceptional win-dow of opportunity has been opened with the appearance of RS techniques, avoiding the long acquisition settings plaguing task-based experiments. Further, the absence of overt task or stimulation, and thus the lack of possibly hard-to-parse instructions, enables to monitor normally unconsidered clinical popula-tions that might not be as responsive as healthy controls when performing a task [62].

Resting-state fMRI (RS-fMRI) typically captures the low frequency fluctuations (< 0.1Hz) of the BOLD signal. It was first used by Biswal et al. [63], who analyzed the moments of baseline activity from a task-based experiment. The RS time course of a seed region of interest in the left motor cortex was correlated with the RS time courses of all other brain voxels, showing a high correlation between the left and right motor cortices. The discovery of FC between these regions, without any external stimulus, was then extended by later publications to bring about the existence of several strongly functionally linked resting-state networks (RSNs) [37, 64, 65]. In reality, a large part of the FC studies carried out has stressed a coherence in RS BOLD fluctuations from regions with similar functions (e.g., the left and right somatomotor cortices [63, 64, 66], as well as visual, auditory, and language systems [66]).

On top of this exquisite spatial complexity in brain activity patterns, the mind wandering state char-acterizing RS protocols is in essence a dynamic flow of thoughts; it is then unsurprising that recently, temporal patterns of RSN expression and interaction were put forward. The dynamic association between the posterior cingulate cortex (PCC), which is part of the DMN, and the task-positive network (TPN) was investigated by Chang and Glover [4]. With the use of a time-frequency analysis, changes over time and frequency were revealed between the PCC and the TPN. In addition, a comparison between anesthetized macaques and awake human individuals was conducted, indicating that the oculomotor network presents different activity levels over time during both awake and anesthetized states [67]). Despite the absence of any task or stimulus, the brain presents well structured patterns spanning different areas and is still very complex and dynamic [38]. More examples illustrating nonstationary brain activity at rest will be further discussed (in Methods for the investigation of dynamic functional connectivity).

2.2.3 Task-rest comparison

From the last two sections, we can easily perceive the differences between task-based and resting-state fMRI. Even so, the knowledge of how RS and task-based functional connectivity patterns relate is a very interesting and useful area to study. More specifically in the dFC field, the correlation between both settings can even be seen as a means of validating the results [40, 68].

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was the subject of a study by Chen et al. [69]. The DMN is one of the most notable RSNs and presents greater neural activity during rest than when performing a task. Although this network’s activity during active tasks is suspended or reduced, the DMN appears operational during a certain sub-kinds of cog-nitive tasks, such as working memory. These findings [70, 71] lead us to understand that task-evoked brain networks reflect the underlying intrinsic activity and also that the DMN relates to cognitive abili-ties, such as the integration of cognitive and emotional processing [71] and monitoring the world around us [72]. These mentioned particularities make the DMN of special interest in the analysis of cognitive dysfunctions in neurological and psychiatric disorders [20, 73, 74, 5].

Moreover, Smith et al. [65] reported that RSNs are also continuously and dynamically involved in cognitive processes. The extraction of the major functional brain networks and also, of their sub decom-posed networks, allowed the conclusion that RS and task-related brain dynamics show a correspondence [75].

Based on the literature, there is obviously a relationship between task-based and RS activity; how-ever, the precise link and interaction between both is still subject of study and speculation, as well as its potential as a biomarker of neuronal disorders.

2.2.4 Dynamics alterations in disease

Dynamic FC has already showed its noticeable potential as a framework to study brain function, despite being a very recent concept. A considerable number of methodological approaches appeared in the last couple of years, allowing preliminary studies about both the healthy and diseased brain. Since dynamic characteristics in the brain manifest differences that can be measured in disease, clinical applications of dFC are being explored. These studies include, for instance, disorders such as schizophrenia [76, 6], major depressive disorder (MDD) [77], and earlier stages of Alzheimer’s disease (such as mild cognitive impairment, MCI) [78, 79, 80].

However, in this project, we aim to apply a dFC framework in order to identify specific characteristics of autism spectrum disorders (ASD) patients. With this in mind, a brief explanation of this disease, as well as recent neuroimaging research into this area, will be provided in the following subsection. 2.2.4.1 Autism spectrum disorders

Autism spectrum disorders (ASD) include a heterogeneity (“spectrum”) of symptoms with varying de-grees of severity. They are characterized by social and communication impairments, as well as repetitive behaviors and narrow obsessive interests (for a full definition: Diagnostic and Statistical Manual of Men-tal Disorders, 5thedition [81]).

The understanding of ASD has changed through the last decades, both in terms of classification and recognition. In the 1940s, Leo Kanner was the first child psychiatrist to describe what we currently know as “classic autism” [82], employing this denomination that had been previously used to describe schizophrenia by Bleuler [83]. In fact, the term “autism” comes from the Greek autos, which means “self” and fits the symptomatology of this disorder. The key components of ASD – social communica-tion difficulties and narrow interests/repetitive behaviors – have been identified since then, including, for instance, the odd and intense disinterest in others, aloneness, difficulties when trying to interpret social contexts, spinning of the body, obsessional interests, and need for sameness. In parallel, Hans Asperger described a different type of children, who had difficulties in social integration, non-social communica-tion skills or empathizing, among others [84]. Asperger first called it “autistic psychopathy”, but this condition would then be recognized as “Asperger syndrome”.

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At the time, autism was presumed to be categorical – one would be autistic or not. It was only in the 1980s that Lorna Wing proposed that autism was a spectrum condition. This brought fundamental implications in the rate of occurrence of this disorder: thirty years ago, the rate was 4 children in every 10000, but after Wing’s reformulation, it was 10–20 in every 10000 [85].

This concept has been evolving up to the present and, nowadays, studies have found an incidence of up to 120 per 10000 children, worldwide [86]. It is true that the criteria used to diagnose ASD are completely different from the original ones, which could partly explain the increase. However, the body of neuroscientists that are focused on this matter are conscious that this is not the only factor, given the atrocious growth in the last few years. Several theories, both genetic- and environment-based, have tried to explain the source of these patients’ behavior [87, 88], but the complexity of ASD makes it challenging to draw decisive conclusions.

The study of the autistic subjects’ brain is an obvious step in the understanding of the mechanisms underlying this disease, but often involves invasive methods or post-mortem analysis. In this sense, the use of neuroimaging studies represents a valuable opportunity to explore the complexity of ASD. Based on tasks that are challenging for autistic individuals, fMRI studies can lead to a better comprehension of the areas that are related to these functions, contrasting with healthy controls. Moreover, studies with respect to the intrinsic spontaneous activity of the brain are also very common to compare ASD and typically developing subjects.

The outcomes from different approaches are fairly variable, as examined by Nair et al. [89]. How-ever, and despite the complexity of this disorder, some interesting and congruent results back up ASD theories and their symptoms. A good example of this is the impact of the disease on the “social brain” [90], a domain that has been extensively targeted both during tasks and rest. The PCC – the central part of the DMN whose functions are still considerably unclear [91], but known for playing an important role as a cortical hub [12] and in cognition (discussed in section 2.2.3) – presents functional abnormalities in ASD. For instance, Kennedy et al. [92] analyzed ASD and non-ASD age-matched individuals, resort-ing to a countresort-ing Stroop task [93] and periods of rest, and noticed an abnormal lack of deactivation of the PCC in autistic participants when performing the task. This study hypothesized a link between the irregularities in ASD resting-state activity and their obsessive interests and/or a hypersensitivity to the external environment. This deactivation was also reported in another region of the DMN, the ventral me-dial prefrontal cortex (VMPFC), which was correlated with a clinical measurement of social impairment. Another interesting study conducted by Chiu et al. [94] relates the defects in the PCC to the severity of the disease, identified in high-functioning males with ASD during a task to assess social interactions. The angular gyrus (which also belongs to the DMN) is another brain region involved in social cognition [95]. Kennedy & Courchesne [96] verified the occurrence of abnormal rates of connectivity in the left angular gyrus in patients with ASD, as well as in the medial prefrontal cortex (MPFC).

Still in the context of social impairments, Pinkham et al. [97] employed the Trustworthi-ness/Approachability Task [98] and a secondary age task [99] in order to evaluate face processing and social cognitive processes in both ASD and paranoid schizophrenia, when compared to control individ-uals. The results showed significant reductions of activation in the amygdala, the fusiform face area (located in the fusiform gyrus), and the ventrolateral prefrontal cortex (VLPFC) in autism.

A more recent RS study involving this topic was designed by Gotts et al. [100], and aimed at under-standing if the main functional differences in autistic adolescents were present in the social brain areas. Applying an innovative whole-brain connectivity approach, reduced connectivity of social processing-related areas was indeed found, reflecting the spread localization of impaired regions in ASD.

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of the “mirror neuron system” (MNS) [101], which is defined by Rajmohan & Mohandas [102] as “a group of specialized neurons that mirrors the actions and behavior of the others”. The MNS is essential for developing empathy and communicating, due to the engagement of imitation in these functions, involving brain regions such as the insula, the superior temporal sulcus (STS), the VMPFC, the inferior parietal lobule (IPL), the inferior frontal gyrus (IFG), and the anterior cingulate cortex (ACC) [103]. An example of MNS malfunction in ASD was evidenced by Dapretto et al. [104], with a study in which high-functioning autistic children were compared to matched controls in the imitation and observation of emotional expressions. One of the findings of this study was the absence of activation in the inferior frontal gyrus for autistic participants, indicating a problem in one of the MNS areas since childhood. In another MNS anatomical study carried out by Hadjikhani et al., [105], autistic individuals also presented a significant thinning in the inferior frontal cortex (IFC), the IPL, and the right STS.

Since the present project is based on dFC, it is also important to mention that dynamic investigations have already been applied in ASD. Yao et al. [106] used a RS time-varying connectivity analysis that included a group independent component analysis (ICA) and a k-means clustering. According to their results, ASD children present an aberrant ratio of connectivity at the whole-brain level, which is believed to relate to the aforementioned “social brain”. A part of these outcomes is highlighted in Figure 2.4, which contains regions that presented lower connectivity in ASD, such as different portions of the DMN (top) and related to visual networks (bottom). This same study showed significantly abnormal ASD connectivity in the MPFC and superior temporal gyrus. According to Kana et al. [107], these regions are related with implicit emotion processing, which may be reflected in the autistic subjects’ social abilities.

Figure 2.4: Examples of spatial distributions of independent components with abnormal connectivity in autistic subjects. Top: left superior middle frontal gyrus (red) and precuneus/posterior cingulate gyrus (blue); bottom: right fusiform gyrus (red) and left middle frontal gyrus (blue) (adapted from [106]).

Other recent investigations also highlight dFC as a particularly promising biomarker of ASD, such as the RS-fMRI work of Price et al. [108]. In this study, RSNs were obtained by a combined ICA and sliding-window connectivity approach (techniques further discussed in section 2.3), followed by the use of a multiple kernel support vector machine, in order to improve the diagnosis of the disorder. Their results showed that the inclusion of dFC improved ASD classification.

As it transpires from the literature, the dFC field is an emerging, and extremely promising, novel way to address brain disorder-related specificities, in particular regarding ASD. The focus is now set on the

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improvement of analytical pipelines, so that information that is as insightful and accurate as possible can be extracted and analyzed.

2.3 Methods for the investigation of dynamic functional connectivity

This section aims to review work from the literature of relevance for the analysis of brain functional networks within a dynamic framework. Although the concept of dFC is fairly recent (the first dFC study dates back to 2010 [4]), the expansion of new methodologies has been very rapid due to its potential. Through the following points, we will cover the main relevant methodological axes pursued in the recent years.

2.3.1 Sliding window analysis

Concerning the use of fMRI to study brain dynamics, the sliding window approach is the most common strategy, applied to many studies in different contexts. For instance, this method was implemented in order to relate the brain activity to cognitive processes [109, 110, 111] and to other functional [112, 113] and structural brain measures [114]. Furthermore, focusing on clinical applications, this basic framework was employed in several task-based and resting-state fMRI studies, connecting changes in dFC to specific brain disorders [6, 78, 79, 80, 108, 106, 115].

This strategy (Figure 2.5) is characterized by considering temporal intervals of fixed duration (win-dow length W), within which data points (from time t = 1 to time t = W ) are employed to compute the connectivity between each pair of time courses, using for example Pearson correlation coefficient. The window is then shifted by a fixed number of data points (step T ) – that defines the amount of overlap between consecutive windows – and the process is repeated over the time interval [1 + T, W + T ]. This process is iterated until it reaches the full duration of the scan, resulting into a connectivity time course. If we consider all regions of the brain and compute dFC for each pair of them, we obtain a dynamic func-tional connectome, i.e. a set of connectivity matrices (one per window) that represents the time-varying behavior of the whole brain.

This technique is widely used to capture relevant dFC phenomena due to its simplicity; nonetheless, this characteristic also constitutes a limitation. The selection of the window size (W ) is still a pertinent issue, since, with a too short W , there is a greater chance of introducing spurious information [39, 116] and, in contrast, the detection of relevant temporal variations might be compromised if a too large W is chosen. In order to find answers to this concern, Leonardi and Van De Ville [116] recently proposed that the use of a W ≥ _f1

min, where fminrepresents the cut-off frequency of the high-pass filter applied to the BOLD signal. However, even with some advances in this sense, the choice of a proper window length remains unclear, and also an user-defined parameter that might influence the results of the analysis.

Another important issue that arises from sliding-window analysis is the fact that physiological noise in general [117, 118, 119] is commonly non-stationary and introduces FC changes over time. Also, other randomly generated signals (such as white noise) can introduce errors, since their fluctuations are similar to those that are actually contained in fMRI data. This type of problems raise the need of appropriate statistical testing of dFC fluctuations, comparing the observed fluctuations to proper null data in which dFC has been dismantled while noise sources remain [120, 121]). In conclusion, one can say that, with proper processing, modeling and statistical testing, this framework is a worthwhile tool for the study of dFC (Hutchison et al. [39]). However, the following paragraphs will focus on various alternatives that meet the referred concerns.