On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology

Brain Microstructure: Impact of the Permeability on Diffusion MRI

Diffusion Magnetic Resonance Imaging (dMRI) enables a non invasive in-vivo characterization of the brain tissue. The disentanglement of each microstructural property reflected on the total dMRI signal is one of the hottest topics in the field. The dMRI reconstruction techniques ground on assumptions on the signal model and consider the neurons axons as impermeable cylinders. Nevertheless, interactions with the environment is characteristic of the biological life and diffusional water exchange takes place through cell membranes. Myelin wraps axons with multiple layers constitute a barrier modulating exchange between the axon and the extracellular tissue. Due to the short transverse relaxation time (T2) of water trapped between sheets, myelin contribution to the diffusion signal is often neglected. This thesis aims to explore how the exchange influences the dMRI signal and how this can be informative on myelin structure. We also aimed to explore how recent dMRI signal reconstruction techniques could be applied in clinics proposing a strategy for investigating the potential as biomarkers of the derived tissue descriptors. The first goal of the thesis was addressed performing Monte Carlo simulations of a system with three compartments: intra-axonal, spiraling myelin and extra-axonal. The experiments showed that the exchange time between intra- and extra-axonal compartments was on the sub-second level (and thus possibly observable) for geometries with small axon diameter and low number of wraps such as in the infant brain and in demyelinating diseases. The second goal of the thesis was reached by assessing the indices derived from three dimensional simple harmonics oscillator-based reconstruction and estimation (3D-SHORE) in stroke disease. The tract-based analysis involving motor networks and the region-based analysis in grey matter (GM) were performed. 3D-SHORE indices proved to be sensitive to plasticity in both white matter (WM) and GM, highlighting their viability as biomarkers in ischemic stroke. The overall study could be considered the starting point for a future investigation of the interdependence of different phenomena like exchange and relaxation related to the established dMRI indices. This is valuable for the accurate dMRI data interpretation in heterogeneous tissues and different physiological conditions

Probing brain microstructure with multidimensional diffusion MRI: Encoding, interpretation, and the role of exchange

Diffusion MRI (dMRI) is a non-invasive probe of human brain microstructure. It is a long-standing promise to use dMRI for ‘in vivo histology’ and estimate tissue quantities. However, this faces several challenges. First, the microstructure models used for dMRI data are based on assumptions that may cause erroneous interpretations. Also, probing neurites in gray matter assumes high microscopic diffusion anisotropy in both axons and dendrites, which is not supported by evidence. Furthermore, dMRI data analysis typically ignores diffusional exchange between microscopic environments. This thesis investigates and addresses these challenges using ‘multidimensional’ dMRI techniques that vary additional sequence encoding parameters to obtain new information on the tissue. In Paper I, we optimized an acquisition protocol for filter exchange imaging (FEXI). We found slow rates of diffusional exchange in normal brain tissue. In patients with gliomas and meningiomas, faster exchange was tentatively associated with higher tumor grade. In Paper II, we used tensor-valued diffusion encoding to test the NODDI microstructure model. The NODDI assumptions were contradicted by independent data and parameter estimates were found to be biased in normal brain and in gliomas. The CODIVIDE model combined data acquired with different b-tensor shapes to remove NODDI assumptions and reduce the susceptibility to bias. In Paper III, we used tensor-valued diffusion encoding with multiple echo times to investigate challenges in estimating neurite density. We found that microscopic anisotropy in the brain reflected axons but not dendrites. We could not separate the densities and T2 values of a two-component model in normal brain, but we did detect different component T2 values in white matter lesions. Microstructure models ranked regions from normal brain and white matter lesions inconsistently with respect to neurite density. In Paper IV, we optimized an acquisition protocol for tensor-valued diffusion encoding with multiple echo times. The data allowed removing all assumptions on diffusion and T2 relaxation from a two-component model. This increased the measurable parameters from two to six and reduced their susceptibility to bias. Data from the normal brain showed different component T2 values and contradicted common model assumptions. In Paper V, we used tensor-valued diffusion encoding in malformations of cortical development. Lesions that appeared gray matter-like in T1- and T2-weighted contrasts featured white matter-like regions with high microscopic diffusion anisotropy. We interpreted these regions as myelin-poor white matter with a high axonal content. By primarily reflecting axons and not dendrites or myelin, microscopic anisotropy may differentiate tissue where alterations to myelin confound conventional MRI contrasts. In Paper VI, we used SDE with multiple diffusion times in patients with acute ischemic stroke. Subacute lesions exhibited elevated diffusional exchange that predicted later infarction. MD reduction was partially reversible and did not predict infarction. Diffusional exchange may improve definition of ischemic core and identify additional patients for late revascularization

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications

Double diffusion encoding and applications for biomedical imaging

Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI

Micro-structure diffusion scalar measures from reduced MRI acquisitions

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP). In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust

Mapping microstructural dynamics in a mouse stroke model using advanced diffusion MRI

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), Universidade de Lisboa, Faculdade de Ciências, 2020O acidente vascular cerebral (AVC) corresponde a uma das principais causas de morte e invalidez a nível mundial, sendo o AVC isquémico o mais predominante, perfazendo mais de 80% dos casos. Este traduz-se no bloqueio de um ou mais vasos sanguíneos devido à formação de coágulos, comprometendo a oxigenação no local e o fornecimento de nutrientes como consequência da redução do fluxo sanguíneo. O único tratamento aceite para o AVC baseia-se na administração de agentes trombolíticos. Porém, a sua aplicabilidade é muito reduzida pois o intervalo de tempo exigido para a sua atuação é demasiado curto (menos de 4 horas desde a última vez em que o sujeito se apresentou assintomático). De momento, o desenvolvimento de técnicas inovadoras encontra-se dependente de um conhecimento mais aprofundado do tecido envolvente no decorrer do acidente isquémico. Posto tal, as técnicas de neuroimagiologia de cariz não invasivo, como a imagem por ressonância magnética (IRM), apresentam um papel crucial na investigação nesta área. A importância da ressonância magnética de difusão (dMRI, do inglês diffusion MRI) tem vindo a ser cada vez mais favorecida, especialmente na deteção do enfarte e no estudo do microambiente na zona respetiva. Contudo, as métricas convencionais de dMRI apenas disponibilizam informação relativa, no máximo, a um sumatório de efeitos não Gaussianos da difusão da água nos tecidos, o que a torna numa técnica consideravelmente inespecífica. Esta falta de especificidade pode então refletir-se numa incorreta caraterização do núcleo da lesão, de tecido recuperável e da resposta ao tratamento, no sentido em que os mecanismos subjacentes ao contraste de dMRI obtido são desconhecidos. No âmbito deste trabalho, de forma a aumentar a sensibilidade e especificidade da dMRI na informação obtida em contexto de AVC isquémico, a metodologia de imagem por contraste do tensor de correlação (CTI, do inglês Correlation Tensor Imaging), desenvolvida no nosso laboratório, foi aplicada num modelo de AVC isquémico em murganho (Mus musculus). A CTI permite a obtenção de informações mais específicas acerca de efeitos de difusão provenientes das fontes de curtose (relativos a anisotropia, dispersão na orientação, variância na difusão nos tecidos e propriedades não Gaussianas). Esta técnica baseia-se na expansão cumulativa do sinal adquirido em sequências avançadas de codificação de difusão dupla (DDE, do inglês Double Diffusion Encoding), em que ambas as codificações podem ser aplicadas em direções e magnitudes independentes (caraterizadas por vetores independentes q1 e q2). Todos os procedimentos em animais foram previamente aprovados pelas autoridades nacionais e internacionais competentes, e foram realizados de acordo com a Diretiva EU 2010/63. Murganhos macho C57BL/6J (N = 12; 26,4 ± 6,5 g) com 11 semanas foram utilizados. O modelo fototrombótico de Rose Bengal foi usado de forma a induzir um enfarte focalizado na região do cortéx de barril com uma solução de um corante fotossensível (15 mg/ml). Em animais do grupo experimental (N = 5), a solução foi administrada de forma intravenosa (10 μl/g peso do animal) e a zona cortical de interesse foi irradiada com uma fonte de luz fria durante 15 minutos. Os animais do grupo de controlo (N = 5) foram submetidos a procedimentos idênticos, à exceção da irradiação responsável pelo desencadeamento da lesão, mantendo o tempo de espera suposto. Os cérebros do grupo de AVC e do grupo de controlo foram perfundidos 3 h após o fim do período com ou sem iluminação e utilizados para aquisições ex vivo. Além disso, dois animais foram usados para examinação in vivo: um submetido à indução do AVC para validação com os resultados ex vivo, e outro animal saudável foi usado para efeitos corroborativos com estudos anteriores. Os cérebros foram examinados no scanner Aeon da Bruker de 16.4 T. As imagens por difusão ex vivo foram adquiridas com sequências de DDE com base em EPI (do inglês Echo Planar Imaging) escritas in house. Usou-se um protocolo com um total de 5 combinações q1-q2 de magnitudes diferentes (1498,0 – 0; 1059,2 – 1059,2; 1059,2 – 0; 749,0 – 749,0 e 749,0 - 0 mT/m), associando cada combinação a uma aquisição. Para combinações em que as magnitudes eram iguais entre vetores, foram usadas 135 direções paralelas e perpendiculares, resultando em 7 aquisições com valor b total de 750, 1500 ou 3000 mm/s2 . Os restantes parâmetros foram: TR/TE = 3000/49 ms, FOV = 11 × 11 mm2 , matriz = 78 × 78, resolução de voxel no plano de 141 × 141 μm2 , 25 fatias coronais com espessura de 0,5 mm, δ/Δ/τm = 1,5/10/10 ms e 2 segmentos. 20 imagens foram adquiridas sem gradientes de difusão aplicados (i.e., b = 0 mm/s2 ). Cada examinação durou aproximadamente 14 h na totalidade. No caso das aquisições in vivo, os animais foram sedados (isoflurano 2,5%) e examinados no scanner Biospec da Bruker de 9.4 T. As imagens por difusão foram adquiridas com um protocolo otimizado em comparação com o utilizado nas sequências ex vivo, diferindo nos seguintes parâmetros: as combinações q1-q2 de magnitude foram de 518,79 – 0; 366,84 – 366,84; 366,84 – 0; 259,4 – 259,4 e 259,4 - 0 mT/m. As combinações de direções foram idênticas às utilizadas nas aquisições ex vivo, resultando também em 7 aquisições com valor b total de 625, 1250 ou 1500 mm/s2 , TR/TE = 2800/44,5 ms, FOV = 12 × 12 mm2 , matriz = 78 × 78, resolução de voxel no plano de 181 × 181 μm2 , 5 fatias coronais com espessura de 0,85 mm, δ/Δ/τm = 4/10/10 ms e 1 segmento. Cada examinação durou aproximadamente 1 h e 15 min. O processamento dos dados englobou a correção do sinal ao nível do ruído, alinhamento dos dados e correção ao nível de Gibbs ringing. As métricas de difusão convencionais, tais como a difusão média, a anisotropia fracional, a difusão radial e axial (MD, FA, RD e AD, respetivamente) foram extraídas pelo tensor de difusão (D). No âmbito das fontes resolvidas pela CTI, a fonte de curtose excedente total (KT) foi obtida a partir de D e do tensor de curtose (W), as curtoses anisotrópica e isotrópica ( e ), ditas fontes intercompartimentais, foram extraídas a partir do tensor D e do tensor de correlação do deslocamento Z (de quarta ordem na expansão cumulativa), e a fonte de curtose intracompartimental () foi extraída a partir da subtração das fontes intercompartimentais à fonte KT. De forma a analisar os mapas obtidos para as métricas de curtose 3 h após enfarte ao nível de substância branca e cinzenta, regiões de interesse foram definidas (com base nos mapas de MD e FA) no hemisfério ipsilateral ou ipsilesional (relativo à lesão), e no hemisfério contralateral (não afetado), dos animais que sofreram o AVC. Regiões de interesse no grupo de controlo foram também definidas no hemisfério ipsilateral. Após associar os dados obtidos de cada hemisfério a diferentes subgrupos, foram realizadas comparações entre subgrupos para as diferentes métricas de curtose e uma análise ANOVA para testar diferenças significativas entre subgrupos, permitindo assim uma análise de especificidade de cada métrica aos efeitos do AVC. Foi ainda realizada uma análise da sensibilidade de cada métrica perante a lesão no hemisfério ipsilesional para todos os animais do grupo de AVC através da quantificação de voxeis sensíveis à lesão em cada animal, quer ao nível da lesão total, quer ao nível das substâncias branca e cinzenta. Uma breve análise histológica foi produzida para uma comparação qualitativa com os mapas das diferentes fontes de curtose e associação com degeneração e perda celular. Os resultados indicaram diferenças significativas (p < 0,05) para as métricas KT e entre o hemisfério ipsilateral do grupo de AVC e o hemisfério contralateral do mesmo grupo, bem como entre o hemisfério ipsilaterial do grupo de AVC e o hemisfério ipsilateral do grupo de controlo (em substância substância cinzenta e substância branca). Porém, a métrica de foi a que mais se destacou, visto ter mostrado sensibilidade para o AVC na substância cinzenta perante outras métricas. Os mapas de curtose in vivo mostraram-se consistentes com os mapas ex vivo. Em comparação com estudos anteriores, os resultados obtidos nas métricas de difusão (MD, FA, RD e AD) demonstraram congruência com a literatura, tendo os valores de KT seguido a tendência dos valores de curtose média (MK) obtidos noutros estudos de AVC em murganho. A menor sensibilidade para o AVC em KT, quando comparada com , por exemplo, sugere que certos efeitos de curtose se poderão anular, informação essa anteriormente desconhecida. Os nossos resultados, além de favorecerem maior sensibilidade comparativamente às métricas convencionais em contexto de AVC, acentuam também a especificidade de cada fonte de curtose perante o tecido isquémico, permitindo uma possível relação com mecanismos patofisiológicos a ocorrer na fase aguda-subaguda do AVC, tais como fenómenos citotóxicos e vasogénicos. Ao resolver as fontes de curtose em tecido isquémico, foi-nos possibilitada uma maior compreensão dos mecanismos microscópicos subjacentes, que, num formato mais sensível e específico, propicia uma caraterização de AVC inovadora e uma maior eficácia no tratamento associado.Stroke is a leading cause of long-term disability and death worldwide, with ischaemic infarct accounting for approximately 80% of all cases. Currently, novel treatments depend on a deeper understanding of the local tissue milieu following ischemia. Therefore, non-invasive neuroimaging plays a crucial role in stroke research. Diffusion MRI (dMRI) is one of the most reliable imaging techniques, mainly for the early detection of ischemic stroke via detection of microstructural changes. However, dMRI is critically unspecific, thereby hampering the conclusive characterization of infarct core, salvageable tissue and response to treatment. To overcome this drawback, Correlation Tensor Imaging (CTI) – a ground-breaking approach enhancing sensitivity and specificity towards tissue microstructure via the resolution of non-Gaussian diffusion sources – was applied for the first time towards the characterization of ischemic tissue (ex vivo and in vivo) and assessment of the mechanisms underlying dMRI contrasts in a mouse stroke model at an early stage post ischemic insult. In this study, a photothrombotic stroke model was used to induce a focal infarct in the barrel cortex and dMRI ex vivo datasets were acquired with CTI pulse sequences written in house. For corroboration of results, in vivo datasets were additionally acquired. The stroke model reproducibly induced well-delimited infarction cores in the targeted region in all animals. Our results suggest that CTI is capable of resolving microscopic features of ischemic tissue in-vivo, which until now were obfuscated or conflated in conventional dMRI measurements. Particularly, intra-compartmental kurtosis (), one of the resolved sources, shows higher sensitivity and specificity towards ischemic alterations when compared to other sources of kurtosis. These are critical first steps towards resolving the contributions of cytotoxic and vasogenic edema sources as well as potential for revealing salvageable tissue or ongoing excitotoxicity

Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA

Producción CientíficaAMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.Ministerio de Ciencia, Innovación y Universidades (grant RTI2018-094569-B-I00)Polish National Agency for Academic Exchange (grant PN/BEK/2019/1/00421)Ministry of Science and Higher Education of Poland (scholarship 692/STYP/13/2018)Junta de Castilla y León - Fondo Social Europeo (ID: 376062

Studying brain connectivity: a new multimodal approach for structure and function integration \u200b

Il cervello \ue8 un sistema che integra organizzazioni anatomiche e funzionali. Negli ultimi dieci anni, la comunit\ue0 neuroscientifica si \ue8 posta la domanda sulla relazione struttura-funzione. Essa pu\uf2 essere esplorata attraverso lo studio della connettivit\ue0. Nello specifico, la connettivit\ue0 strutturale pu\uf2 essere definita dal segnale di risonanza magnetica pesato in diffusione seguito dalla computazione della trattografia; mentre la correlazione funzionale del cervello pu\uf2 essere calcolata a partire da diversi segnali, come la risonanza magnetica funzionale o l\u2019elettro-/magneto-encefalografia, che consente la cattura del segnale di attivazione cerebrale a una risoluzione temporale pi\uf9 elevata. Recentemente, la relazione struttura-funzione \ue8 stata esplorata utilizzando strumenti di elaborazione del segnale sui grafi, che estendono e generalizzano le operazioni di elaborazione del segnale ai grafi. In specifico, alcuni studi utilizzano la trasformata di Fourier applicata alla connettivit\ue0 strutturale per misurare la decomposizione del segnale funzionale in porzioni che si allineano (\u201caligned\u201d) e non si allineano (\u201cliberal\u201d) con la sottostante rete di materia bianca. Il relativo allineamento funzionale con l\u2019anatomia \ue8 stato associato alla flessibilit\ue0 cognitiva, sottolineando forti allineamenti di attivit\ue0 corticali, e suggerendo che i sistemi sottocorticali contengono pi\uf9 segnali liberi rispetto alla corteccia. Queste relazioni multimodali non sono, per\uf2, ancora chiare per segnali con elevata risoluzione temporale, oltre ad essere ristretti a specifiche zone cerebrali. Oltretutto, al giorno d'oggi la ricostruzione della trattografia \ue8 ancora un argomento impegnativo, soprattutto se utilizzata per l'estrazione della connettivit\ue0 strutturale. Nel corso dell'ultimo decennio si \ue8 vista una proliferazione di nuovi modelli per ricostruire la trattografia, ma il loro conseguente effetto sullo strumento di connettivit\ue0 non \ue8 ancora chiaro. In questa tesi, ho districato i dubbi sulla variabilit\ue0 dei trattogrammi derivati da diversi metodi di trattografia, confrontandoli con un paradigma di test-retest, che consente di definire la specificit\ue0 e la sensibilit\ue0 di ciascun modello. Ho cercato di trovare un compromesso tra queste, per definire un miglior metodo trattografico. Inoltre, ho affrontato il problema dei grafi pesati confrontando alcune possibili stime, evidenziando la sufficienza della connettivit\ue0 binaria e la potenza delle propriet\ue0 microstrutturali di nuova generazione nelle applicazioni cliniche. Qui, ho sviluppato un modello di proiezione che consente l'uso dei filtri aligned e liberal per i segnali di encefalografia. Il modello estende i vincoli strutturali per considerare le connessioni indirette, che recentemente si sono dimostrate utili nella relazione struttura-funzione. I risultati preliminari del nuovo modello indicano un\u2019implicazione dinamica di momenti pi\uf9 aligned e momenti pi\uf9 liberal, evidenziando le fluttuazioni presenti nello stato di riposo. Inoltre, viene presentata una relazione specifica di periodi pi\uf9 allineati e liberali per il paradigma motorio. Questo modello apre la prospettiva alla definizione di nuovi biomarcatori. Considerando che l\u2019encefalografia \ue8 spesso usata nelle applicazioni cliniche, questa integrazione multimodale applicata su dati di Parkinson o di ictus potrebbe combinare le informazioni dei cambiamenti strutturali e funzionali nelle connessioni cerebrali, che al momento sono state dimostrate individualmente.The brain is a complex system of which anatomical and functional organization is both segregated and integrated. A longstanding question for the neuroscience community has been to elucidate the mutual influences between structure and function. To that aim, first, structural and functional connectivity need to be explored individually. Structural connectivity can be measured by the Diffusion Magnetic Resonance signal followed by successive computational steps up to virtual tractography. Functional connectivity can be established by correlation between the brain activity time courses measured by different modalities, such as functional Magnetic Resonance Imaging or Electro/Magneto Encephalography. Recently, the Graph Signal Processing (GSP) framework has provided a new way to jointly analyse structure and function. In particular, this framework extends and generalizes many classical signal-processing operations to graphs (e.g., spectral analysis, filtering, and so on). The graph here is built by the structural connectome; i.e., the anatomical backbone of the brain where nodes represent brain regions and edge weights strength of structural connectivity. The functional signals are considered as time-dependent graph signals; i.e., measures associated to the nodes of the graph. The concept of the Graph Fourier Transform then allows decomposing regional functional signals into, on one side, a portion that strongly aligned with the underlying structural network (\u201caligned"), and, on the other side, a portion that is not well aligned with structure (\u201cliberal"). The proportion of aligned-vs-liberal energy in functional signals has been associated with cognitive flexibility. However, the interpretation of these multimodal relationships is still limited and unexplored for higher temporal resolution functional signals such as M/EEG. Moreover, the construction of the structural connectome itself using tractography is still a challenging topic, for which, in the last decade, many new advanced models were proposed, but their impact on the connectome remains unclear. In the first part of this thesis, I disentangled the variability of tractograms derived from different tractography methods, comparing them with a test-retest paradigm, which allows to define specificity and sensitivity of each model. I want to find the best trade-off between specificity and sensitivity to define the best model that can be deployed for analysis of functional signals. Moreover, I addressed the issue of weighing the graph comparing few estimates, highlighting the sufficiency of binary connectivity, and the power of the latest-generation microstructural properties in clinical applications. In the second part, I developed a GSP method that allows applying the aligned and liberal filters to M/EEG signals. The model extends the structural constraints to consider indirect connections, which recently demonstrated to be powerful in the structure/function link. I then show that it is possible to identify dynamic changes in aligned-vs-liberal energy, highlighting fluctuations present motor task and resting state. This model opens the perspective of novel biomarkers. Indeed, M/EEG are often used in clinical applications; e.g., multimodal integration in data from Parkinson\u2019s disease or stroke could combine changes of both structural and functional connectivity