Large scale functional brain networks, defined by synchronized spontaneous oscillations between spatially distinct anatomical regions, are essential to brain function and have been implicated in disease states, cognitive capacity, and many sensing and motor processes. In this work, we sever the corpus callosum in the rodent model to determine if structural connectivity (specifically the primary interhemispheric pathway) organizes and influences bilateral functional connectivity and brain-wide spatiotemporal dynamic activity patterns. Prior to the callosotomy work, resting state brain networks were evaluated using blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) magnetic resonance imaging contrast mechanisms, and revealed that BOLD and CBV provide highly similar spatial maps of functional connectivity; however, the amplitude of BOLD connectivity was generally stronger. The effects of extended anesthetic durations on functional connectivity were also evaluated revealing extended isoflurane anesthetic periods prior to the switch to dexmedetomidine attenuates functional activity for a longer duration as compared to a shorter isoflurane paradigm. We also observed a secondary significant evolution of functional metrics occurring during long durations of dexmedetomidine use under the currently accepted and refined dexmedetomidine sedation paradigm. Taking these previous findings into account, we moved forward with the callosotomy study. Functional network integrity was evaluated in sham and full callosotomy groups using BOLD and electrophysiology. Functional connectivity analysis indicated a similar significant reduction in bilateral connectivity in the full callosotomy group as compared to the sham group across both recording modalities. Spatiotemporal dynamic analysis revealed bilaterally symmetric propagating waves of activity in the sham data, but none were present in the full callosotomy data; however, the emergence of unilateral spatiotemporal patterns became prominent following the callosotomy. This finding suggests that the corpus callosum could be largely responsible for maintaining bilateral network integrity, but non-bilaterally symmetric propagating waves occur in the absence of the corpus callosum, suggesting a possible subcortical driver of the dynamic cascading event. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks.

L’imagerie par résonance magnétique fonctionnelle (IRMf) est un outil de choix pour cartographier d’une manière non invasive l’activité du cortex, donnant ainsi un accès à l’organisation fonctionnelle cérébrale. Cette organisation des aires cérébrales en réseaux complexes reste encore un vaste sujet d’étude, autant dans le domaine de la recherche fondamentale, pour mieux comprendre le développement et le fonctionnement du cerveau, que dans le domaine clinique, à des fins diagnostiques par exemple. Les réseaux cérébraux dits de repos, chez un sujet donné, peuvent être observés lors d’études IRMf lorsqu’aucune tâche motrice ou cognitive n’est imposée au sujet imagé. La première partie de cette thèse a permis le développement d’une méthode automatique d’identification de ces réseaux. Réalisée à l’échelle du sujet, cette méthode permet de sélectionner tous les réseaux spécifiques au sujet ce qui s’avère nécessaire dans un cadre diagnostique où l’individu prime. Au delà de la détection et de l’identification de ces réseaux, l’étude de leurs modes d’interaction dans l’espace et dans le temps et plus généralement l’analyse de la dynamique de la connectivité fonctionnelle (DCF) fait l’objet d’un intérêt grandissant. Cette analyse nécessite le développement de méthodes innovantes de traitement du signal et de l’image qui, pour l’heure, sont encore de nature exploratoire. La deuxième partie de cette thèse présente donc de nouvelles approches pour caractériser la DCF en utilisant le cadre probabiliste de modèles de Markov cachés multidimensionnels. Les mécanismes conversationnels entre réseaux cérébraux peuvent ainsi être identifiés et caractérisés à l’échelle de la seconde. Deux applications, au niveau du sujet puis du groupe, ont permis de mettre en avant les modifications des propriétés dynamiques des interactions entre réseaux sous certaines conditions ou pathologies. / The functional magnetic resonance imaging (fMRI) is a perfect tool for mapping in a non- invasive manner the activity of the cortex, giving access to the functional organization of the brain. This organization of brain areas into complex networks remains a large topic of study, both from a fundamental research perspective, to better understand the development and function of the brain, and from a clinical perspective, for diagnostic purposes for instance. The resting-state networks in a given subject can be observed in fMRI studies where no motor or cognitive tasks are imposed to the subject. The first part of this thesis focused on the development of an automatic identification method of these networks. Performed at the subject level, this method selects all the resting-state networks proper to the subject. Beyond the detection and identification of these networks, the study of interactions between these networks in space and time, and more generally the analysis of the dynamic functional connectivity (DFC), is the subject of growing interest. This analysis requires the development of innovative methods of signal or image processing that, for now, are still exploratory. The second part of this thesis thus presents new approaches to characterize the DFC using the probabilistic framework of multidimensional hidden Markov models. Conversational mechanisms between brain networks can be identified and characterized at the resolution of the second. Two applications, first on a single subject then on a group, helped to highlight the changes of dynamic properties of interaction between networks under certain conditions or diseases.

Abstract Functional communication between brain regions is likely to play a key role in complex cognitive processes that require continuous integration of information across different regions of the brain. This makes the studying of functional connectivity in the human brain of high importance. It also provides new insights into the hierarchical organization of the human brain regions. Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. A growing number of ICA studies have reported altered functional connectivity in clinical populations. In the current work, it was hypothesized that ICA model order selection influences characteristics of RSNs as well as their functional connectivity. In addition, it was suggested that high ICA model order could be a useful tool to provide more detailed functional connectivity results. RSNs’ characteristics, i.e. spatial features, volume and repeatability of RSNs, were evaluated, and also differences in functional connectivity were investigated across different ICA model orders. ICA model order estimation had a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Notably, at low model orders neuroanatomically and functionally different units tend to aggregate into large singular RSN components, while at higher model orders these units become separate RSN components. Disease-related differences in functional connectivity also seem to alter as a function of ICA model order. The volume of between-group differences reached maximum at high model orders. These findings demonstrate that fine-grained RSNs can provide detailed, disease-specific functional connectivity alterations. Finally, in order to overcome the multiple comparisons problem encountered at high ICA model orders, a new framework for group-ICA analysis was introduced. The framework involved concatenation of IC maps prior to permutation tests, which enables statistical inferences from all selected RSNs. In SAD patients, this new correction enabled the detection of significantly increased functional connectivity in eleven RSNs. / Tiivistelmä Toiminnallisten aivoalueiden välinen viestintä on todennäköisesti avainasemassa kognitiivisissa prosesseissa, jotka edellyttävät jatkuvaa tiedon integraatiota aivojen eri alueiden välillä. Tämä tekee ihmisaivojen toiminnallisen kytkennällisyyden tutkimuksesta erittäin tärkeätä. Kytkennälllisyyden tutkiminen antaa myös uutta tietoa ihmisaivojen osa-alueiden välisestä hierarkiasta. Aivojen hermoverkot voidaan luotettavasti ja toistettavasti havaita lepotilan toiminnasta yksilö- ja ryhmätasolla käyttämällä itsenäisten komponenttien analyysia (engl. Independent component analysis, ICA). Yhä useammat ICA-tutkimukset ovat raportoineet poikkeuksellisia toiminnallisen konnektiviteetin muutoksia kliinisissä populaatioissa. Tässä tutkimuksessa hypotetisoitiin, että ICA:lla laskettaujen komponenttien lukumäärä (l. asteluku) vaikuttaa tuloksena saatujen hermoverkkojen ominaisuuksiin kuten tilavuuteen ja kytkennällisyyteen. Lisäksi oletettiin, että korkea ICA-asteluku voisi olla herkempit tuottamaan yksityiskohtaisia toiminnallisen jaottelun tuloksia. Aivojen lepotilan hermoverkkojen ominaisuudet, kuten anatominen jakautuminen, volyymi ja lepohermoverkkojen havainnoinnin toistettavuus evaluoitin. Myös toiminnallisen kytkennällisyyden erot tutkitaan eri ICA-asteluvuilla. Havaittiin että asteluvulla on huomattava vaikutus aivojen lepotilan hermoverkkojen tilaominaisuuksiin sekä niiden jakautumiseen alaverkoiksi. Pienillä asteluvuilla hermoverkojen neuroanatomisesti erilliset yksiköt pyrkivät keräytymään laajoiksi yksittäisiksi komponenteiksi, kun taas korkeammilla asteluvuilla ne havaitaan erillisinä. Sairauksien aiheuttamat muutokset toiminnallisessa kytkennällisyydessä näyttävät muuttuvan myös ICA asteluvun mukaan saavuttaen maksiminsa korkeilla asteluvuilla. Korkeilla asteluvuilla voidaan havaita yksityiskohtaisia, sairaudelle ominaisia toiminnallisen konnektiviteetin muutoksia. Korkeisiin ICA asteluvun liittyvän tilastollisen monivertailuongelman ratkaisemiseksi kehitimme uuden menetelmän, jossa permutaatiotestejä edeltävien itsenäisten IC-karttoja yhdistämällä voidaan tehdä luotettava tilastollinen arvio yhtä aikaa lukuisista hermoverkoista. Kaamosmasennuspotilailla esimerkiksi kehittämämme korjaus paljastaa merkittävästi lisääntynyttä toiminnallista kytkennällisyyttä yhdessätoista hermoverkossa.

Les aires cérébrales entretiennent des relations fonctionnelles, formant ainsi des réseaux qui peuvent être altérés dans diverses pathologies. L'étude de ces réseaux de connectivité fonctionnelle pourrait potentiellement aider au diagnostic d'un individu et au traitement de sa pathologie. À travers quatre études, nous avons montré que l'analyse en composantes indépendantes spatiale est une méthode suffisamment sensible, reproductible et spécifique pour mettre en évidence, à l’échelle individuelle et au repos, des réseaux sains et pathologiques fiables. Ainsi, le réseau pathologique sous-tendant les hallucinations acoustico-verbales permet de définir les aires cérébrales à traiter par stimulation magnétique transcrânienne. Parmi les réseaux sains, ceux qui impliquent le cortex cingulaire postérieur et le précunéus semblent profondément altérés dans les troubles de la conscience, et peuvent servir d'outil diagnostic pour distinguer le locked-in syndrome de l'état végétatif. Il est désormais possible de cartographier, à l'échelle individuelle, les relations fonctionnelles entre les aires cérébrales. L’étude à venir de la dynamique et du niveau d’activité des réseaux de connectivité fonctionnelle nous renseignera davantage sur leurs fonctions et leur implication dans la pathologie. / Brain areas are functionally connected in networks, even at rest. Since such connectivity networks could be impaired in several pathologies, they could potentially serve for diagnosis and treatment. Based on four studies, spatial independent component analysis has shown sufficient sensitivity, reproducibility and specificity to produce reliable healthy as well as pathological networks at the individual level. Therefore, the network underlying auditory hallucination could define the brain areas to treat by transcranial magnetic stimulation. Among the common resting-state networks, the ones involving the posterior cingular cortex and the precuneus seem deeply altered in disorders of consciousness, and so could be used as a diagnostic tool to distinguish the locked-in syndrome from the vegetative state. We can now map, at the individual level, the functional relationships between brain areas. Further studies on the dynamic and on the level of activity of the functional connectivity networks might provide relevant information about their functions and their involvement in the pathology.

Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long been thought that infraslow (< 0.1 Hz) fMRI signals, which have become so important to the study of brain function, might have a direct electrophysiological counterpart. If true, EEG could be positioned as a low-cost alternative to fMRI when fMRI is impractical and therefore could also become much more influential in the study of functional brain networks. Previous works have produced indirect support for the fMRI-EEG relationship, but until recently the hypothesized link between them had not been tested in resting humans. The objective of this study was to investigate and characterize their relationship by simultaneously recording infraslow fMRI and EEG signals in resting human adults. We present evidence strongly supporting their link by demonstrating significant stationary and dynamic correlations between the two signal types. Moreover, functional brain networks appear to be a fundamental unit of this coupling. We conclude that infraslow electrophysiology is likely playing an important role in the dynamic configuration of the resting state brain networks that are well-known to fMRI research. Our results provide new insights into the neuronal underpinnings of hemodynamic activity and a foundational point on which the use of infraslow EEG in functional connectivity studies can be based.

La complexité de la connaissance humaine est révèlée dans l'organisation spatiale et temporelle de la dynamique du cerveau. Nous pouvons connaître cette organisation grâce à l'analyse des signaux dépendant du niveau d'oxygène sanguin (BOLD), lesquels sont obtenus par l'imagerie par résonance magnétique fonctionnelle (IRMf). Nous observons des dépendances statistiques entre les régions du cerveau dans les données BOLD. Ce phénomène s' appelle connectivité fonctionnelle (CF). Des modèles computationnels sont développés pour reproduire la connectivité fonctionnelle (CF). Comme les études expérimentales précédantes, ces modèles assument que la CF est stationnaire, c'est-à-dire la moyenne et la covariance des séries temporelles BOLD utilisées par la CF sont constantes au fil du temps. Cependant, des nouvelles études expérimentales concernées par la dynamique de la CF à différentes échelles montrent que la CF change dans le temps. Cette caractéristique n'a pas été reproduite dans ces modèles computationnels précédants. Ici on a augmenté la non-linéarité de la dynamique locale dans un modèle computationnel à grande échelle. Ce modèle peut reproduire la grande variabilité de la CF observée dans les études expérimentales. / The complexity of human cognition is revealed in the spatio-temporal organization of brain dynamics. We can gain insight into this organization through the analysis of blood oxygenation-level dependent (BOLD) signals, which are obtained from functional magnetic resonance imaging (fMRI). In BOLD data we can observe statistical dependencies between brain regions. This phenomenon is known as functional connectivity (FC). Computational models are being developed to reproduce the FC of the brain. As in previous empirical studies, these models assume that FC is stationary, i.e. the mean and the covariance of the BOLD time series used for the FC are constant over time. Nevertheless, recent empirical studies focusing on the dynamics of FC at different time scales show that FC is variable in time. This feature is not reproduced in the simulated data generated by some previous computational models. Here we have enhanced the non-linearity of local dynamics in a large-scale computational model. By enhancing this non-linearity, our model is able to reproduce the variability of the FC found in empirical data.

[pt] O agonista do receptor do peptídeo semelhante ao glucagon 1 (GLP-1), melhora o controle glicêmico, reduz o apetite e o peso corporal, sendo usado para o tratamento de diabetes tipo 2 (DM2). Também se mostrou associado a alterações nas respostas cerebrais, relacionadas a estímulos alimentares. Entretanto, seus efeitos na conectividade funcional intrínseca do cérebro não são conhecidos. Com objetivo de melhor entender o papel do GLP-1 na conectividade intrínseca do cérebro em pacientes DM2, dados de ressonância magnética funcional (RMf) de redes do estado de repouso relevantes para o comportamento alimentar foram analisados em dois estudos. Em ambos, todas as imagens foram adquiridas após um jejum noturno (8-12 horas). O estudo 1 teve como meta investigar o efeito agudo do bloqueio de GLP-1 na conectividade funcional. Foram adquiridas imagens de RMf durante o estado de repouso, em dois dias separados, de 20 pacientes DM2 sem complicações e 20 controles saudáveis, primeiro sob infusão de solução salina e, posteriormente, sob a infusão de antagonista do receptor de GLP-1. Já o estudo 2 teve como objetivo investigar, em pacientes DM2, se haveria diferenças na conectividade intrínseca, quando comparados os tratamentos com agonista do GLP1 liraglutida e com insulina glargina. Os mesmos pacientes DM2, participantes do estudo 1, foram tratados, em ordem aleatória, por 12 semanas com liraglutida e por 12 semanas com insulina glargina. Os dados de RMf em estado de repouso foram coletados antes do início do tratamento, após 10 dias e após 12 semanas. As análises de neuroimagem foram corrigidas para múltiplas comparações com o Family-wise error, as correlações foram feitas com coeficiente de correlação de Pearson. Os resultados do estudo 1 mostraram que, durante a infusão da solução salina, pacientes DM2 apresentaram maior conectividade comparados a controles na ínsula esquerda e opérculo, relacionada à maior perda de peso, mediada pelo agonista de GLP-1 após 10 dias e 12 semanas. Além disso, a conectividade foi maior em pacientes DM2 versus controles no polo frontal, córtex frontal medial, no giro cingulado anterior e no giro paracingulado, a qual se correlacionou com menor perda de peso, mediada por agonista de GLP-1, após 10 dias (todos P(FWE) menor que 0,05). Não houve efeito da infusão do antagonista do receptor de GLP-1 ou do tratamento com agonista de GLP-1, na conectividade (todos P(FWE) maior que 0,05). Em conclusão, a conectividade basal em estado de repouso mostrou estar relacionada à mudança de peso, mediada pelo agonista do GLP-1, com maior conectividade frontal correlacionando com menos perda de peso durante o tratamento com agonista do GLP-1, enquanto maior conectividade na ínsula esquerda, correlacionou com maior perda de peso, mediada pelo GLP-1, indicando relação entre a conectividade intrínseca dessas redes e o efeito de perda de peso do tratamento com GLP-1. / [en] The glucagon-like peptide 1 (GLP-1) receptor agonist is used for the treatment of type 2 diabetes (DM2) as it improves glycemic control, reduces appetite and body weight. It is also related to altered brain responses to food stimuli, but its effects on intrinsic brain connectivity are unknown. With the goal of better understanding GLP-1 s role in the intrinsic brain connectivity of DM2 patients, functional resonance imaging (fMRI) data of resting-state networks relevant for eating behavior was analyzed in two studies. In both, all images were acquired after an overnight fast (8-12 hours). Study 1 aimed to investigate the acute effect of GLP1 blockade on functional connectivity. On two separate days, fMRI data was acquired from 20 DM2 patients and 20 healthy controls, first under saline infusion and thereafter under GLP-1 antagonist infusion. Study 2 aimed to investigate, in DM2 patients, if there were any between treatment differences in intrinsic connectivity when comparing GLP-1 receptor agonist liraglutide with insulin glargine. The same DM2 participants in study 1 were thus treated in random order for 12 weeks with liraglutide and insulin glargine, fMRI data was collected at the start of treatment, after 10 days and after 12 weeks. Study 1 results showed that, during saline infusion, DM2 patients had greater connectivity compared to controls in the left insula and operculum, which related to greater GLP-1 mediated weightloss after 10 days and 12 weeks. Also, connectivity was greater in DM2 patients versus controls in the frontal pole, frontal medial cortex, anterior cingulate and paracingulate giry, which related to less GLP-1 mediated weight-loss after 10 days (all P(FWE) less than 0.05). There was no effect on connectivity for GLP-1 antagonist, and no long-term differences between treatments (all P(FWE) less than 0.05). In conclusion, baseline resting-state connectivity was shown to be related to GLP-1 mediated weightchange, with greater frontal connectivity relating to less weight loss during GLP-1 treatment, while higher left insula connectivity correlated to greater weight loss during GLP-1 treatment, indicating a relationship between baseline intrinsic connectivity in these regions and weight loss during GLP-1 treatment.

<p>Alzheimer’s disease (AD) is a degenerative disease with progressive deterioration of neural networks in the brain. Fractal dimension analysis (FD) of resting state blood oxygen level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) allows us to quantify complex signalling in the brain and may offer a window into the network erosion. This novel approach can provide a sensitive tool to examine early stages of AD. As AD progresses, we expect to see a reduction in brain connectivity and signal complexity concurrent with biochemical changes (e.g. altered levels of N-acetyl aspartate (NAA), myoinositol (mI) and glutamate as measured using magnetic resonance spectroscopy, MRS), volumetric changes and abnormally high levels of brain iron.</p> <p>Over a series of 4 studies we examined the relationship of BOLD signal complexity and functional connectivity with documented MRI markers of pathology in AD (n=38) as compared to normal controls (NC) (n=16). AD subjects were in early stage of illness (mild to moderate impairment on the mini mental state exam, MMSE). We validated the temporal (short term (within minutes) and longer term (over a number of months)) consistency of FD measurement and choice of BOLD acquisition method (spiral vs. EPI), provided MRI sequence repeat time (TR) was kept constant. FD reduction (decrease in signal complexity) correlated with worsening pathological values on MRS (­NAA decrease and mI increase) and with a decrease in functional connectivity. This demonstrates that FD (signal complexity) reduces in proportion to AD severity. FD reduction is connected to functional connectivity measured through resting state network (RSN) analysis suggesting the reduction in FD relates to neuronal loss rather than altered vascularity. The narrow range of cognitive impairment (such as scores on the MMSE or the clinical dementia rating scale, CDR) likely precluded correlation between these measures and FD or RSN. Functional connectivity (RSN) was also reduced when brain iron levels were increased within certain network nodes (posterior cingulate cortex and lateral parietal cortex). Therefore iron deposition may play a role in network disruption of AD brains.</p> <p>The overall conclusion of this thesis is that signal complexity of BOLD fMRI signals, as measured with FD, may detect early pathology in the progression of AD. FD can detect neuronal changes in deep brain structures before volume loss in these structures and before significant changes in MRS markers were detectable between the AD and NC groups. An FD change mirrors disruptions in functional connectivity but detection is not limited to RSN nodes in the brain. This novel approach could further our understanding of AD and may be applied to other pathologies of the brain.</p> / Doctor of Philosophy (PhD)

While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.