Dissertations / Theses on the topic 'Brain functional Network'

Thesis (Ph.D.)--Biomedical Engineering, Georgia Institute of Technology, 2007.<br>Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Butera, Robert; Committee Member: Oshinski, John; Committee Member: Sathian, Krish.

Recent developments in the complex networks analysis, based largely on graph theory, have been used to study the brain network organization. The brain is a complex system that can be represented by a graph. A graph is a mathematical representation which can be useful to study the connectivity of the brain. Nodes in the brain can be identified dividing its volume in regions of interest and links can be identified calculating a measure of dependence between pairs of regions whose activation signal, measured by functional magnetic resonance imaging (fMRI) techniques, represents the strength of the connec-tion between regions. A graph can be synthesized by the so-called adjacency matrix, which, in its simplest form, is an undirected, binary, and symmetric matrix, whose en-tries are set to one if a link exists between a pair of brain areas and zero otherwise. The adjacency matrix is particularly useful because allows the calculation of several measures which summarize global and local character-istics of functional brain connectivity, such as centrality, e ciency, density and small worldness property. In this work, we consider the global measures, such as the clustering coe cient, the characteristic path length and the global e ciency, and the local measures, such as centrality measures and local e ciency, in order to represent global and local dynam-ics and changes between networks. This is achieved by studying with resting state (rs) fMRI data of healthy subjects and patients with neurodegenerative diseases. Furthermore we illustrate an original methodology to construct the adjacency matrix. Its entries, containing the information about the ex-istence of links, are identified by testing the correlation between the time series that characterized the dynamic behavior of the nodes. This involves the problem of multiple comparisons in order to control the error rates. The method based on the estimation of positive false discovery rate (pFDR) has been used. A similar measure involving false negatives (type II errors), called the positive false nondiscovery rate (pFNR) is then considered, proposing new point and interval estimators for pFNR and a method for balancing the two types of error. This approach is demonstrated using both simulations and fMRI data, and providing nite sample as well as large sample results for pFDR and pFNR estimators. Besides a ranking of the most central nodes in the networks is proposed using q-values, the pFDR analog of the p-values. The di erences on the inter-regional connectivity between cases and controls are studied. Finally network models are discussed. In order to gain deeper insights into the complex neurobiological interaction, exponential random graph models (ERGMs) are applied to assess several network properties simultaneously and to compare case/control brain networks.

Complex alterations in brain function and neurochemistry underlie pathology and treatment in multiple neuropsychiatric disorders, yet remain incompletely characterised. This thesis outlines possibilities for human neuroimaging techniques sensitive to spontaneous fluctuations in large-scale neurobiological signalling, or ‘resting-state network (RSN) functional connectivity’, to address such knowledge gaps. Novel RSN-sensitive analysis approaches to functional magnetic resonance imaging data are introduced. These techniques are then evaluated experimentally, in contexts relevant for maladaptive cognitive and motivational processing, for their utility to identify and characterise systems-level signatures of individual differences in neurochemistry and psychopharmacological responsiveness. Firstly, RSN functional connectivity measures are investigated in the context of pharmacological intervention with nicotine replacement therapy in habitual smokers. Results identify connectivity between executive control and ‘default mode’ RSNs as a neural signature of pharmacotherapeutic efficacy in treating cognitive symptoms of nicotine withdrawal. Secondly, RSN connectivity is investigated alongside specific neuroreceptor-sensitive measures to investigate the extent to which network connectivity patterns reflect fundamental neurobiology in healthy subjects. Individual differences in dopamine D3 receptor availability - a possible marker for reward-related behaviours -are associated with topographic connectivity signatures within RSNs implicated in cognitive and motivational control. Thirdly, the ability of RSN metrics to characterise distinct neurochemical manipulations is tested in healthy subjects. Dopamine agnostic and antagonistic neuromodulations display differential effects on signalling in cortico-cubcortical and cortico-cortical reward circuitry and interact selectively with subject impulsivity. Finally, RSN cortico-subcortical connectivity metrics are investigated for their sensitivity to clinical-pharmacological effects in Parkinson’s disease. Results reveal evidence for both compensatory large-scale network mechanisms and ‘non-normalising’ dopaminergic medication effects in patients. Overall, findings indicate novel systems-level neuroimaging methodology probing interactions within and between RSNs to provide sensitive, biologically plausible markers for behavioural and neuropharamacological characteristics of neuropsychiatric disorders. Continued developments of functional network analysis approaches may facilitate their direct application to clinical and drug development domains.

Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention.

Huntington’s disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene on chromosome 4. In recent years there have been significant advances in understanding both the cellular pathology and the macrostructural changes that occur in the striatum and cortical structures as the disease proceeds. However, it remains unclear how abnormalities at the cellular level lead to characteristic patterns of macrostructural change in the brains of HD patients. In this thesis I aim to link structural and functional brain network abnormalities with regional changes at the cellular level. Using diffusion tractography and resting state functional MRI in well characterised HD cohorts I examine the relationship between structural and functional brain network organisation. I link these changes in structure and function to the neuropsychiatric symptoms prevalent in HD, occurring years before the manifestation of motor symptoms. By characterising changes in white matter brain networks I reveal how the brain network breaks down as HD progresses and show how this network deterioration leads to the emergence of clinical deficits. Using characteristics of the healthy white matter brain network I demonstrate how it is possible to predict the atrophy of specific brain connections in HD over time. In doing so I highlight a hierarchy of white matter connection vulnerability showing cortico-striatal connections are the first to be affected. In order to link these macrostructural white matter changes to cellular level abnormalities I utilise data from the Allen Institute transcription atlas and show how differences in regional gene expression in the healthy brain can account for the selective vulnerability of specific white matter connections in HD. The work presented in this thesis demonstrates how linking systems and cellular pathobiology in HD can inform us about disease mechanisms that drive brain atrophy and ultimately lead to clinical deficits.

Complex network models of functional connectivity have emerged as a paradigm shift in brain mapping over the past decade. Despite significant attention within the neuroimaging and cognitive neuroscience communities, these approaches have hitherto not been extensively explored in neurosurgery. The aim of this thesis is to investigate how the field of connectomics can contribute to understanding the effects of focal brain lesions and to functional brain mapping in neurosurgery. This datasets for this thesis include a clinical population with focal brain tumours and a cohort focused on healthy adolescent brain development. Multiple network analyses of increasing complexity are performed based upon resting state functional MRI. In patients with focal brain tumours, the full complement of resting state networks were apparent, while also suggesting putative patterns of network plasticity. Connectome analysis was able to identify potential signatures of node robustness and connections at risk that could be used to individually plan surgery. Focal lesions induced the formation of new hubs while down regulating previously established hubs. Overall these data are consistent with a dynamic rather than a static response to the presence of focal lesions. Adolescent brain development demonstrated discrete dynamics with distinct gender specific and age-gender interactions. Network architecture also became more robust, particularly to random removal of nodes and edges. Overall these data provide evidence for the early vulnerability rather than enhanced plasticity of brain networks. In summary, this thesis presents a combined analysis of pathological and healthy development datasets focused on understanding the functional effects of focal brain lesions at a network level. The coda serves as an introduction to a forthcoming study, known as Connectomics and Electrical Stimulation for Augmenting Resection (CAESAR), which is an evolution of the results and methods herein.

Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>A set of widely distributed brain areas, collectively known as the fronto-parietal network, serve to modulate aspects of visual perception. However, the unique influence exerted by these regions on low-level visual processing remains unclear. The goals of this thesis were (1) to examine how right frontal, parietal and occipital brain areas interact to process and modulate visual function and (2) to investigate the ability to improve foveal visual performance by means of noninvasive neurostimulation. In a first set of experiments, visual percepts known as 'phosphenes' were measured following low-frequency neurostimulation of the right occipital pole, Intraparietal Sulcus (IPS) or Frontal Eye Fields (FEF). Stimulation of the occipital pole and IPS were capable of evoking phosphenes with similar appearances. Furthermore, occipital or IPS stimulation decreased the excitability of the locally stimulated region but had no effect on the non-stimulated brain area. These results indicate a lack of sufficient inter-regional interactions capable of supporting long-range changes in brain activity. In a second set of experiments, contrast sensitivity and reaction times were assessed as the capacity to detect centrally located, high or low spatial frequency stimuli. Low-frequency rTMS to the FEF, but not the occipital pole or IPS improved contrast sensitivity for high spatial frequency stimuli. Stimulation of the occipital pole decreased reaction times for low spatial frequency stimuli and was shown to depend on transcollicular projections. Finally, stimulation of the IPS decreased reaction times for both types of stimuli. These effects however did not appear to depend on transcollicular pathways, indicating that performance was enhanced through cortico-cortical connections. In a final set of experiments, we investigated whether patterns of individual white matter connectivity linking stimulated brain regions could predict the effects of neurostimulation on visual processing and performance. None of the probability measures however correlated with changes in visual performance. Overall, these data suggest that occipital, parietal, frontal and tectal areas uniquely contribute to the modulation of visual perception. Moreover, results show that targeted stimulation to these brain regions serves to generate lasting improvements in visual performance, which could be used to enhance aspects of vision in healthy and clinical populations.

Abstract: The default mode network of the brain is a set of functionally connected regions associated with introspection and daydreaming. Recent fMRI studies have discovered that the default mode network is often perturbed in the diseased brain. For example, the default mode network is known to be modulated in dementia, ADHD, depression, and schizophrenia, among others. This has led many into believing that this network could have a role in the physiopathology of nervous system disease, or could be a useful marker of brain function. However, very few studies have yet been done which investigate how surgical lesions such as brain tumours affect the default mode network. Consequently, the goal of this project was to characterise the effect of brain tumours on the default mode network based on their location, histological type, and other parameters.<br>Le mode de fonctionement par défaut du cerveau est un réseau cérébral associé à la rêverie et à l’introspection. Des études récentes sur ce réseau ont découvert qu’il est perturbé dans plusieurs pathologies cérébrales. Par example, le mode de fonctionnement par défaut est modulé en démence, TDAH, dépression, schizophrénie et plusieurs autres maladies liés au cerveau. Ceci a mené à l’hypothèse que le mode de fonctionnement par défaut pourrait avoir un rôle dans la physiopathologie des maladies du système nerveux, ou pourrait être un marqueur utile du fonctionnement cérébral. Par contre, très peu d’études ont investigué l’effet de lésions chirurgicaux comme les tumeurs cérébrales sur le mode de fonctionnement par défaut. Par conséquent, le but de ce projet était de caractériser l’importance de l’histologie, de la localisation et de plusieurs autres paramètres de l’effet d’une tumeur cérébrale sur le mode de fonctionnement par défaut.

El cerebro está constituido por numerosos elementos que se encuentran interconectados de forma masiva y organizados en módulos que forman redes jerárquicas. Ciertas patologías cerebrales, como la enfermedad de Alzheimer y el trastorno por consumo de alcohol, se consideran el resultado de efectos en cascada que alteran la conectividad cerebral. La presente tesis tiene como objetivo principal la aplicación de las técnicas de análisis de la ciencia de redes para el estudio de las redes estructurales y funcionales en el cerebro, tanto en un estado control como en un estado patológico. Así, en el primer estudio de la presente tesis se examina la relación entre la conectividad estructural y funcional en la corteza cerebral de la rata. Se lleva a cabo un análisis comparativo entre las conexiones estructurales en la corteza cerebral de la rata y los valores de correlación calculados sobre las mismas regiones. La información acerca de la conectividad estructural se ha obtenido a partir de estudios previos, mientras que la conectividad funcional se ha calculado a partir de imágenes de resonancia magnética funcional. Determinadas propiedades topológicas, y extraídas de la conectividad estructural, se relacionan con la organización modular de las redes funcionales en estado de reposo. Los resultados obtenidos en este primer estudio demuestran que la conectividad estructural y funcional cortical están altamente relacionadas entre sí. Estudios recientes sugieren que el origen de la enfermedad de Alzheimer reside en un mecanismo en el cual depósitos de ovillos neurofibrilares y placas de beta-amiloide se acumulan en ciertas regiones cerebrales, y tienen la capacidad de diseminarse por el cerebro actuando como priones. En el segundo estudio de la presente tesis se investiga si las redes estructurales que se generan con la técnica de resonancia magnética ponderada en difusión podrían ser de utilidad para el diagnóstico de la pre-demencia causada por la enfermedad de Alzheimer. Mediante el uso de imágenes procedentes de la base de datos ADNI, se aplican técnicas de aprendizaje máquina con el fin de identificar medidas de centralidad que se encuentran alteradas en la demencia. En la segunda parte del estudio, se utilizan imágenes procedentes de la base de datos NKI para construir un modelo matemático que simule el proceso de envejecimiento normal, así como otro modelo que simule el proceso de desarrollo de la enfermedad. Con este modelado matemático, se pretende estimar la etapa más temprana que está asociada con la demencia. Los resultados obtenidos de las simulaciones sugieren que en etapas tempranas de la enfermedad de Alzheimer se producen alteraciones estructurales relacionados con la demencia. La cuantificación de la relación estadística entre las señales BOLD de diferentes regiones puede informar sobre el estado funcional cerebral característico de enfermedades neurológicas y psiquiátricas. En el tercer estudio de la presente tesis se estudian las alteraciones en la conectividad funcional que tienen lugar en ratas dependientes del consumo de alcohol cuando se encuentran en estado de reposo. Para ello, se ha aplicado el método NBS. El análisis de este modelo de rata revela diferencias estadísticamente significativas en una subred de regiones cerebrales que están implicadas en comportamientos adictivos. Por lo tanto, estas estructuras cerebrales podrían ser el foco de posibles dianas terapéuticas. La tesis aporta tres innovadoras contribuciones para entender la conectividad cerebral bajo la perspectiva de la ciencia de redes, tanto en un estado control como en un estado patológico. Los resultados destacan que los modelos basados en las redes cerebrales permiten esclarecer la relación entre la estructura y la función en el cerebro. Y quizás más importante, esta perspectiva de red tiene aplicaciones que se podrían trasladar a la práctica clínica.<br>The brain is composed of massively connected elements arranged into modules that form hierarchical networks. Experimental evidence reveals a well-defined connectivity design, characterized by the presence of strategically connected core nodes that critically contribute to resilience and maintain stability in interacting brain networks. Certain brain pathologies, such as Alzheimer's disease and alcohol use disorder, are thought to be a consequence of cascading maladaptive processes that alter normal connectivity. These findings have greatly contributed to the development of network neuroscience to understand the macroscopic organization of the brain. This thesis focuses on the application of network science tools to investigate structural and functional brain networks in health and disease. To accomplish this goal, three specific studies are conducted using human and rodent data recorded with MRI and tracing technologies. In the first study, we examine the relationship between structural and functional connectivity in the rat cortical network. Using a detailed cortical structural matrix obtained from published histological tracing data, we first compare structural connections in the rat cortex with their corresponding spontaneous correlations extracted empirically from fMRI data. We then show the results of this comparison by relating structural properties of brain connectivity to the functional modularity of resting-state networks. Specifically, we study link reciprocity in both intra- and inter-modular connections as well as the structural motif frequency spectrum within functionally defined modules. Overall, our results provide further evidence that structural connectivity is coupled to and shapes functional connectivity in cortical networks. The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting pahtogenic seeding and subsequent prion-like spreading processes of neurofibrillary tangles and amyloid plaques. In the second study of this thesis, we investigate whether structural brain networks as measured with dMRI could serve as a complementary diagnostic tool in prodromal dementia. Using imaging data from the ADNI database, we first aim to implement machine learning techniques to extract centrality features that are altered in Alzheimer's dementia. We then incorporate data from the NKI database and create dynamical models of normal aging and Alzheimer's disease to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Our model results suggest that changes associated with dementia begin to manifest structurally at early stages. Statistical dependence measures computed between BOLD signals can inform about brain functional states in studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements between clinical and animal studies, providing excellent translational capabilities. In the last study, we apply the NBS method to investigate alterations in the resting-state functional connectivity of the rat brain in a PD state, an established animal model of clinical relevant features in alcoholism. The analysis reveal statistically significant differences in a connected subnetwork of structures with known relevance for addictive behaviors, hence suggesting potential targets for therapy. This thesis provides three novel contributions to understand the healthy and pathological brain connectivity under the perspective of network science. The results obtained in this thesis underscore that brain network models offer further insights into the structure-function coupling in the brain. More importantly, this network perspective provides potential applications for the diagnosis and treatment of neurological and psychiatric disorders.<br>El cervell està constituït per nombrosos elements que es troben interconnectats de forma massiva i organitzats en mòduls que formen xarxes jeràrquiques. Certes patologies cerebrals, com la malaltia d'Alzheimer i el trastorn per consum d'alcohol, es consideren el resultat d'efectes en cascada que alteren la connectivitat cerebral. La present tesi té com a objectiu principal l'aplicació de les tècniques d'anàlisi de la ciència de xarxes per a l'estudi de les xarxes estructurals i funcionals en el cervell, tant en un estat control com en un estat patològic. Així, en el primer estudi de la present tesi s'examina la relació entre la connectivitat estructural i funcional en l'escorça cerebral de la rata. Es du a terme una anàlisi comparativa entre les connexions estructurals en l'escorça cerebral de la rata i els valors de correlació calculats sobre les mateixes regions. La informació sobre la connectivitat estructural s'ha obtingut a partir d'estudis previs, mentre que la connectivitat funcional s'ha calculat a partir d'imatges de ressonància magnètica funcional. Determinades propietats topològiques, i extretes de la connectivitat estructural, es relacionen amb l'organització modular de les xarxes funcionals en estat de repòs. Els resultats obtinguts en este primer estudi demostren que la connectivitat estructural i funcional cortical estan altament relacionades entre si. Estudis recents suggereixen que l'origen de la malaltia d'Alzheimer resideix en un mecanisme en el qual depòsits d'ovulets neurofibrilars i plaques de beta- miloide s'acumulen en certes regions cerebrals, i tenen la capacitat de disseminar-se pel cervell actuant com a prions. En el segon estudi de la present tesi s'investiga si les xarxes estructurals que es generen amb la tècnica de la imatge per ressonància magnètica ponderada en difusió podrien ser d'utilitat per al diagnòstic de la predemència causada per la malaltia d'Alzheimer. Per mitjà de l'ús d'imatges procedents de la base de dades ADNI, s'apliquen tècniques d'aprenentatge màquina a fi d'identificar mesures de centralitat que es troben alterades en la demència. En la segona part de l'estudi, s'utilitzen imatges procedents de la base de dades NKI per a construir un model matemàtic que simule el procés d'envelliment normal, així com un altre model que simule el procés de desenrotllament de la malaltia. Amb este modelatge matemàtic, es pretén estimar l'etapa més primerenca que està associada amb la demència. Els resultats obtinguts de les simulacions suggereixen que en etapes primerenques de la malaltia d'Alzheimer es produeixen alteracions estructurals relacionats amb la demència. La quantificació de la relació estadística entre els senyals BOLD de diferents regions pot informar sobre l'estat funcional cerebral característic de malalties neurològiques i psiquiàtriques. A més, a causa de la seua naturalesa no invasiva, és possible comparar els resultats obtinguts entre estudis clínics i estudis amb animals d'experimentació. En el tercer estudi de la present tesi s'estudien les alteracions en la connectivitat funcional que tenen lloc en rates dependents del consum d'alcohol quan es troben en estat de repòs. Per a realitzar-ho, s'ha aplicat el mètode NBS. L'anàlisi d'aquest model de rata revela diferències estadísticament significatives en una subxarxa de regions cerebrals que estan implicades en comportaments addictius. Per tant, estes estructures cerebrals podrien ser el focus de possibles dianes terapèutiques. La tesi aporta tres innovadores contribucions per a entendre la connectivitat cerebral davall la perspectiva de la ciència de xarxes, tant en un estat control com en un estat patològic. Els resultats destaquen que els models basats en les xarxes cerebrals permeten aclarir la relació entre l'estructura i la funció en el cervell. I potser més important, esta perspectiva de xarxa té aplicacions que es podrien traslladar a la pràcti<br>Díaz Parra, A. (2018). A network science approach of the macroscopic organization of the brain: analysis of structural and functional brain networks in health and disease [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106966<br>TESIS

Resting-state functional MRI(rs-fMRI) represents a powerful means to assess brain functional connectivity in healthy subjects and in neuropsychiatric patients. Aberrant functional connectivity has been observed in subjects affected by Alcohol Use Disorders (AUD) and other forms of substance dependence, a major health issue worldwide with limited treatment options. Despite intense investigation, the specific neuronal substrates involved and the functional implications of aberrant connectivity in these patients remain unknown. Moreover, it is unclear whether treatment can reverse these alterations, and normalize functional connectivity. Several methodological and conceptual questions in the analysis of functional connectivity are still open, and contribute to this uncertainty. Functional connectivity is defined in terms of correlated MR-signal fluctuations, and in-scanner patient motion and other nuisance signals can introduce spurious correlations, thus representing substantial confounding factors. At a more general level, understanding the effects of complex conditions, like AUD, on brain connectivity and their functional implications requires a deep comprehension of the brain organizational principles at multiple scales, a tremendous challenge that is at the heart of modern neuroscience. In this PhD dissertation I address some of the outstanding questions in the analysis and interpretation of aberrant functional connectivity in AUD. To this end, I have embraced the formalism of graph-theory, a powerful framework to assess the effects of alcohol abuse on the local and global topological organization of resting state connectivity. On the methodological side, I have investigated the effects of subject’s motion on the structure of resting state networks, and compared efficacy of different approaches to remove motion-related confounds. Moreover, I demonstrate the importance of network sparsification to remove spurious connections from the graph while maximizing the structural information that can be extracted from the system. Leveraging these methodological developments, I have evaluated functional alterations in different samples of AUD patients. In two independent studies, I demonstrated specific alterations in the topological organization of the insular cortex and subcortical basal structures in recently detoxified alcoholics. Interestingly, protracted abstinence appears to partially normalize functional connectivity, thus suggesting that alcohol-induced alterations in connectivity may be amenable to treatment. Based on these findings, I have studied the effects on brain functional networks of a putative novel treatment based on deep Transcranial Magnetic Stimulation (TMS). Specifically, I analyzed resting state connectivity in AUD patients subjected to repetitive TMS of the bilateral insula and of the anterior cingulate cortex (ACC), and demonstrated treatment-induced changes that may underlie the efficacy of this potential treatment in surrogate clinical read-outs.

The Default Mode Network (DMN) is a large-scale brain network implicated in the control and monitoring of internal modes of cognition. The aim of this research was to investigate DMN function and its relationship to other large-scale cognitive control networks through functional connectivity analysis and analysis of combined electroencephalographic (EEG) recordings. Data utilised across a series of three experiments were obtained from combined EEG-functional Magnetic Resonance Imaging recordings acquired during technical development of a new scanner in the Clinical Research Centre, Ninewells Hospital, Dundee. Analyses were based on data acquired from neurologically healthy participants while they rested with their eyes-closed for five minutes. Following this, participants completed a 14-minute auditory attention task, designed to engage the dorsal and ventral attention networks. In this task, participants responded to task-relevant stimuli (odd/even numbers) and attempted to inhibit their responses to task-irrelevant ‘oddballs’ (the number ‘0’) and task-irrelevant/distractor stimuli (environment sounds). Experiment 1 utilised the simultaneous acquired EEG-fMRI resting-state data in order to establish whether EEG frequency content in the beta range (13-30 Hz) was a significant predictor of DMN activity (regions of which were identified on an individual basis using functional connectivity analysis). Results were comparable to existing literature showing there is inconsistency in establishing a reliable electrophysiological signature of the DMN. Experiment 1 also employed region-of-interest (ROI)-to-ROI functional connectivity analysis as a method of exploring the functional relationship between the DMN and: (1) a task-positive resting-state network; (2) other commonly identified DMN regions; and (3) regions covering the whole of the cerebral cortex. Results revealed networks were correlated at a component-based level and challenged existing literature which appears to over-generalise results from exploration of network interaction. Findings also revealed activation of specific DMN components were coupled with down-regulation of sensory-associated cortical regions. Experiment 2 analysed the fMRI data that were obtained from the auditory attention task in order to: (1) determine whether DMN activity was observed when participants were engaged in an externally-directed task; and (2) explore changes in DMN activity associated with increasing task duration. Results revealed that activation of the DMN was prominent and did not vary over three equal time periods. This supports existing research showing the DMN is a continuously active system (whose activity is modulated based on external-task demands). Results also hinted at the existence of possible relationships between the DMN and components of several other large-scale control networks. Therefore, in Experiment 3 potential interactions were explored using ROI-to-ROI functional connectivity analysis of the whole 14-minute time series. Firstly, functional connectivity within the dorsal/ventral attention, executive/frontoparietal control and salience networks was analysed; secondly, the relationships between putative regions of these networks and the DMN were analysed. Overall, results revealed that networks were functionally connected with one another at a component-based level only. This suggests flexible interaction between several large-scale control networks allows neurologically healthy participants to allocate resources to the simultaneous monitoring of the internal and external worlds.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2007.<br>Includes bibliographical references (leaves 95-103).<br>Diffusion MRI and fMRI have provided neuroscientists with non-invasive tools to probe the functional and structural network of the brain. Diffusion MRI is a neuroimaging technique capable of measuring the diffusion of water in neural tissue. It can reveal histological architecture irresolvable by conventional magnetic resonance imaging methods and has emerged as a powerful tool to investigate a wide range of neuropathologies. fMRI is a neuroimaging technique sensitive to hemodynamics which is indirectly linked to neural activity. Despite the applications of diffusion MRI and fMRI in basic and clinical neuroscience, the underlying biophysical mechanisms of cerebral diffusion and the hemodynamic response remain largely unknown. Also, these neurotechnqiues suffer from low SNR compared to conventional MRI. The challenges associated with the acquisition and interpretation of diffusion MRI and fMRI limit the application of these powerful non-invasive neuroimaging tools to study the functional and structural network of the brain. The purpose of this thesis is three fold; (1) improve the acquisition and reconstruction of the diffusion MRI and fMRI signals and (2) develop an MR-compatible cortical cooling system to reversibly deactivate cerebral glucose metabolism, and (3) apply the cortical cooling system to investigate the effect of cerebral glucose metabolism on cerebral diffusion and the hemodynamic response. First, I describe a novel phased array monkey coil capable of improving the resolution of diffusion MRI (4 fold increase) and fMRI (2 fold increase) in monkeys. Secondly, I present a novel reconstruction method to resolve complex white matter architecture which boosts the sampling efficiency of the diffusion MRI acquisition by 274-377%.<br>(cont.) Thirdly, I present a MR-compatible cortical cooling system capable of reversibly deactivating cerebral metabolism in monkeys. The cortical cooling system has been applied to study the effect of cerebral glucose metabolism on the cerebral diffusion of water. I use MR temperature maps to quantify the region and degree of deactivation (accuracy of ±1 °C in vivo). Then, I show that reversible deactivation of cerebral glucose metabolism affects the magnitude of cerebral diffusion (12-20%) but not the anisotropy. Finally, I apply the cortical cooling system to study the effect of reversibly deactivating cerebral glucose metabolism in V1 and its effect on the hemodynamic response in the visual system. Reversible deactivation of V1 decreased the hemodynamic response in visually driven regions upstream and downstream from V1. Compensatory effects were observed in V1 in both hemispheres and ipsilateral TEO with in 2 minutes of deactivation. Here I have described the tools to probe the functional and structural network of the macaque brain.<br>by Mark Haig Khachaturian.<br>Ph.D.

Electrical Engineering<br>Ph.D.<br>For almost a century, the electrical properties of the brain and the nervous system have been investigated to gain a better understanding of their mechanisms and to find cures for pathological conditions. Despite the fact that today's advancements in surgical techniques, research, and medical imaging have improved our ability to treat brain disorders, our knowledge of the brain and its functions is still limited. Culturing dissociated cortical neurons on Micro-Electrode Array dishes is a powerful experimental tool for investigating functional and structural characteristics of in-vitro neuronal networks, such as the cellular basis of brain learning, memory and synaptic developmental plasticity. This dissertation focuses on combining MEAs with novel electrophysiology experimental paradigms and statistical data analysis to investigate the mechanisms that regulate brain development at the level of synaptic formation and growth cones. The goal is to use a mathematical approach and specifically designed experiments to investigate whether dissociated neuronal networks can dependably display long and short-term plasticity, which are thought to be the building blocks of memory formation in the brain. Quantifying the functional evolution of dissociated neuronal networks during in- vitro development, using a statistical analysis tool was the first aim of this work. The results of the False Discovery Rate analysis show an evolution in network activity with changes in both the number of statistically significant stimulus/recording pairs as well as the average length of connections and the number of connections per active node. It is therefore proposed that the FDR analysis combined with two metrics, the average connection length and the number of highly connected "supernodes" is a valuable technique for describing neuronal connectivity in MEA dishes. Furthermore, the statistical analysis indicates that cultures dissociated from the same brain tissue display trends in their temporal evolution that are more similar than those obtained with respect to different batches. The second aim of this dissertation was to investigate long and short-term plasticity responsible for memory formation in dissociated neuronal networks. In order to address this issue, a set of experiments was designed and implemented in which the MEA electrode grid was divided into four quadrants, two of which were chronically stimulated, every two days for one hour with a stimulation paradigm that varied over time. Overall network and quadrant responses were then analyzed to quantify what level of plasticity took place in the network and how this was due to the stimulation interruption. The results demonstrate that here were no spatial differences in the stimulus-evoked activity within quadrants. Furthermore, the implemented stimulation protocol induced depression effects in the neuronal networks as demonstrated by the consistently lower network activity following stimulation sessions. Finally, the analysis demonstrated that the inhibitory effects of the stimulation decreased over time, thus suggesting a habituation phenomenon. These findings are sufficient to conclude that electrical stimulation is an important tool to interact with dissociated neuronal cultures, but localized stimuli are not enough to drive spatial synaptic potentiation or depression. On the contrary, the ability to modulate synaptic temporal plasticity was a feasible task to achieve by chronic network stimulation.<br>Temple University--Theses

La actividad cerebral exhibe complejos fenómenos oscilatorios similares a los que se observan en modelos de redes artificiales con osciladores acoplados. Por un lado, estudios sobre la actividad cerebral durante el reposo han demostrado la presencia de fluctuaciones lentas estructuradas y modulaciones de potencia a distintas frecuencias. Simultáneamente, estudios teóricos en el ámbito de la física muestran dinámicas similares usando osciladores acoplados. En este trabajo, por primera vez, se usan modelos de osciladores de fase en redes inspiradas en la arquitectura real del cerebro. Los resultados muestran la aparición espontánea de una dinámica similar a la observada experimentalmente. Además, esta correspondencia es comparable cuantitativamente con datos de neuroimagen, lo que sugiere procesos generales de integración subyacentes a la cognición. Por otra parte, se propone que la actividad cerebral alterada observada en algunas enfermedades psiquiátricas podría tener su origen en desconexiones estructurales que afectarían el comportamiento cooperativo de regiones corticales.<br>Neural activity in the brain exhibits complex oscillatory phenomena that can be compared with the ones observed in artificial network models of coupled oscillators. In particular, neuroimaging studies of brain activity during rest have reported slow spatiotemporally organized fluctuations and correlated band-limited power modulations. Simultaneously, theoretical works on the area of physics have reported similar dynamic behaviours using simple models of coupled oscillators with intermittent modular synchronization. In this work, for the first time, we use models of phase oscillators in networks inspired in the brain’s wiring architecture. Results show the spontaneous emergence of a dynamics similar to the one observed experimentally. In addition, this correspondence is quantitatively comparable to neuroimaging data, which is suggestive of general integrative processes underlying cognition. Furthermore, we propose that altered brain activity observed in some psychiatric diseases might originate from structural disconnections, which affect the cooperative behaviour of coupled cortical regions.

Neurological and psychiatric disorders reveal that the basal ganglia subserve diverse functional domains, including movement, reward, and cognitive disorders (e.g., Parkinson's disease, addiction, schizophrenia). Monkey anatomical studies show that the striatum, the input structure of the basal ganglia, receives projections from nearly the entire cerebral cortex with a broad topography of motor, limbic, and association zones. However, until recently, non-invasive methods have not been available to conduct the complete mapping of the cortex to the striatum in humans. The development of functional connectivity magnetic resonance imaging (fcMRI) now allows the identification of functional connections in humans. The present dissertation reports two studies that first create a complete map of corticostriatal connectivity and then more closely examine striatal connectivity with association networks underlying cognition.

Despite the interest in the neuroimaging of working memory, little is still known about the neurobiology of complex working memory in tasks that require simultaneous manipulation and storage of information. In addition to the central executive network, we assumed that the recently described salience network [involving the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC)] might be of particular importance to working memory tasks that require complex, effortful processing. Method: Healthy participants (n = 26) and participants suffering from working memory problems related to the Kleine–Levin syndrome (KLS) (a specific form of periodic idiopathic hypersomnia; n = 18) participated in the study. Participants were further divided into a high- and low-capacity group, according to performance on a working memory task (listening span). In a functional magnetic resonance imaging (fMRI) study, participants were administered the reading span complex working memory task tapping cognitive effort. Principal findings: The fMRI-derived blood oxygen level dependent (BOLD) signal was modulated by (1) effort in both the central executive and the salience network and (2) capacity in the salience network in that high performers evidenced a weaker BOLD signal than low performers. In the salience network there was a dichotomy between the left and the right hemisphere; the right hemisphere elicited a steeper increase of the BOLD signal as a function of increasing effort. There was also a stronger functional connectivity within the central executive network because of increased task difficulty. Conclusion: The ability to allocate cognitive effort in complex working memory is contingent upon focused resources in the executive and in particular the salience network. Individual capacity during the complex working memory task is related to activity in the salience (but not the executive) network so that high-capacity participants evidence a lower signal and possibly hence a larger dynamic response.

Evidence of networks in the resting-brain reflecting the spontaneous brain activity is perhaps the most significant discovery to understand intrinsic brain functionality. Moreover, subsequent detection of dynamics in these networks can be milestone in differentiating the normal and disordered brain functions. However, capturing the correct dynamics is a challenging task since no ground truths' are present for comparison of the results. The change points of these networks can be different for different subjects even during normal brain functions. Even for the same subject and session, dynamics can be different at the start and end of the session based on the fatigue level of the subject scanned. Despite the absence of ground truths, studies have analyzed these dynamics using the existing methods and some of them have developed new algorithms too. One of the most commonly used method for this purpose is sliding window correlation. However, the result of the sliding window correlation is dependent on many parameters and without the ground truth there is no way of validating the results. In addition, most of the new algorithms are complicated, computationally expensive, and/or focus on just one aspect on these dynamics. This study applies the algorithms and concepts from signal processing, image processing, video processing, information theory, and machine learning to analyze the results of the sliding window correlation and develops a novel algorithm to detect change points of these networks adaptively. The findings in this study are divided into three parts: 1) Analyzing the extent of variability in well-defined networks of rodents and humans with sliding window correlation applying concepts from information theory and machine learning domains. 2) Analyzing the performance of sliding window correlation using simulated networks as ground truths for best parameters’ selection, and exploring its dependence on multiple frequency components of the correlating signals by processing the signals in time and Fourier domains. 3) Development of a novel algorithm based on image similarity measures from image and video processing that maybe employed to identify change points of these networks adaptively.

Human structural and functional brain architecture is increasingly studied by applying the mathematical framework of complex networks to data from magnetic resonance imaging. Connections (edges) in such brain networks are commonly constructed using correlations of features between pairs of brain regions, such as regional morphology (across participants) or neurophysiological time series (within participants). Subsequent analyses frequently focus on summary network statistics calculated using the strongest correlations, but often neglect potential underlying shifts within the correlation distribution. This thesis presents methods for the construction and analysis of correlation-derived structural and functional brain networks, focusing on the implications of changes within the correlation distribution. First, schizophrenia is considered as an example disease which is known to present a reduction in mean correlation between regional neurophysiological time series. Previous studies reported increased network randomisation in schizophrenia, but these results may have been driven by inclusion of a greater number of noisy edges in patients’ networks, based on retention of a fixed proportion of the strongest edges during network thresholding. Here, a novel probabilistic thresholding procedure is applied, based on the realisation that the strongest edges are not necessarily most likely to be true following adjustment of edge probabilities for effects of participant in-scanner motion. Probabilistically thresholded functional networks show decreased randomness, and increased consistency across participants. Further, applying probabilistic thresholding eliminates increased network randomisation in schizophrenia, supporting the hypothesis that previously reported group differences originated in the application of standard thresholding approaches to patient networks with decreased functional correlations. Subsequently, healthy adolescent development is studied, to help understand the frequent emergence of psychiatric disorders in this period. Importantly, both structural and functional brain networks undergo maturational shifts in correlation distribution over adolescence. Due to reliance of structural correlation networks on a group of subjects, previous studies of adolescent structural network development divided groups into discrete age-bins. Here, a novel sliding-window method is used to describe adolescent development of structural correlation networks in a continuous manner. Moreover, networks are probabilistically thresholded by retaining edges that are most consistent across bootstrapped samples of participants, leading to clearer maturational trajectories. These structural networks show non-linear trajectories of adolescent development driven by changes in association cortical areas, compatible with a developmental process of pruning combined with consolidation of surviving connections. Robustness of the results is demonstrated using extensive sensitivity analyses. Finally, adolescent developmental changes in functional network architecture are described, focusing on the characterisation of unthresholded (fully weighted) networks. The distribution of functional correlations presents a non-uniform shift over adolescence. Initially strong cortical connections to primary sensorimotor areas further strengthen into adulthood, whereas association cortical and subcortical edges undergo a subtler reorganisation of functional connectivity. Furthermore, individual subcortical regions show distinct maturational profiles. Patterning of maturation according to known functional systems is affirmed by partitioning regions developing at similar rates into maturational modules. Taken together, this thesis comprises novel methods for the characterisation of disease-related and normative developmental changes in structural and functional correlation brain networks. These methods are generalizable to a wide range of scenarios, beyond the specific disease and developmental age-ranges presented herein.

In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.

Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention.

The proper functioning of the auditory processing needs an integration of many types of information, and a synchronised action between auditory cortex and other cortical and subcortical centres. The normal development of connectivity between the auditory system and the higher neurocognitive functions depends on sensory experience, and congenital hearing loss makes it essentially impossible. The aim of this work was to perform an electrophysiological analysis of auditory cortical areas in patients with cochlear implant (CI). Thirty implanted patients were included in the study. Twenty-four of them were prelingual patients and they were divided into three groups, according to the age at time of CI surgery and to the duration of CI use: group A - early implant and lengthy CI use, group B - late implant and lengthy CI use, group C - late implant and short CI use. The remaining six patients were affected by postlingual deafness, and they were included in the group D. Each patient group was compared with a normal hearing age matched control group. Each subject underwent an Event-related potentials (ERPs) evaluation and electroencephalographic registration. All data analysis were performed by using Loreta software (Low Resolution Electromagnetic Tomography). ERPs latencies were for the most part significantly longer in patients than in controls. Concerning the Event-related cortical activity, all the control groups showed a high and well-defined activation in frontals areas and the cingulate cortex, in the N200 and P300 time windows. A comparable activation in strength and timing, between patients and controls, was only found in the first prelingual patient group (A), and to a lesser extent in the second group (B), while patients belonging to the third prelingual group (C) showed a very low cortical activation, with no cyclic pattern. Postlingual patients (D) showed no difference in activation compared to controls. ln a second step of the study, functional connectivity was analysed from EEG data, in two different conditions: resting state and activation state. Default mode network, left and right Precuneus and associative visual cortex were examined. No difference between prelingual patients and controls was found in the first group (A). Functional connectivity showed a significant increase in the second (B) and third (C) prelingual patient group, especially in the activation state, and specifically between visual areas and Precuneus and posterior cingulate cortex, while postlingual patients (D) showed no difference compared to controls. Cochlear implant adds a new auditory modality in prelingual patients, allowing the creation of a functional network. This involves the areas implicated in sensory and cognitive modalities, and needs some time to form. The duration of CI use is crucial: prolonged CI use, in addiction to an early time of implant, can restore auditory network, allowing a normalization process, from both an audiological and a neurophysiological point of view. However, in the case of patients with postlingual hearing loss, cochlear implant seems to restore and reinforce a cortical network that has already been formed, before the onset of the hearing impairment.<br>Un corretto funzionamento del processamento uditivo necessità di una sincronizzazione tra corteccia uditiva ed altre unità corticali e subcorticali, e di elaborare molti tipi di informazioni differenti. Il normale sviluppo della connettività tra sistema uditivo a altre funzioni neurocognitive è strettaemente legato all’esperienza uditiva del soggetto. In questo senso la deprivazione uditiva rende impossibile un corretto sviluppo. Scopo del lavoro è stato valutare da un punto di vista elettrofisiologico l’attività corticale in pazienti con impianto cocleare. Il campione dello studio è costituito da trenta pazienti portatori di impianto cocleare (IC), dei quali 24 con un’ipoacusia preverbale e 6 postverbale. I soggetti preverbali sono stati suddivisi in tre gruppi, sulla base di due parametri, età di impianto e tempo di utilizzo del dispositivo: gruppo A – impianto precoce e lungo utilizzo; gruppo B – impianto tardivo e lungo utilizzo; gruppo C – impianto tardivo e breve periodo di utilizzo. I pazienti postverbali costituiscono il gruppo D. Ciascun gruppo di pazienti è stato confrontato con un gruppo di soggetti normoacusici, comparabile per età. Ogni soggetto è stato sottoposto a registrazione dei potenziali evento-correlati e a registrazione elettroencefalografica. Tutti i dati sono stati analizzati mediante l’utilizzo del software Loreta (Low Resolution Electromagnetic Tomography). Le latenze dei potenziali registrati sono risultati complessivamente maggiori nei pazienti rispetto ai controlli. Per quanto riguarda l’attivazione delle sorgenti corticali durante l’elicitazione dei potenziali, tutti i controlli hanno mostrato un’attivazione corticale definita e rilevante, in corrispondenza delle aree frontali e del cingolato, sia per quanto riguarda la N200 che per la P300. Un’attivazione corticale simile si è riscontrata solo nei pazienti appartenenti al gruppo A, e in misura minore a quelli del gruppo B, mentre i pazienti del gruppo C hanno mostrato un’attivazione corticale molto bassa, e senza un pattern ciclico. Nei pazienti postverbali del gruppo D invece non sono state riscontrate differenze di attivazione rispetto ai relativi controlli. In una seconda fase dello studio è stata valutata la connettività funzionale, mediante analisi dei dati EEG, in due differenti condizioni: stato di veglia rilassata e stato di attivazione. Sono stati analizzati il Default mode network, il precuneo, la corteccia visiva. Anche in questo caso il gruppo A di pazienti non ha mostrato differenze con i controlli, in termini di connettività. I pazienti del gruppo B, e ancora di più quelli del gruppo C, hanno mostrato valori più alti di connettività, specialmente per quanto riguarda lo stato di attivazione. Anche in questa analisi i pazienti del gruppo D non hanno mostrato differenze rispetto ai controlli. L’impianto cocleare crea una nuova modalità uditiva nei pazienti preverbali, permettendo la creazione di un network funzionale che richiede del tempo per formarsi, e che coinvolge aree implicate in attività di tipo sensoriale e cognitivo. Fondamentale per un miglioramento in termini audiologici e neurofisiologici è risultato il parametro di durata di utilizzo dell’impianto cocleare. Nei pazienti postverbali invece l’impianto cocleare va a ripristinare un network corticale già formato prima dell’insorgenza dell’ipoacusia.

L’étude des propriétés fractales des séries biologiques fait l’objet d’un intérêt croissant. Néanmoins la littérature met en évidence une ambiguïté quand à l’explication causale de la présence de ces séries temporelles ne permettant pas de distinguer entre l’adaptation effective réalisée par un sujet ou ses capacités d’adaptabilité globales. La présente thèse a pour objectif de décorréler ces deux notions, notamment en liant le niveau comportemental au niveau cérébral. Notre première étude a permise de mettre en évidence que les propriétés mono-fractales pourraient refléter l’adaptabilité des sujets tandis que les propriétés multifractales seraient liées à l’adaptation effective réalisée au cours de la tâche. La seconde étude à mise en évidence une corrélation entre les propriétés multifractales et le nombre de réseaux cérébraux mis en oeuvre au cours de la tâche, reflétant l’adaptation effective aux contraintes expérimentales imposées. Les résultats de ces travaux de thèse nous ont permis de mieux comprendre la signification fonctionnelle des analyses fractales en terme d’adaptation effective et d’adaptabilité<br>The study of fractal properties in biological time series is of increasing interest. Nevertheless, the literature highlights an ambiguity on the causal explanation of the presence of these time series which does not make it possible to distinguish between the effective adaptation made by a subject or his overall adaptability capacities. The aim of this dissertation is to decorrelate these two notions, notably by linking the behavioral level to the cerebral level. Our first study allowed to highlight that the mono-fractal properties could reflect the adaptability of the subjects whereas the multifractal properties would be related to the effective adaptation carried out during the task. The second study showed a correlation between the multifractal properties and the number of brain networks implemented during the task, reflecting the effective adaptation to the experimental constraints imposed. The results of this work have allowed us to better understand the functional meaning of fractal analyzes in terms of effective adaptation and adaptability

El balance del cerebro se altera a nivel estructural y funcional por el consumo de alcohol y puede causar trastornos por consumo de alcohol (TCA). El objetivo de esta Tesis Doctoral fue investigar los efectos del consumo crónico y excesivo de alcohol en el cerebro desde una perspectiva funcional y estructural, mediante análisis de imágenes multimodales de resonancia magnética (RM). Realizamos tres estudios con objetivos específicos: i) Para entender cómo las neuroadaptaciones desencadenadas por el consumo de alcohol se ven reflejadas en la conectividad cerebral funcional entre redes cerebrales, así como en la actividad cerebral, realizamos estudios en ratas msP en condiciones de control y tras un mes con acceso a alcohol. Para cada sujeto se obtuvieron las señales específicas de sus redes cerebrales tras aplicar análisis probabilístico de componentes independientes y regresión espacial a las imágenes funcionales de RM en estado de reposo (RMf-er). Después, estimamos la conectividad cerebral en estado de reposo mediante correlación parcial regularizada. Para una lectura de la actividad neuronal realizamos un experimento con imágenes de RM realzadas con manganeso. En la condición de alcohol encontramos hipoconectividades entre la red visual y las redes estriatal y sensorial; todas con incrementos en actividad. Por el contrario, hubo hiperconectividades entre tres pares de redes cerebrales: 1) red prefrontal cingulada media y red estriatal, 2) red sensorial y red parietal de asociación y 3) red motora-retroesplenial y red sensorial, siendo la red parietal de asociación la única red sin incremento de actividad. Estos resultados indican que las redes cerebrales ya se alteran desde una fase temprana de consumo continuo y prolongado de alcohol, disminuyendo el control ejecutivo y la flexibilidad comportamental. ii) Para comparar el volumen de materia gris (MG) cortical entre 34 controles sanos y 35 pacientes con dependencia al alcohol, desintoxicados y en abstinencia de 1 a 5 semanas, realizamos un análisis de morfometría basado en vóxel. Las principales estructuras cuyo volumen de MG disminuyó en los sujetos en abstinencia fueron el giro precentral (GPreC), el giro postcentral (GPostC), la corteza motora suplementaria (CMS), el giro frontal medio (GFM), el precúneo (PCUN) y el lóbulo parietal superior (LPS). Disminuciones de MG en el volumen de esas áreas pueden dar lugar a cambios en el control de los movimientos (GPreC y CMS), en el procesamiento de información táctil y propioceptiva (GPostC), personalidad, previsión (GFM), reconocimiento sensorial, entendimiento del lenguaje, orientación (PCUN) y reconocimiento de objetos a través de su forma (LPS). iii) Caracterizar estados cerebrales dinámicos en señales de RMf mediante una metodología basada en un modelo oculto de Markov (HMM en inglés)-Gaussiano en un paradigma con diseño de bloques, junto con distintas señales temporales de múltiples redes: componentes independientes y modos funcionales probabilísticos (PFMs en inglés) en 14 sujetos sanos. Cuatro condiciones experimentales formaron el paradigma de bloques: reposo, visual, motora y visual-motora. Mediante la aplicación de HMM-Gaussiano a los PFMs pudimos caracterizar cuatro estados cerebrales a partir de la actividad media de cada PFM. Los cuatro mapas espaciales obtenidos fueron llamados HMM-reposo, HMM-visual, HMM-motor y HMM-RND (red neuronal por defecto). HMM-RND apareció una vez el estado de tarea se había estabilizado. En un futuro cercano se espera obtener estados cerebrales en nuestros datos de RMf-er en ratas, para comparar dinámicamente el comportamiento de las redes cerebrales como un biomarcador de TCA. En conclusión, las técnicas de neuroimagen aplicadas en imagen de RM multimodal para estimar la conectividad cerebral en estado de reposo, la actividad cerebral y el volumen de materia gris han permitido avanzar en el entendimiento de los mecanismos homeostático<br>La ingesta d'alcohol altera el balanç del cervell a nivell estructural i funcional i pot causar trastorns per consum d' alcohol (TCA). L'objectiu d'aquesta Tesi Doctoral fou estudiar els efectes en el cervell del consum crònic i excessiu d'alcohol, des d'un punt de vista funcional i estructural i per mitjà d'anàlisi d'imatges de ressonància magnètica (RM). Vam realitzar tres anàlisis amb objectius específics: i) Per a entendre com les neuroadaptacions desencadenades pel consum d'alcohol es veuen reflectides en la connectivitat cerebral funcional entre xarxes cerebrals, així com en l'activitat cerebral, vam realitzar estudis en rates msP en les condicions de control i després d'un mes amb accés a alcohol. Per a cada subjecte vam obtindre els senyals de les xarxes cerebrals tras aplicar a les imatges funcionals de RM en estat de repòs una anàlisi probabilística de components independents i regressió espacial. Després, estimàrem la connectivitat cerebral en estat de repòs per mitjà de correlació parcial regularitzada. Per a una lectura de l'activitat cerebral vam adquirir imatges de RM realçades amb manganés. En la condició d'alcohol vam trobar hipoconnectivitats entre la xarxa visual i les xarxes estriatal i sensorial, totes amb increments en activitat. Al contrari, va haver-hi hiperconnectivitats entre tres parells de xarxes cerebrals: 1) xarxa prefrontal cingulada mitja i xarxa estriatal, 2) xarxa sensorial i xarxa parietal d'associació i 3) xarxa motora-retroesplenial i xarxa sensorial, sent la xarxa parietal d'associació l'única xarxa sense increment d'activitat. Aquests resultats indiquen que les xarxes cerebrals ja s'alteren des d'una fase primerenca caracteritzada per consum continu i prolongat d'alcohol, disminuint el control executiu i la flexibilitat comportamental. ii) Per a comparar el volum de MG cortical entre 34 controls sans i 35 pacients amb dependència a l'alcohol, desintoxicats i en abstinència de 1 a 5 setmanes vam emprar anàlisi de morfometria basada en vòxel. Les principals estructures on el volum de MG va disminuir en els subjectes en abstinència van ser el gir precentral (GPreC), el gir postcentral (GPostC), la corteça motora suplementària (CMS), el gir frontal mig (GFM), el precuni (PCUN) i el lòbul parietal superior (LPS). Les disminucions de MG en eixes àrees poden donar lloc a canvis en el control dels moviments (GPreC i CMS), en el processament d'informació tàctil i propioceptiva (GPostC), personalitat, previsió (GFM), reconeixement sensorial, enteniment del llenguatge, orientació (PCUN) i reconeixement d'objectes a través de la seua forma (LPS). iii) Caracterització de les dinàmiques temporals del cervell com a diferents estats cerebrals, en senyals de RMf mitjançant una metodologia basada en un model ocult de Markov (HMM en anglès)-Gaussià en imatges de RMf, junt amb dos tipus de senyals temporals de múltiples xarxes cerebrals: components independents i modes funcionals probabilístics (PFMs en anglès) en 14 subjectes sans. Quatre condicions experimentals van formar el paradigma de blocs: repòs, visual, motora i visual-motora. HMM-Gaussià aplicat als PFMs (senyals de RM funcional de xarxes cerebrals) va permetre la millor caracterització dels quatre estats cerebrals a partir de l'activitat mitjana de cada PFM. Els quatre mapes espacials obtinguts van ser anomenats HMM-repòs, HMM-visual, HMM-motor i HMM-XND (xarxa neuronal per defecte). HMM-XND va aparèixer una vegada una tasca estava estabilitzada. En un futur pròxim s'espera obtindre estats cerebrals en les nostres dades de RMf-er en rates, per a comparar dinàmicament el comportament de les xarxes cerebrals com a biomarcador de TCA. En conclusió, s'han aplicat tècniques de neuroimatge per a estimar la connectivitat cerebral en estat de repòs, l'activitat cerebral i el volum de MG, aplicades a imatges multimodals de RM i s'han obtés resultats que han permés avançar en l'enteniment dels m<br>Alcohol intake alters brain balance, affecting its structure and function, and it may cause Alcohol Use Disorders (AUDs). We aimed to study the effects of chronic, excessive alcohol consumption on the brain from a functional and structural point of view, via analysis of multimodal magnetic resonance (MR) images. We conducted three studies with specific aims: i) To understand how the neuroadaptations triggered by alcohol intake are reflected in between-network resting-state functional connectivity (rs-FC) and brain activity in the onset of alcohol dependence, we performed studies in msP rats in control and alcohol conditions. Group probabilistic independent component analysis (group-PICA) and spatial regression were applied to resting-state functional magnetic resonance imaging (rs-fMRI) images to obtain subject-specific time courses of seven resting-state networks (RSNs). Then, we estimated rs-FC via L2-regularized partial correlation. We performed a manganese-enhanced (MEMRI) experiment as a readout of neuronal activity. In alcohol condition, we found hypoconnectivities between the visual network (VN), and striatal (StrN) and sensory-cortex (SCN) networks, all with increased brain activity. On the contrary, hyperconnectivities were found between three pairs of RSNs: 1) medial prefrontal-cingulate (mPRN) and StrN, 2) SCN and parietal association (PAN) and 3) motor-retrosplenial (MRN) and SCN networks, being PAN the only network without brain activity rise. Interestingly, the hypoconnectivities could be explained as control to alcohol transitions from direct to indirect connectivity, whereas the hyperconnectivities reflected an indirect to an even more indirect connection. These findings indicate that RSNs are early altered by prolonged and moderate alcohol exposure, diminishing the executive control and behavioral flexibility. ii) To compare cortical gray matter (GM) volume between 34 healthy controls and 35 alcohol-dependent patients who were detoxified and remained abstinent for 1-5 weeks before MRI acquisition, we performed a voxel-based morphometry analysis. The main structures whose GM volume decreased in abstinent subjects compared to controls were precentral gyrus (PreCG), postcentral gyrus (PostCG), supplementary motor cortex (SMC), middle frontal gyrus (MFG), precuneus (PCUN) and superior parietal lobule (SPL). Decreases in GM volume in these areas may lead to changes in control of movement (PreCG and SMC), in processing tactile and proprioceptive information (PostCG), personality, insight, prevision (MFG), sensory appreciation, language understanding, orientation (PCUN) and the recognition of objects by touch and shapes (SPL). iii) To characterize dynamic brain states in functional MRI (fMRI) signals by means of an approach based on the Hidden Markov model (HMM). Several parameter configurations of HMM-Gaussian in a block-design paradigm were considered, together with different time series: independent components (ICs) and probabilistic functional modes (PFMs) on 14 healthy subjects. The block-design fMRI paradigm consisted of four experimental conditions: rest, visual, motor and visual-motor. Characterizing brain states' dynamics in fMRI data was possible applying the HMM-Gaussian approach to PFMs, with mean activity driving the states. The four spatial maps obtained were named HMM-rest, HMM-visual, HMM-motor and HMM-DMN (default mode network). HMM-DMN appeared once a task state had stabilized. The ultimate goal will be to obtain brain states in our rs-fMRI rat data, to dynamically compare the behavior of brain RSNs as a biomarker of AUD. In conclusion, neuroimaging techniques to estimate rs-FC, brain activity and GM volume can be successfully applied to multimodal MRI in the advance of the understanding of brain homeostasis in AUDs. These functional and structural alterations are a biomarker of chronic alcoholism to explain impairments in executive control, reward evaluation and visuospatial processing.<br>Pérez Ramírez, MÚ. (2018). Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/113164<br>TESIS

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.

Recent research, that has been focused on recognition memory, has revealed that two processes contribute to recognition of previously encountered items: recollection and familiarity (Aggleton & Brown, 1999; Eichenbaum, 2006; Eichenbaum, Yonelinas, & Ranganath, 2007; Rugg & Yonelinas, 2003; Skinner & Fernandes, 2007; Squire, Stark, & Clark, 2004; Wixted, 2007a; Yonelinas, 2001a; Yonelinas, 2002). The findings of neural correlates of recollection and familiarity lead to the assumption that there are different brain regions activated in either process, but there are, to the best of my knowledge, no studies assessing how these brain regions are working together in a recollection or a familiarity network, respectively. Additionally, there are almost no studies to date, which directly searched for overlapping regions. Therefore, in study I of the current thesis, brain regions associated to both recognition processes are searched investigated. Additionally, a connectivity analysis will search for functional correlated brain activations that either build a recollection or a familiarity network. It is undoubtable that the Brain Derived Neurotrophic Factor (BDNF) is strongly involved in synaptic plasticity in the hippocampus (Bramham & Messaoudi, 2005) and there is evidence that a genetic variant of this neurotrophin (BDNF 66Met) is related to poorer memory performance (Egan, et al., 2003). Therefore, in study II of the current thesis, the effect of BDNF Val66Met on recollection and familiarity performance and related brain activations is investigated. Finally, one could summarize, that serotonin, like BDNF, is strongly involved in brain development and plasticity as well as in learning and memory processes (Vizi, 2008). More precisely, there is evidence for alterations in the structure of brain regions, which are known to be involved in emotional memory formation and retrieval, like amygdala and hippocampus (Frodl, et al., 2008; Munafo, Brown, & Hariri, 2008; Pezawas, et al., 2005). One study found an slight epistatic effect of BDNF and 5-HTTLPR on the grey matter volume of the amygdala (Pezawas, et al., 2008). Therefore, in study III, it is investigated if such an interaction effect could be substantiated for the amygdala and additionally revealed for the hippocampus. The results of the current thesis allow further comprehension of recollection, hence episodic memory, and point to a special role of the BDNF in temporal and prefrontal brain regions. Additionally, the finding of an epistatic effect between BDNF and serotonin transporter function point to the need of analyzing interactions between genes and also between genes and environmental factors which reveals more information than the study of main effects alone. In conclusion, analyzing behavioral and neural correlates of episodic memory reveal allowed insights in brain functions that may serve as guideline for future studies in clinical populations with memory deficits, including susceptibility factors such as good or bad environment, as well as promising gene variants that influence episodic memory.

The study of the central nervous system represents a great challenge in the field of neuroscience. For years, various techniques have been developed to study neuronal cells in-vitro as it is difficult to conduct in-vivo experiments due to ethical problems deriving from its anatomical location. Consequently, both in-vivo and in-vitro animal models have been used extensively to gain new insights into basic functioning principles of neuronal tissue and therapeutic approaches for brain diseases. Over time, we have seen that there is a poor correlation between the clinical diagnosis and the underlying pathological mechanisms. In fact, some symptoms that may occur in the patient are not replicated in the animal, making many promising approaches in animal studies not translatable in the clinic. With the advent of human-induced pluripotent stem cells (h-iPSC) several protocols for the generation of human-neuronal cells are becoming available for all laboratories. The importance of this technique lies in the opportunity to develop a human model derived directly from the patient: the patient's in-vitro cells will exhibit the same genetic and epigenetic modifications as the in-vivo cells. This has raised hopes for the generation of engineered brain models that can be coupled to sensors / actuators in order to better investigate their functional properties in-vitro (i.e. brain-on-a-chip). A reliable method for evaluating the functionality of neuronal cultures is the study of the spontaneous electrophysiological activity using microelectrode arrays (MEA). There are numerous studies in the literature that used h-iPSC on MEAs, showing the characterization of neuronal patterns of patient-derived cultures, demonstrating how this platform is valid for disease phenotyping, drug discovery and translational medicine. Although these models helped to shed light on fundamental biological mechanisms, the majority is based on two-dimensional neuronal cultures, which lack some key features to mimic in-vivo behavior. Three-dimensional h-iPSC-derived models possess a microenvironment, tissue architecture and potential to model network activity with greater complexity than two-dimensional models. Depending on the purpose of the study, we can choose different approaches to recreate 3D in-vitro brain, from those that aim to reproduce the trajectories of neurodevelopment (i.e. brain-organoids) to the use of synthetic materials that reproduce the functionalities of the extracellular matrix (ECM) (i.e. scaffold-based) (Chiaradia and Lancaster, 2020, Tang et al., 2006). Although h-iPSC-derived brain models summarize many aspects of network function in the human brain, they are subject to variability and still do not perfectly mimic behavior in-vivo. Therefore, to reach the full potential of this model we need improvements in differentiation methods and bioengineering, making these models engineered and reproducible. The aim of this PhD thesis was to implement different 3D neuronal culture generation methodologies that can be integrated on MEA devices to offer robust engineered platforms for functional studies.

Abstract Neuropsychiatric illnesses usually become overtly manifest in adolescence and early adulthood. A critical long-term aim is to be able to prevent the development of such illnesses, which requires instruments to identify subjects at high risk of illness and to offer them effective interventions. There is an indisputable need for more sophisticated methods to enable more precise detection of adolescents and young adults who are at high risk of developing psychosis. Abnormal function in brain networks has been reported in people with schizophrenia and other psychotic disorders. Similar abnormalities have been found also in people at risk for developing psychosis, but it is not known whether this applies also to spontaneous resting state activity in young people with a familial risk for psychosis. We conducted resting-state functional MRI (R-fMRI) in 72 (29 male) young adults with a history of psychosis in one or both parents (FR) but without psychosis themselves, and 72 (29 male) similarly healthy control subjects without familial risk for psychosis. Both groups in the Oulu Brain and Mind study were drawn from the Northern Finland Birth Cohort 1986. All volunteers were 20–25 years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data was pre-processed using independent component analysis (ICA). A dual regression technique was used to detect between-group differences with p &lt; 0.05 threshold corrected for multiple comparisons at voxel level. FR subjects demonstrated significantly decreased activity compared to control subjects in the default mode network and in the central executive network and increased activity in the cerebellum. The findings clarify previously controversial literature on the subject. The finding suggests that abnormal activity in these brain networks in rest may be associated with increased vulnerability to psychosis. The findings maybe helpful in developing more precise methods for detecting young people at highest risk for developing psychosis<br>Tiivistelmä Psykoottisiin häiriöihin sairastutaan yleensä nuoruudessa tai varhaisaikuisuudessa. Psykoositutkimuksen tavoitteena on löytää uusia menetelmiä, joiden avulla kyettäisiin tunnistamaan suurimmassa psykoosiriskissä olevat nuoret, jotta heille voitaisiin tarjota sairautta ennaltaehkäiseviä hoitokeinoja. Skitsofreniaan ja muihin psykoottisiin häiriöihin sairastuneilla on havaittu aivotoiminnan poikkeavuuksia. Samankaltaisia aivotoiminnan poikkeavuuksia on havaittu myös nuorilla, jotka ovat vaarassa sairastua psykoosiin. Toistaiseksi on ollut epäselvää, onko psykoosiin sairastuneiden henkilöiden lapsilla aivohermoverkkojen toiminnan poikkeavuuksia lepotilassa. Suoritimme aivojen lepotilan MRI-tutkimuksen (R-fMRI) 72:lle (29 miestä) nuorelle aikuiselle, joiden jompikumpi vanhempi oli sairastunut psykoosin sekä 72:lle (29 miestä) nuorelle aikuiselle, joiden vanhemmat eivät olleet sairastaneet psykoosia. Molemmat tutkimusryhmät tässä Oulu Brain and Mind -tutkimuksessa olivat Pohjois-Suomen 1986 syntymäkohortin jäseniä. Tutkittavat olivat 20–25 vuoden iässä. Lepotilan toiminnallinen magneettikuvaus suoritettiin 1.5 Teslan Siemensin magneettikuvantamislaitteella. Tutkimuskohteiksi valittiin lepotilan toiminnallinen aivohermoverkko, toiminnan ohjauksesta vastaava aivohermoverkko ja pikkuaivot. Kuvantamisdataan sovellettiin itsenäisten komponenttien analyysia aivohermoverkkojen määrittämistä varten. Ryhmien välisen eron havaitsemiseen käytettiin ei-parametristä permutaatiotestiä, joka kynnystettiin tilastollisesti merkitsevään tasoon (p &lt; 0.05). Lepotilan oletushermoverkossa ja toiminnanohjauksesta vastaavassa aivohermoverkoissa havaittiin vähäisempää aktiivisuutta ja pikkuaivoissa kohonnutta aktiivisuutta perinnöllisessä psykoosiriskissä olevilla nuorilla aikuisilla verrattuna verrokkeihin. Tutkimustulokset selkeyttivät aiempaa ristiriitaista kirjallisuutta tutkimusaiheesta. Tutkimuksessa havaittujen aivoalueiden poikkeava toiminta lepotilassa voi liittyä kohonneeseen psykoosin puhkeamisriskiin. Tutkimuslöydösten avulla voidaan todennäköisesti edesauttaa parempien kuvantamismenetelmien kehittämistä suurimmassa psykoosiriskissä olevien nuorten tunnistamiseen

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.

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<br>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

A ballistic pendulum impulse generator was used to impact networks in primary culture growing on microelectrode arrays. This approach has the advantage of imparting pure tangential acceleration insults (50 to 300 g) with simultaneous morphological and electrophysiological multichannel monitoring for days before and after the impact. Action potential (AP) production, network activity patterns, and cell electrode coupling of individual units using AP waveshape templates were quantified. Network adhesion was maintained after tangential impacts up to 300g with minimal loss of pre-selected active units. Time lapse phase contrast microscopy revealed stable nuclei pre-impact, but post impact nuclear rotation in 95% of observations (n= 30). All recording experiments (n=31) showed a repeatable two-phase spike production response profile: recovery to near reference in 1-2 hrs, followed by a slow activity decay to a stable, level plateau approximately 30-40% below reference. Phase 1 consisted of a complex two-step recovery: rapid activity increase to an average 23.6% (range: 11-34%) below reference, forming a level plateau lasting from 5 to 20 min, followed by a climb to within 20% of reference where a second plateau was established for 1 to 2 hrs. Cross correlation profiles showed changes in firing hierarchy after impact, and in spontaneous network oscillatory activity. Native oscillations were found in the Delta band (2 to 3 Hz), and decreased by approximately 20% after impact. Under network disinhibition with bicuculline, oscillations were slower (0.8-1Hz) and decreased 40% after impact. These data link network performance deficits with microscopically observable subcellular changes.

Tissue engineers – in order to develop therapies for the treatment of complex neurological injuries and diseases – attempt to recreate elaborate developmental mechanisms in vitro. Neuronal precursor cells are excellent candidates for the study of developmental operations such as cell adhesion, differentiation, and axonal pathfinding. Hyaluronan (HA) is a common polysaccharide that is found extensively throughout the neuronal extracellular matrix (ECM), and can be functionalised and crosslinked to form stable hydrogels that support growing neuronal cells. Hyaluronan hydrogels can be modified chemically and mechanically to mimic the ECM of the developing brain, awarding control over mechanisms such as differentiation and axonal pathfinding. This thesis is concerned with the functionalisation and characterisation of HA hydrogels, ultimately in order to simulate vital properties of the developing brain. Here we show that HA hydrogels can be finely tuned mechanically (by modulating stiffness and viscosity), and chemically, by the conjugation of peptides that mimic the neural cell adhesion molecule (NCAM). NCAM mimics and novel mimics of sialylated NCAM significantly influence the differentiation of NSPCs in 2D and 3D. HA hydrogels successfully support long term culture of neural cells in 3D, and encourage the formation and extension of neurites of several cell types including human, mouse and rat neuronal precursor and stem cells. These results demonstrate for the first time that novel NCAM mimicking peptides can be conjugated to well defined hydrogel matrices that influence intricate developmental behaviours in 3D. Understanding how neural cells form functional networks is essential for the development of clinical approaches that attempt to address the injuries and diseases that affect these systems.

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analyzed using a phase synchronization based measure, minimum spanning tree and k-core decomposition. The analysis was performed for each classical brain rhythm separately. Furthermore, we aim to provide a network approach insensitive to the effects that epoch length has on functional connectivity (FC) and network reconstruction. Two different measures, the phase lag index (PLI) and the Amplitude Envelope Correlation (AEC), were applied to EEG resting-state recordings for a group of eighteen healthy volunteers. Weighted clustering coefficient (CCw), weighted characteristic path length (Lw) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Results about distinctive functional core, show highest classification rates from k-core decomposition in gamma (EER=0.130, AUC=0.943) and high beta (EER=0.172, AUC=0.905) frequency bands. Results from scalp analysis concerning the influence of epoch length, show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 seconds for PLI and 6 seconds for AEC. Moreover, CCw and Lw show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 seconds versus 4-8 seconds for AEC). At the source-level the results were even more reliable, with stability already at 1 second duration for PLI-based MSTs. Our results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. Regarding epoch length, the present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.

The advancement of magnetic resonance imaging (MRI) has allowed us to begin to explore how brain regions work collectively in order to produce important functions. The cognitive control network (CCN)—involving the dorsolateral prefrontal cortex, the dorsal parietal cortex and the dorsal anterior cingulate cortex—is associated with the production of goal-directed or “cognitive control” behaviors, which are imperative to our intellectual, social and emotional processes. However, how certain properties of this network directly relate to function is yet to be established. This thesis aimed to use structural and functional MRI to examine how changes in this network over time relate to behavioral change in order to better understand the mechanisms of cognitive control. The first three chapters linked task-related functional activity and connectivity in the CCN, structural development of the CCN and intrinsic connectivity of the CCN and a related network (the default mode network (DMN)), to change in behavioral ability over time in healthy controls. Finally, we examined how CCN and DMN activity may vary in mood disorders in which cognitive control function is impaired. Across all three analyses of the CCN in healthy controls we found change in volume, connectivity and activation of the dorsal parietal node to have a relationship with change in behavior. Additionally, all these relationships were independent of age, introducing the possibility that change in this circuitry is being driven by experience-dependent mechanisms. Finally, we found that failure to suppress the DMN, which is typically down-regulated during cognitive tasks, is a common feature of asymptomatic bipolar and unipolar depressive disorders and could relate to cognitive control dysfunction. This work helps in understanding the neural correlates of cognitive control function and, with future work, could aid in the development of targeted treatments to address cognitive control dysfunction.

The brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain. The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects. In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them. Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices. Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning.

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.

This study investigated the effects of lesion presence within larger brain networks (e.g., default mode network (DMN), salience network (SN), and mentalizing network (MN)) in the chronic phase of a pediatric traumatic brain injury (TBI) and the effect on social function. We compared children with a TBI to children with an orthopedic injury (OI) with three different aims. The first aim was to determine whether network volume differed by group (e.g., TBI vs. OI). Second, investigate if lesion presence in a sub component region of the network resulted in total network volume loss for that network. Finally, learn whether network volume would predict outcome on the Behavior Assessment System for Children, Second Edition (BASC-2). Approximately 184 participants (65% male; 70% Caucasian) between the ages of 6-17 years completed testing and a structural MRI scan in the chronic stage (at least one-year post-injury) of the injury. Injury severity included complicated mild, moderate, and severe TBI. Radiological findings were analyzed using recommendations from the Common Data Elements' core (presence or absence of a lesion) and supplementary (lesion type and location) recommendations. Volumetrics for all participants were obtained with FreeSurfer to quantify total network volumes for the DMN, SN, and MN. The parent of each participant completed a behavioral measure for externalizing and internalizing behaviors. Three sets of statistical analyses were completed, including multivariate analysis of covariance, analysis of covariance, and multiple regression, for each of the three aims of the study, respectively. There were significant differences in total DMN volume between the two groups and participants with lesions solely in the MN had lower total MN volume. Moreover, lower total MN volume was associated with worse functioning on measures of externalizing and internalizing behaviors. The larger implications, including developmental and social implications, of these findings are discussed.

Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease. This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed.

This research focused on the development of a methodology for analyzing functional magnetic resonance imaging (fMRI) data collected from patients with epilepsy in order to map epileptic networks. Epilepsy, a chronic neurological disorder characterized by recurrent, unprovoked seizures, affects up to 1% of the world's population. Antiepileptic drug therapies either do not successfully control seizures or have unacceptable side effects in over 30% of patients. Approximately one-third of patients whose seizures cannot be controlled by medication are candidates for surgical removal of the affected area of the brain, potentially rendering them seizure free. Accurate localization of the epileptogenic focus, i.e., the area of seizure onset, is critical for the best surgical outcome. The main objective of the research was to develop a set of fMRI data features that could be used to distinguish between normal brain tissue and the epileptic focus. To determine the best combination of features from various domains for mapping the focus, genetic programming and several feature selection methods were employed. These composite features and feature sets were subsequently used to train a classifier capable of discriminating between the two classes of voxels. The classifier was then applied to a separate testing set in order to generate maps showing brain voxels labeled as either normal or epileptogenic based on the best feature or set of features. It should be noted that although this work focuses on the application of fMRI analysis to epilepsy data, similar techniques could be used when studying brain activations due to other sources. In addition to investigating in vivo data collected from temporal lobe epilepsy patients with uncertain epileptic foci, phantom (simulated) data were created and processed to provide quantitative measures of the efficacy of the techniques.