Academic literature on the topic 'Graph fMRI brain resting state functional connectivity'
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Journal articles on the topic "Graph fMRI brain resting state functional connectivity"
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Razi, Adeel, Mohamed L. Seghier, Yuan Zhou, et al. "Large-scale DCMs for resting-state fMRI." Network Neuroscience 1, no. 3 (2017): 222–41. http://dx.doi.org/10.1162/netn_a_00015.
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This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.
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Aqil, Marco, Selen Atasoy, Morten L. Kringelbach, and Rikkert Hindriks. "Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome." PLOS Computational Biology 17, no. 1 (2021): e1008310. http://dx.doi.org/10.1371/journal.pcbi.1008310.
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Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed “connectome harmonics”, have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.
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Mechling, Anna E., Tanzil Arefin, Hsu-Lei Lee, et al. "Deletion of the mu opioid receptor gene in mice reshapes the reward–aversion connectome." Proceedings of the National Academy of Sciences 113, no. 41 (2016): 11603–8. http://dx.doi.org/10.1073/pnas.1601640113.
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Connectome genetics seeks to uncover how genetic factors shape brain functional connectivity; however, the causal impact of a single gene’s activity on whole-brain networks remains unknown. We tested whether the sole targeted deletion of the mu opioid receptor gene (Oprm1) alters the brain connectome in living mice. Hypothesis-free analysis of combined resting-state fMRI diffusion tractography showed pronounced modifications of functional connectivity with only minor changes in structural pathways. Fine-grained resting-state fMRI mapping, graph theory, and intergroup comparison revealed Oprm1-specific hubs and captured a unique Oprm1 gene-to-network signature. Strongest perturbations occurred in connectional patterns of pain/aversion-related nodes, including the mu receptor-enriched habenula node. Our data demonstrate that the main receptor for morphine predominantly shapes the so-called reward/aversion circuitry, with major influence on negative affect centers.
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Hamdi, Shah Muhammad, Yubao Wu, Rafal Angryk, Lisa Crystal Krishnamurthy, and Robin Morris. "Identification of Discriminative Subnetwork from fMRI-Based Complete Functional Connectivity Networks." International Journal of Semantic Computing 13, no. 01 (2019): 25–44. http://dx.doi.org/10.1142/s1793351x19400026.
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The comprehensive set of neuronal connections of the human brain, which is known as the human connectomes, has provided valuable insight into neurological and neurodevelopmental disorders. Functional Magnetic Resonance Imaging (fMRI) has facilitated this research by capturing regionally specific brain activity. Resting state fMRI is used to extract the functional connectivity networks, which are edge-weighted complete graphs. In these complete functional connectivity networks, each node represents one brain region or Region of Interest (ROI), and each edge weight represents the strength of functional connectivity of the adjacent ROIs. In order to leverage existing graph mining methodologies, these complete graphs are often made sparse by applying thresholds on weights. This approach can result in loss of discriminative information while addressing the issue of biomarkers detection, i.e. finding discriminative ROIs and connections, given the data of healthy and disabled population. In this work, we demonstrate a novel framework for representing the complete functional connectivity networks in a threshold-free manner and identifying biomarkers by using feature selection algorithms. Additionally, to compute meaningful representations of the discriminative ROIs and connections, we apply tensor decomposition techniques. Experiments on a fMRI dataset of neurodevelopmental reading disabilities show the highly interpretable nature of our approach in finding the biomarkers of the diseases.
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RANGAPRAKASH, D., XIAOPING HU, and GOPIKRISHNA DESHPANDE. "PHASE SYNCHRONIZATION IN BRAIN NETWORKS DERIVED FROM CORRELATION BETWEEN PROBABILITIES OF RECURRENCES IN FUNCTIONAL MRI DATA." International Journal of Neural Systems 23, no. 02 (2013): 1350003. http://dx.doi.org/10.1142/s0129065713500032.
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It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.
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Faivre, Anthony, Emmanuelle Robinet, Maxime Guye, et al. "Depletion of brain functional connectivity enhancement leads to disability progression in multiple sclerosis: A longitudinal resting-state fMRI study." Multiple Sclerosis Journal 22, no. 13 (2016): 1695–708. http://dx.doi.org/10.1177/1352458516628657.
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Background: The compensatory effect of brain functional connectivity enhancement in relapsing-remitting multiple sclerosis (RRMS) remains controversial. Objective: To characterize the relationships between brain functional connectivity changes and disability progression in RRMS. Methods: Long-range connectivity, short-range connectivity, and density of connections were assessed using graph theoretical analysis of resting-state functional magnetic resonance imaging (fMRI) data acquired in 38 RRMS patients (disease duration: 120 ± 32 months) and 24 controls. All subjects were explored at baseline and all patients and six controls 2 years later. Results: At baseline, levels of long-range and short-range brain functional connectivity were higher in patients compared to controls. During the follow-up, decrease in connections’ density was inversely correlated with disability progression. Post-hoc analysis evidenced differential evolution of brain functional connectivity metrics in patients according to their level of disability at baseline: while patients with lowest disability at baseline experienced an increase in all connectivity metrics during the follow-up, patients with higher disability at baseline showed a decrease in the connectivity metrics. In these patients, decrease in the connectivity metrics was associated with disability progression. Conclusion: The study provides two main findings: (1) brain functional connectivity enhancement decreases during the disease course after reaching a maximal level, and (2) decrease in brain functional connectivity enhancement participates in disability progression.
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Simos, Nicholas John, Stavros I. Dimitriadis, Eleftherios Kavroulakis, et al. "Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach." Brain Sciences 10, no. 11 (2020): 777. http://dx.doi.org/10.3390/brainsci10110777.
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Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.
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V. Farahani, Farzad, Magdalena Fafrowicz, Waldemar Karwowski, et al. "Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory." Brain Sciences 11, no. 1 (2021): 111. http://dx.doi.org/10.3390/brainsci11010111.
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Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.
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Kim, Kamin, Matthew S. Sherwood, Lindsey K. McIntire, R. Andy McKinley, and Charan Ranganath. "Transcranial Direct Current Stimulation Modulates Connectivity of Left Dorsolateral Prefrontal Cortex with Distributed Cortical Networks." Journal of Cognitive Neuroscience 33, no. 7 (2021): 1381–95. http://dx.doi.org/10.1162/jocn_a_01725.
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Abstract Studies have shown that transcranial direct current stimulation increases neuronal excitability of the targeted region and general connectivity of relevant functional networks. However, relatively little is understood of how the stimulation affects the connectivity relationship of the target with regions across the network structure of the brain. Here, we investigated the effects of transcranial direct current stimulation on the functional connectivity of the targeted region using resting-state fMRI scans of the human brain. Anodal direct current stimulation was applied to the left dorsolateral prefrontal cortex (lDLPFC; cathode on the right bicep), which belongs to the frontoparietal control network (FPCN) and is commonly targeted for neuromodulation of various cognitive functions including short-term memory, long-term memory, and cognitive control. lDLPFC's connectivity characteristics were quantified as graph theory measures, from the resting-state fMRI scans obtained prior to and following the stimulation. Critically, we tested pre- to poststimulation changes of the lDLPFC connectivity metrics following an active versus sham stimulation. We found that the stimulation had two distinct effects on the connectivity of lDLPFC: for Brodmann's area (BA) 9, it increased the functional connectivity between BA 9 and other nodes within the FPCN; for BA 46, net connectivity strength was not altered within FPCN, but connectivity distribution across networks (participation coefficient) was decreased. These findings provide insights that the behavioral changes as the functional consequences of stimulation may come about because of the increased role of lDLPFC in the FPCN.
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Song, Ke, Juan Li, Yuanqiang Zhu, Fang Ren, Lingcan Cao, and Zi-Gang Huang. "Altered Small-World Functional Network Topology in Patients with Optic Neuritis: A Resting-State fMRI Study." Disease Markers 2021 (June 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/9948751.
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Aim. This study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory. Methods. A total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest. Small-world network parameters and area under the integral curve (AUC) were calculated from pairwise brain interval correlation coefficients. Differences in brain network parameter AUCs between the 2 groups were evaluated with the independent sample t -test, and changes in brain connection strength between ON patients and control subjects were assessed by network-based statistical analysis. Results. In the sparsity range from 0.08 to 0.48, both groups exhibited small-world attributes. Compared to the control group, global network efficiency, normalized clustering coefficient, and small-world value were higher whereas the clustering coefficient value was lower in ON patients. There were no differences in characteristic path length, local network efficiency, and normalized characteristic path length between groups. In addition, ON patients had lower brain functional connectivity strength among the rolandic operculum, medial superior frontal gyrus, insula, median cingulate and paracingulate gyri, amygdala, superior parietal gyrus, inferior parietal gyrus, supramarginal gyrus, angular gyrus, lenticular nucleus, pallidum, superior temporal gyrus, and cerebellum compared to the control group ( P < 0.05 ). Conclusion. Patients with ON show typical “small world” topology that differed from that detected in HC brain networks. The brain network in ON has a small-world attribute but shows reduced and abnormal connectivity compared to normal subjects and likely causes symptoms of cognitive impairment.
Dissertations / Theses on the topic "Graph fMRI brain resting state functional connectivity"
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Schäfer, Alexander. "Identifying Changes of Functional Brain Networks using Graph Theory." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-166041.
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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.
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Rossi, Magi Lorenzo. "Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8332/.
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Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani.
L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra.
L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata.
In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo.
La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione.
Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.
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García-García, Isabel, María Ángeles Jurado, Maite Garolera, et al. "Functional network centrality in obesity: a resting-state and task fMRI study." Psychiatry research (2015) 233, 3, S. 331-338, 2015. https://ul.qucosa.de/id/qucosa%3A14785.
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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.
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Williams, Kathleen Anne. "Resting State Connectivity in the Rat Brain." Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14059.
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Functional MRI is a method of imaging changes in blood oxygenation that accompany neural activity in the brain. A specific area within fMRI studies investigates what the brain is doing when it is not being stimulated. It is postulated that there are distinctly separate regions of the brain that are connected based upon functional relations and that these connected regions synchronously communicate even during rest. Resting state connectivity has become a tool to investigate neurological disorders in humans without specific knowledge of the mechanisms that correlate neural activity with brain metabolism and blood flow. This work attempts to characterize resting state connectivity in the rat brain to establish a model that will help elucidate the relationship between functional connectivity, as measured with fMRI, and brain function. Four analysis techniques, power spectrum estimation, cross correlation analysis, principle component analysis, and independent component analysis, are employed to examine data acquired during a non-stimulation, single-slice, gradient echo EPI sequence in search of functionally connected, spatially distant regions of the rat brain.
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Guzmán-Veléz, Edmarie. "Association between bilingualism and functional brain connectivity in older adults." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2217.
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Older bilingual adults typically perform better than monolinguals in tasks of executive control, and are diagnosed later with dementia. Studies have also shown structural and functional brain differences between bilinguals and monolinguals. However, it remains poorly understood how language history influences the functional organization of the aging brain. The current study investigated; 1) differences in resting-state functional connectivity between monolinguals and bilinguals in the Default Mode Network (DMN), Frontoparietal Network (FPN), Executive Control Network (ECN), Language Network (LANG), and a network consisting of structures associated with tasks of executive control coined the Bilingual Control Network (BCN); 2) the relationship of cognitive performance with functional connectivity of the BCN; and 3) whether proficiency, age of second language acquisition, degree of second language exposure, and frequency of language use predicts the network’s functional connectivity. Healthy older bilinguals (N=10) were matched pairwise for age, sex and education to healthy older monolinguals (N=10). All participants completed a battery of cognitive tests, a language history questionnaire, and a 6-minute functional scan during rest. Results showed that groups did not differ in cognitive performance, or in the functional connectivity of the FPN, ECN, LANG, or BCN. However, monolinguals had significantly stronger functional connectivity in the DMN compared to bilinguals. Later age of second language acquisition and lower proficiency were also associated with greater DMN functional connectivity. None of these variables predicted BCN’s functional connectivity. However, bilinguals showed stronger functional connectivity with other structures outside of the canonical networks compared to monolinguals. Finally, vocabulary scores, local switch cost accuracy and reaction time were negatively correlated with BCN’s functional connectivity. Overall, these findings illustrate differences in functional brain organization associated with language experience in the DMN, while challenging the “bilingual advantage” hypothesis. The results also suggest a possible neural mechanism by which bilingualism might mediate cognitive reserve.
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Demirtaş, Murat. "Exploring functional connectivity dynamics in brain disorders: a whole-brain computational framework for resting state fMRI signals." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/350799.
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Brain activity, on every scale, spontaneously fluctuates, thereby exhibiting complex, dynamic interactions that manifest rich synchronization patterns. The past ten years have been dominated by studies intended to further our understanding of the mecha-nisms behind the dynamic interactions within the brain through the basis of its structural and functional connectivity structures. Moreover, there is a tremendous effort to unveil the role that these interactions play in psychiatric disorders. This thesis addresses these questions from novel perspectives. The first pillar of this thesis is the time-varying na-ture of the dynamic interactions between brain regions. The second pillar is the role that FC dynamics play in clinical populations. The third pillar uncovers the connectivity structure that links the observed anatomical and functional connectivity patterns through computational modeling. The final pillar of the thesis proposes a mechanistic explana-tion for brain disorders.<br>L'activitat del cervell fluctua espontàniament a diferents escales i per tant exhibeix in-teraccions dinàmiques i complexes que manifesten patrons de sincronització rics. Du-rant els darrers deu anys han abundat els estudis orientats a comprendre els mecanismes que hi ha darrere les interaccions cerebrals basant-se en les seves estructures funcionals i estructurals. A més, existeix un esforç ingent per desvetllar el paper que aquestes in-teraccions juguen en els trastorns psiquiàtrics. Aquesta tesi aborda les qüestions esmen-tades des de noves perspectives. El primer pilar d'aquesta tesi és la naturalesa variable en el temps de la interacció dinàmica entre diferents regions del cervell. El segon pilar és el paper que aquesta dinàmica de connectivitat funcional juga en diferents poblacions clíniques. El tercer pilar es centra en l'ús de models computacionals per determinar l'es-tructura de connectivitat que relaciona els patrons de connectivitat funcional i anatòmics observats. El quart pilar de la tesi proposa una explicació del mecanisme dels trastorns cerebrals.
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Black, Chelsea Lynn. "Resting-State Functional Brain Networks in Bipolar Spectrum Disorder: A Graph Theoretical Investigation." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/393135.
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Psychology<br>Ph.D.<br>Neurobiological theories of bipolar spectrum disorder (BSD) propose that the emotional dysregulation characteristic of BSD stems from disrupted prefrontal control over subcortical limbic structures (Strakowski et al., 2012; Depue & Iacono, 1989). However, existing neuroimaging research on functional connectivity between frontal and limbic brain regions remains inconclusive, and is unable to adequately characterize global functional network dynamics. Graph theoretical analysis provides a framework for understanding the local and global connections of the brain and comparing these connections between groups (Sporns et al., 2004). The purpose of this study was to investigate resting state functional connectivity in individuals at low and high risk for BSD based on moderate versus high reward sensitivity, both with and without a BSD diagnosis, using graph theoretical network analysis. Results demonstrated decreased connectivity in a cognitive control region (dorsolateral prefrontal cortex), but increased connectivity of a brain region involved in the detection and processing of reward (bilateral orbitofrontal cortex), among participants at high risk for BSD. Participants with BSD showed increased inter-module connectivity of the dorsal anterior cingulate cortex (ACC). Reward sensitivity was associated with decreased global and local efficiency, and interacted with BSD risk group status to predict inter-module connectivity. Findings are discussed in relation to neurobiological theories of BSD.<br>Temple University--Theses
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Abou, Elseoud A. (Ahmed). "Exploring functional brain networks using independent component analysis:functional brain networks connectivity." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526201597.
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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
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Xiao, Yaqiong. "Resting-state functional connectivity in the brain and its relation to language development in preschool children." Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-217874.
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Human infants have been shown to have an innate capacity to acquire their mother tongue. In recent decades, the advent of the functional magnetic resonance imaging (fMRI) technique has made it feasible to explore the neural basis underlying language acquisition and processing in children, even in newborn infants (for reviews, see Kuhl & Rivera-Gaxiola, 2008; Kuhl, 2010) .
Spontaneous low-frequency (< 0.1 Hz) fluctuations (LFFs) in the resting brain have been shown to be physiologically meaningful in the seminal study (Biswal et al., 1995) . Compared to task-based fMRI, resting-state fMRI (rs-fMRI) has some unique advantages in neuroimaging research, especially in obtaining data from pediatric and clinical populations. Moreover, it enables us to characterize the functional organization of the brain in a systematic manner in the absence of explicit tasks. Among brain systems, the language network has been well investigated by analyzing LFFs in the resting brain.
This thesis attempts to investigate the functional connectivity within the language network in typically developing preschool children and the covariation of this connectivity with children’s language development by using the rs-fMRI technique. The first study (see Chapter 2.1; Xiao et al., 2016a) revealed connectivity differences in language-related regions between 5-year-olds and adults, and demonstrated distinct correlation patterns between functional connections within the language network and sentence comprehension performance in children. The results showed a left fronto-temporal connection for processing syntactically more complex sentences, suggesting that this connection is already in place at age 5 when it is needed for complex sentence comprehension, even though the whole functional network is still immature. In the second study (see Chapter 2.2; Xiao et al., 2016b), sentence comprehension performance and rs-fMRI data were obtained from a cohort of children at age 5 and a one-year follow-up. This study examined the changes in functional connectivity in the developing brain and their relation to the development of language abilities. The findings showed that the development of intrinsic functional connectivity in preschool children over the course of one year is clearly observable and individual differences in this development are related to the advancement in sentence comprehension ability with age.
In summary, the present thesis provides new insights into the relationship between intrinsic functional connectivity in the brain and language processing, as well as between the changes in intrinsic functional connectivity and concurrent language development in preschool children. Moreover, it allows for a better understanding of the neural mechanisms underlying language processing and the advancement of language abilities in the developing brain.
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Rickels, Audreyana Cleo Jagger. "THE ORGANIZATION OF FUNCTIONAL AND EFFECTIVE CONNECTIVITY OF RESTING-STATE BRAIN NETWORKS IN ADOLESCENTS WITH AND WITHOUT NEURODEVELOPMENTAL AND/OR INTERNALIZING DISORDERS." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1687.
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The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).
Books on the topic "Graph fMRI brain resting state functional connectivity"
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Soriano-Mas, Carles, and Ben J. Harrison. Brain Functional Connectivity in OCD. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0024.
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This chapter provides an overview of studies assessing alterations in brain functional connectivity in obsessive-compulsive disorder (OCD) as assessed by functional magnetic resonance imaging (fMRI). Although most of the reviewed studies relate to the analysis of resting-state fMRI data, the chapter also reviews studies that have combined resting-state with structural or task-based approaches, as well as task-based studies in which the analysis of functional connectivity was reported. The main conclusions to be drawn from this review are that patients with OCD consistently demonstrate altered patterns of brain functional connectivity in large-scale “frontostriatal” and “default mode” networks, and that the heterogeneity of OCD symptoms is likely to partly arise via distinct modulatory influences on these networks by broader disturbances of affective, motivational, and regulatory systems. The variable nature of some findings across studies as well as the influence of medications on functional connectivity measures is also discussed.
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Konrad, Kerstin, Adriana Di Martino, and Yuta Aoki. Brain volumes and intrinsic brain connectivity in ADHD. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0006.
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Neuroimaging studies have increased our understanding of the neurobiological underpinnings of ADHD. Structural brain imaging studies demonstrate widespread changes in brain volumes, in particular in frontal-striatal-cerebellar networks. Based on the widespread nature of structural and functional brain abnormalities, approaches able to capture the organizing principles of large-scale neural systems have been used in ADHD. These include diffusion magnetic resonance imaging (MRI) and resting state functional MRI (R-fMRI). Complementary to findings of volumetric studies, diffusion investigations have reported structural connectivity abnormalities in frontal-striatal-cerebellar networks. In parallel, R-fMRI studies point towards abnormalities in the interaction of multiple networks, extending the functional territory of explorations beyond cognitive and motor control. In the future, a deep phenotypic characterization beyond diagnostic categories combined with longitudinal study designs and novel analytical approaches will accelerate the pace towards clinical translations of neuroimaging to improve the detection and prediction of neural trajectories and treatment response in ADHD.
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Ramani, Ramachandran, ed. Functional MRI. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.001.0001.
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Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.
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Brennan, Brian P., and Scott L. Rauch. Functional Neuroimaging Studies in Obsessive-Compulsive Disorder: Overview and Synthesis. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0021.
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Studies using functional neuroimaging have played a critical role in the current understanding of the neurobiology of obsessive-compulsive disorder (OCD). Early studies using positron emission tomography (PET) identified a core cortico-striatal-thalamo-cortical circuit that is dysfunctional in OCD. Subsequent studies using behavioral paradigms in conjunction with functional magnetic resonance imaging (fMRI) have provided additional information about the neural substrates underlying specific psychological processes relevant to OCD. More recently, studies utilizing resting state fMRI have identified abnormal functional connectivity within intrinsic brain networks including the default mode and frontoparietal networks in OCD patients. Although these studies, as a whole, clearly substantiate the model of cortico-striatal-thalamo-cortical circuit dysfunction in OCD and support the continued investigation of neuromodulatory treatments targeting these brain regions, there is also growing evidence that brain regions outside this core circuit, particularly frontoparietal regions involved in cognitive control processes, may also play a significant role in the pathophysiology of OCD.
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Bandettini, Peter A., and Hanzhang Lu. Magnetic Resonance Methodologies. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0008.
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Magnetic resonance imaging is a noninvasive tool for assessing brain anatomy, perfusion, metabolism, and function with precision. In this chapter, the basics and the most cutting edge examples of MRI-based measures are described. The first is measurement of cerebral perfusion, including the latest techniques involving spin-labelling as well as the tracking of exogenous contrast agents. Functional MRI is then discussed, along with some of the cutting edge methodology that has yet to make it into routine clinical practice. Next, resting state fMRI is described, a powerful technique whereby the entire brain connectivity can be established. Diffusion-based MRI techniques are useful for diagnosing brain trauma as well as understanding the structural connections in healthy and pathological brains. Spectroscopy is able to make spatially specific and metabolite-specific assessment of brain metabolism. The chapter ends with an overview of structural imaging with MRI, highlighting the developing field of morphometry and its potential for differentially assessing individual brains.
Book chapters on the topic "Graph fMRI brain resting state functional connectivity"
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Feng, Chunxiang, Biao Jie, Xintao Ding, Daoqiang Zhang, and Mingxia Liu. "Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification." In Machine Learning in Medical Imaging. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_31.
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Nandakumar, Naresh, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, and Archana Venkataraman. "A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity." In Connectomics in NeuroImaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_2.
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Bhaskaran, Bhuvaneshwari, and Kavitha Anandan. "Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI." In Research Anthology on Diagnosing and Treating Neurocognitive Disorders. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3441-0.ch030.
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Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.
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la Iglesia-Vaya, Maria de, Jose Molina-Mateo, Ma Jose, Ahmad S., and Luis Marti-Bonmati. "Brain Connections – Resting State fMRI Functional Connectivity." In Novel Frontiers of Advanced Neuroimaging. InTech, 2013. http://dx.doi.org/10.5772/52273.
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Harris, Neil G., and Jessica G. Ashley. "Potential utility of resting state fMRI–determined functional connectivity to guide neurorehabilitation." In Traumatic Brain Injury. CRC Press, 2017. http://dx.doi.org/10.1201/9781315371351-10.
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Tang, Cheuk Ying. "Basic Principles of Functional MRI." In Functional MRI, edited by Ramachandran Ramani. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.003.0002.
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Blood oxygen level dependent (BOLD) MRI, also called functional MRI (fMRI), is one of the most widely used modalities for studying brain function. The underlying source of the fMRI signal is blood flow and the oxygenation state of hemoglobin. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI has been developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform a goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. These signals come from spatially distinct brain regions but exhibit coherent, time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. The networks represent the strength of the functional connectivity between distinct brain regions and have been used as imaging biomarkers for various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. In this chapter, we provide an introductory review of the basic principles of fMRI: signal sources, acquisition methods, and data analysis.
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"Future Trends in Functional MRI." In Functional MRI, edited by S. Kathleen Bandt, Dennis D. Spencer, and Ramachandran Ramani. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.003.0012.
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While MRI became a standard workhorse in neurology/neurosurgery within a few years of installation of the first MRI unit, fMRI, in spite of being a powerful imaging tool, remains primarily a research tool, even though the first fMRI study was published 25 years ago. Scientifically, fMRI has made a major impact, judging by the number of PubMed citations and publications in high-impact journals. In cognitive neuroscience, fMRI is the most commonly used imaging technique in published peer-reviewed articles. fMRI is used clinically for preoperative brain mapping in neurosurgery to delineate the proximity of the lesion (tumor) to eloquent areas of the brain, with the aim of achieving adequate tumor resection with minimal functional damage to the brain. fMRI connectivity and activation maps have identified altered activation patterns and resting-state networks in psychiatric disorders like schizophrenia, bipolar disorder, autism, and Alzheimer’s disease, but fMRI is still not a standard diagnostic procedure in psychiatry. Diffusion imaging technique is being used for triaging stroke patients who are likely to respond to stroke therapy (embolectomy and/or clot lysis). Meanwhile, major collaborative fMRI studies are in progress in many institutions to collect normative data on connectivity, activation response, and behavioral response as well as correlation among them. Studies focused on specific neuropsychiatric disorders also have been initiated by the National Institutes of Health. All this is a reflection of the huge potential application of fMRI in clinical practice envisioned by the scientific community.
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Vidhusha, S., and A. Kavitha. "Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI." In Advances in Computational Intelligence and Robotics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3038-2.ch009.
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Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.
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"Functional MRI Studies of Cognitive Evaluation in the Elderly." In Functional MRI, edited by S. Kathleen Bandt, Dennis D. Spencer, and Ramachandran Ramani. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.003.0007.
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Over the last century, life expectancy has improved significantly in the United States—from 47 years for men and 49 years for women in 1900 to 76 years and 81 years, respectively, in 2017. Older people have altered mental function that can vary from subtle cognitive changes to dementia. Additionally in elderly patients cognitive function seems to worsen after a medical illness, hospital admission or major surgery. The cognitive neuroscience of aging is an emerging field of research. Clinically, older patients can show alteration in working memory, executive function, multitasking, speed of response, etc. Anatomically, age-related changes in the brain are primarily in the frontal lobe. However, in neuropathological diseases affecting cognition in elderly (Alzheimer’s disease, Huntington’s disease, etc.), the changes are primarily in the temporal lobe. fMRI activation studies have revealed consistent changes in activation pattern with age. In younger persons, many activation-induced responses are lateralized—verbal activation is lateralized to the left and spatial memory activation is right lateralized. In the elderly, these activations induce a bilateral response. This is an age-related compensatory response. fMRI connectivity studies give a global perspective on mental function. The default mode network (DMN) is active in the resting “no task” state of the brain; with a task, activity decreases in the DMN. The elderly have less resting DMN activity than younger people, and their ability to decrease DMN activity during a task (which is essential for shifting attention and focusing on a task) is also less.
Conference papers on the topic "Graph fMRI brain resting state functional connectivity"
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Zhen, Zonglei, Jie Tian, Wei Qin, and Hui Zhang. "Partial correlation mapping of brain functional connectivity with resting state fMRI." In Medical Imaging, edited by Armando Manduca and Xiaoping P. Hu. SPIE, 2007. http://dx.doi.org/10.1117/12.709012.
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Marui, Wataru, Shigenobu Kan, Manabu Nii, Masahiko Shibata, and Syoji Kobashi. "MD-LMS Algorithm Based Brain Functional Connectivity Analysis in Resting State fMRI." In 2018 World Automation Congress (WAC). IEEE, 2018. http://dx.doi.org/10.23919/wac.2018.8430471.
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Wang, Junqi, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu-Ping Wang. "Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2020. http://dx.doi.org/10.1117/12.2549378.
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Ahmad, M. Faizan, James Murphy, Deniz Vatansever, Emmanuel A. Stamatakis, and Simon Godsill. "Tracking changes in functional connectivity of brain networks from resting-state fMRI using particle filters." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178079.
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Fardafshari, Parisa, Farzaneh Keyvanfard, and Abbas Nasiraei Moghaddam. "Assessing the influence of functional connectivity estimation methods on graph metrics in resting-state fMRI." In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE, 2017. http://dx.doi.org/10.1109/iraniancee.2017.7985264.
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Ai, Jing, Tiantian Liu, Kexin Wang, Tianyi Yan, Jian Zhang, and Tianlin Huang. "Alterations of Brain Functional Networks in Older Adults: A Resting-state fMRI Study Using Graph Theory." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263643.
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Gan, Jiangzhang, Xiaofeng Zhu, Rongyao Hu, et al. "Multi-graph Fusion for Functional Neuroimaging Biomarker Detection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/81.
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Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance. In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.
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Amiri, S., M. Arbabi, K. Kazemi, M. Parvaresh-Rizi, and M. M. Mirbagheri. "Resting-State Functional Connectivity in Popular Targets for Deep Brain Stimulation in the Treatment of Major Depression: An Application of a Graph Theory." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856413.
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Yang, Xin, Ning Zhang, and Donglin Wang. "Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110714.
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The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically developing (TD) participants using the functional connectivity calculated from the derived functional networks. In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. We downloaded a total of 120 training samples, including 58 ASD and 62 TD participants, which were obtained from the public repository: Autism Brain Imaging Data Exchange I (ABIDE I). Our methodology and results have five main parts. First, we utilize a group ICA model to extract functional networks from the ASD group and rank the top 20 regions of interest (ROIs). Second, we utilize a dictionary learning model to extract functional networks from the ASD group and rank the top 20 ROIs. Third, we merged the 40 selected ROIs from the two models together as the ASD functional networks. Fourth, we generate three corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs selected from dictionary learning, and the 40 combined ROIs selected from both. Finally, we extract ROIs for all training samples using the above three masks, and the calculated functional connectivity was used as features for ASD and TD classification. The classification results showed that the functional networks derived from ICA and dictionary learning together outperform those derived from a single ICA model or a single dictionary learning model.