Relevant bibliographies by topics / Functional connectomes

Academic literature on the topic 'Functional connectomes'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Functional connectomes"

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Seguin, Caio, Ye Tian, and Andrew Zalesky. "Network communication models improve the behavioral and functional predictive utility of the human structural connectome." Network Neuroscience 4, no. 4 (January 2020): 980–1006. http://dx.doi.org/10.1162/netn_a_00161.

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The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.
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Kesler, Shelli R., Paul Acton, Vikram Rao, and William J. Ray. "Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease." Network Neuroscience 2, no. 2 (June 2018): 241–58. http://dx.doi.org/10.1162/netn_a_00048.

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Neurodegeneration in Alzheimer’s disease (AD) is associated with amyloid-beta peptide accumulation into insoluble amyloid plaques. The five-familial AD (5XFAD) transgenic mouse model exhibits accelerated amyloid-beta deposition, neuronal dysfunction, and cognitive impairment. We aimed to determine whether connectome properties of these mice parallel those observed in patients with AD. We obtained diffusion tensor imaging and resting-state functional magnetic resonance imaging data for four transgenic and four nontransgenic male mice. We constructed both structural and functional connectomes and measured their topological properties by applying graph theoretical analysis. We compared connectome properties between groups using both binarized and weighted networks. Transgenic mice showed higher characteristic path length in weighted structural connectomes and functional connectomes at minimum density. Normalized clustering and modularity were lower in transgenic mice across the upper densities of the structural connectome. Transgenic mice also showed lower small-worldness index in higher structural connectome densities and in weighted structural networks. Hyper-correlation of structural and functional connectivity was observed in transgenic mice compared with nontransgenic controls. These preliminary findings suggest that 5XFAD mouse connectomes may provide useful models for investigating the molecular mechanisms of AD pathogenesis and testing the effectiveness of potential treatments.
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Chen, Vincent Chin-Hung, Tung-Yeh Lin, Dah-Cherng Yeh, Jyh-Wen Chai, and Jun-Cheng Weng. "Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy." Brain Sciences 10, no. 11 (November 12, 2020): 851. http://dx.doi.org/10.3390/brainsci10110851.

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Breast cancer is the leading cancer among women worldwide, and a high number of breast cancer patients are struggling with psychological and cognitive disorders. In this study, we aim to use machine learning models to discriminate between chemo-brain participants and healthy controls (HCs) using connectomes (connectivity matrices) and topological coefficients. Nineteen female post-chemotherapy breast cancer (BC) survivors and 20 female HCs were recruited for this study. Participants in both groups received resting-state functional magnetic resonance imaging (rs-fMRI) and generalized q-sampling imaging (GQI). Logistic regression (LR), decision tree classifier (CART), and xgboost (XGB) were the models we adopted for classification. In connectome analysis, LR achieved an accuracy of 79.49% with the functional connectomes and an accuracy of 71.05% with the structural connectomes. In the topological coefficient analysis, accuracies of 87.18%, 82.05%, and 83.78% were obtained by the functional global efficiency with CART, the functional global efficiency with XGB, and the structural transitivity with CART, respectively. The areas under the curves (AUCs) were 0.93, 0.94, 0.87, 0.88, and 0.84, respectively. Our study showed the discriminating ability of functional connectomes, structural connectomes, and global efficiency. We hope our findings can contribute to an understanding of the chemo brain and the establishment of a clinical system for tracking chemo brain.
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Rajapandian, Meenusree, Enrico Amico, Kausar Abbas, Mario Ventresca, and Joaquín Goñi. "Uncovering differential identifiability in network properties of human brain functional connectomes." Network Neuroscience 4, no. 3 (January 2020): 698–713. http://dx.doi.org/10.1162/netn_a_00140.

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The identifiability framework (𝕀 f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀 f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀 f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀 f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.
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Baker, Justin T., Daniel G. Dillon, Lauren M. Patrick, Joshua L. Roffman, Roscoe O. Brady, Diego A. Pizzagalli, Dost Öngür, and Avram J. Holmes. "Functional connectomics of affective and psychotic pathology." Proceedings of the National Academy of Sciences 116, no. 18 (April 15, 2019): 9050–59. http://dx.doi.org/10.1073/pnas.1820780116.

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Converging evidence indicates that groups of patients with nominally distinct psychiatric diagnoses are not separated by sharp or discontinuous neurobiological boundaries. In healthy populations, individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of transdiagnostic symptom profiles observed in psychiatric patients may map onto detectable patterns of network function. To examine the manner through which neurobiological variation might underlie clinical presentation, we obtained fMRI data from over 1,000 individuals, including 210 diagnosed with a primary psychotic disorder or affective psychosis (bipolar disorder with psychosis and schizophrenia or schizoaffective disorder), 192 presenting with a primary affective disorder without psychosis (unipolar depression, bipolar disorder without psychosis), and 608 demographically matched healthy comparison participants recruited through a large-scale study of brain imaging and genetics. Here, we examine variation in functional connectomes across psychiatric diagnoses, finding striking evidence for disease connectomic “fingerprints” that are commonly disrupted across distinct forms of pathology and appear to scale as a function of illness severity. The presence of affective and psychotic illnesses was associated with graded disruptions in frontoparietal network connectivity (encompassing aspects of dorsolateral prefrontal, dorsomedial prefrontal, lateral parietal, and posterior temporal cortices). Conversely, other properties of network connectivity, including default network integrity, were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. This work allows us to establish key biological and clinical features of the functional connectomes of severe mental disease.
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Garzón, Benjamín, Martin Lövdén, Lieke de Boer, Jan Axelsson, Katrine Riklund, Lars Bäckman, Lars Nyberg, and Marc Guitart-Masip. "Role of dopamine and gray matter density in aging effects and individual differences of functional connectomes." Brain Structure and Function 226, no. 3 (January 9, 2021): 743–58. http://dx.doi.org/10.1007/s00429-020-02205-4.

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AbstractWith increasing age, functional connectomes become dissimilar across normal individuals, reflecting heterogenous aging effects on functional connectivity (FC). We investigated the distribution of these effects across the connectome and their relationship with age-related differences in dopamine (DA) D1 receptor availability and gray matter density (GMD). With this aim, we determined aging effects on mean and interindividual variance of FC using fMRI in 30 younger and 30 older healthy subjects and mapped the contribution of each connection to the patterns of age-related similarity loss. Aging effects on mean FC accounted mainly for the dissimilarity between connectomes of younger and older adults, and were related, across brain regions, to aging effects on DA D1 receptor availability. Aging effects on the variance of FC indicated a global increase in variance with advancing age, explained connectome dissimilarity among older subjects and were related to aging effects on variance of GMD. The relationship between aging and the similarity of connectomes can thus be partly explained by age differences in DA modulation and gray matter structure.
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Osmanlioglu, Yusuf, Drew Parker, Steven Brem, Ali Shokoufandeh, and Ragini Verma. "NIMG-69. PERSONALIZED CONNECTOMIC SIGNATURES: BRIDGING THE GAP BETWEEN NEURO-ONCOLOGY AND CONNECTOMICS." Neuro-Oncology 22, Supplement_2 (November 2020): ii163. http://dx.doi.org/10.1093/neuonc/noaa215.682.

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Abstract PURPOSE Connectomics has led to significant neuroscientific findings within the last two decades, eventually making impact in the clinics. Neuro-oncology can benefit immensely from connectomics in evaluating structural connectivity of brains with tumor for pre- and post-treatment planning, as a tumor connectome along with derived network measures will make it possible to determine the cognitive effects of treatment and quantify the effect of surgery on quality of life. However, generating connectomes in the presence of tumor is a challenging task. Specifically, registration of an atlas to the brain, which is essential in parcellating the brain into regions of interest, fails around the tumor due to mass effect and infiltration related distortions which are not present in the atlas that comes from a healthy brain. We aim to tackle this problem by introducing a novel atlas registration method. METHOD Although tumor deforms the geometrical shape of its surrounding regions, it does not violate the connectivity of displaced cortical voxels to the rest of the brain. Leveraging this fact, we represent the brain as an annotated graph with nodes representing ROIs encoding geometric features of regions and weighted edges representing the connectivity between regions. In encoding the surroundings of the tumor into the graph, we subsample the region into smaller patches to represent the area with multiple nodes. We then calculate many-to-one graph matching between the graphs of a tumor patient and a healthy control to associate surroundings of tumor with healthy ROIs. OUTCOME A tumor connectome showing how the connectivity is morphed around the tumor, which can further be extended to creating connectomes of recurrence. CLINICAL IMPLICATIONS Use of connectomes can revolutionize neuro-oncology by helping surgeons in estimating structural, functional, and behavioral outcomes of resection prior to surgery and in predicting recovery after the surgery, potentially suggesting subject specific treatment plans.
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Ma, Qing, Yanqing Tang, Fei Wang, Xuhong Liao, Xiaowei Jiang, Shengnan Wei, Andrea Mechelli, Yong He, and Mingrui Xia. "Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study." Schizophrenia Bulletin 46, no. 3 (November 22, 2019): 699–712. http://dx.doi.org/10.1093/schbul/sbz111.

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Abstract Psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), share clinical and neurobiological features. Because previous investigations of functional dysconnectivity have mainly focused on single disorders, the transdiagnostic alterations in the functional connectome architecture of the brain remain poorly understood. We collected resting-state functional magnetic resonance imaging data from 512 participants, including 121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls. Individual functional brain connectomes were constructed in a voxelwise manner, and the modular architectures were examined at different scales, including (1) global modularity, (2) module-specific segregation and intra- and intermodular connections, and (3) nodal participation coefficients. The correlation of these modular measures with clinical scores was also examined. We reliably identify common alterations in modular organization in patients compared to controls, including (1) lower global modularity; (2) lower modular segregation in the frontoparietal, subcortical, visual, and sensorimotor modules driven by more intermodular connections; and (3) higher participation coefficients in several network connectors (the dorsolateral prefrontal cortex and angular gyrus) and the thalamus. Furthermore, the alterations in the SCZ group are more widespread than those of the BD and MDD groups and involve more intermodular connections, lower modular segregation and higher connector integrity. These alterations in modular organization significantly correlate with clinical scores in patients. This study demonstrates common hyper-integrated modular architectures of functional brain networks among patients with SCZ, BD, and MDD. These findings reveal a transdiagnostic mechanism of network dysfunction across psychiatric disorders from a connectomic perspective.
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Hyon, Ryan, Yoosik Youm, Junsol Kim, Jeanyung Chey, Seyul Kwak, and Carolyn Parkinson. "Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village." Proceedings of the National Academy of Sciences 117, no. 52 (December 14, 2020): 33149–60. http://dx.doi.org/10.1073/pnas.2013606117.

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People often have the intuition that they are similar to their friends, yet evidence for homophily (being friends with similar others) based on self-reported personality is inconsistent. Functional connectomes—patterns of spontaneous synchronization across the brain—are stable within individuals and predict how people tend to think and behave. Thus, they may capture interindividual variability in latent traits that are particularly similar among friends but that might elude self-report. Here, we examined interpersonal similarity in functional connectivity at rest—that is, in the absence of external stimuli—and tested if functional connectome similarity is associated with proximity in a real-world social network. The social network of a remote village was reconstructed; a subset of residents underwent functional magnetic resonance imaging. Similarity in functional connectomes was positively related to social network proximity, particularly in the default mode network. Controlling for similarities in demographic and personality data (the Big Five personality traits) yielded similar results. Thus, functional connectomes may capture latent interpersonal similarities between friends that are not fully captured by commonly used demographic or personality measures. The localization of these results suggests how friends may be particularly similar to one another. Additionally, geographic proximity moderated the relationship between neural similarity and social network proximity, suggesting that such associations are particularly strong among people who live particularly close to one another. These findings suggest that social connectivity is reflected in signatures of brain functional connectivity, consistent with the common intuition that friends share similarities that go beyond, for example, demographic similarities.
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Gatica, Marilyn, Fernando E. Rosas, Pedro A. M. Mediano, Ibai Diez, Stephan P. Swinnen, Patricio Orio, Rodrigo Cofré, and Jesus M. Cortes. "High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model." PLOS Computational Biology 18, no. 9 (September 2, 2022): e1010431. http://dx.doi.org/10.1371/journal.pcbi.1010431.

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The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain’s connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.
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Dissertations / Theses on the topic "Functional connectomes"

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Bollmann, Yannick. "Emergence of functional and structural cortical connectomes through the developmental prism." Thesis, Aix-Marseille, 2019. http://theses.univ-amu.fr.lama.univ-amu.fr/191113_BOLLMANN_844bezee521trbla166eo565zm_TH.pdf.

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Les neurones corticaux sont générés sur de longues périodes embryonnaires et post-natales. Des travaux précédents ont montré que les neurones générés à des stades embryonnaires précoces jouent un rôle essentiel dans la coordination de l'activité neuronale nécessaire à la maturation des réseaux de neurones corticaux. La première partie de mon travail a consisté à caractériser le connectome structural des neurones glutamatergiques et GABAergiques en utilisant la méthode du « fate mapping » permettant l’expression de protéines fluorescentes en fonction de la date de genèse des neurones. En utilisant la microscopie à feuillet de lumière sur des cerveaux transparisés, j’ai pu quantifier la distribution de différentes populations neuronales dans le cerveau entier.La deuxième partie de mon travail a été consacrée à caractériser le connectome fonctionnel des neurones GABAergiques et à démontrer la présence de neurones « hubs » dans le cortex en baril en développement. En utilisant des lignées de souris transgéniques exprimant l’indicateur de calcium GCaMP6s, nous avons suivi la maturation et la dynamique fonctionnelle du réseau neuronal au cours des deux premières semaines postnatales en utilisant l’imagerie à deux photons in vivo. La distribution des liens fonctionnels entre neurones suit une loi de probabilité à queue lourde suggérant la présence de neurones « hubs ». En utilisant l’imagerie calcique à deux photons et une stimulation « optogénétique-holographique », nous avons démontré le rôle « hub » d’une sous-population de neurones GABAergiques dans la synchronisation de l’activité du réseau dans le cortex en baril au cours du développement
Cortical neurons are generated throughout an extended embryonic period. Recent studies indicate that the cells originating from the earliest stages of neurogenesis are critically involved in coordinating neuronal activity, instructing network maturation throughout large cortical areas. The first part of my work was building and mining brain cell atlases and connectomes. I first characterized the brain-wide structural connectome of early-born glutamatergic and GABAergic neurons, fluorescently labeled according to their date of birth (genetic fate-mapping approach). Using light-sheet microscopy on cleared brains, I quantify the distribution of both populations in the whole brain to create an Atlas.The second part of my work was the characterization of GABAergic neurons functional connectome and the characterization of hub cells in the developing barrel cortex in vivo. By using transgenic mice lines expressing the calcium indicator GCaMP6s, we follow the maturation and the functional dynamics of the network during the two first postnatal weeks using two-photon imaging. The characteristically heavy-tailed distribution of functional connections between neurons that we observed, strongly suggest the presence of hub neurons. Using two-photon calcium imaging and holographic-optogenetic stimulation we entangle the necessary and sufficient conditions of how GABAergic neurons contribute to and synchronize network activity as acting as hub neuron in the barrel cortex
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Afyouni, Soroosh. "Application of graph theoretical models to the functional connectome of human brain." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/88528/.

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During the past decade, there has been a great interest in creating mathematical models to describe the properties of connectivity in the human brain. One of the established tools to describe these interactions among regions of the brain is graph theory. However, graph theoretical methods were mainly designed for the analysis of single network which is problematic for neuroscientists wishing to study groups of subjects. Specifically, studies using the Rich Club (RC) graph measure require cumbersome methods to make statistical inferences. In the first part of this work, we propose a framework to analyse the inter-subject variability in Rich Club organisation. The proposed framework is used to identify the changes in RC coefficient and RC organisation in patients with schizophrenia relative to healthy control. We follow this work by proposing a novel method, named Rich Block (RB), which is a combination of the tradition Rich Club and Stochastic Block Models (SBM). We show that using RBs can not only facilitate an inter-subject statistical inference, it can also account for differences in profile of connectivity, and control for subject-level covariates. We validate the Rich Block approach by simulating networks of different size and structure. We find that RB accurately estimates RC coefficients and RC organisations, specifically, in network with large number of nodes and blocks. With real data we use RB to identify changes in coefficient and organisation of highly connected sub-graphs of hub blocks in schizophrenia. In the final portion of this work, we examine the methods used to define each edge in networks formed from resting-state functional magnetic resonance imaging (rs-fMRI). The standard approach in rs-fMRI is to divide the brain into regions, extract time series, and compute the temporal correlation between each region. These correlations are assumed to follow standard results, when in fact serial autocorrelation in the time series can corrupt these results. While some authors have proposed corrections to account for autocorrelation, they are poorly documented and always assume homogeneity of autocorrelation over brain regions. Thus we propose a method to account for bias in interregion correlation estimates due to autocorrelation. We develop an exact method and an approximate, more computationally efficient method that adjusts for the sampling variability in the correlation coefficient. We use inter-subject scrambled real-data to validate the proposed methods under a null setting, and intact real-data to examine the impact of our method on graph theoretical measures. We find that the standard methods fail to practically correct the sensitivity and specificity level due to over-simplifying the temporal structure of BOLD time series, while even our approximate method is substantially more accurate.
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Mahama, Edward Kofi. "Connectome eigenmodes underlies functional connectivity patterns in conscious awake and anesthetic mice." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/880.

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Consciousness and loss of consciousness is something we encounter in our everyday lives. Despite its commonplace in everyday life, scientists are still trying to understand and find reliable markers for it. In this work we use a data-driven K-means clustering approach to uncover the different functional patterns associated with different consciousness levels. We pursue this study using a high resolution optogenetic voltage image of the mouse brain waking up from anesthesia. The main questions we addressed in this study are: Can we identify signatures of conscious and unconsciousness from functional connectivity patterns? What is the nature of the different patterns that correspond to wakefulness and anesthesia? What is the nature of dynamics between these functional patterns in wakefulness and anesthesia? How does the anatomical connectivity support the observed functional patterns in wakefulness and anesthesia? Our results show that during anesthesia, the brain is characterized by a single dominant brain pattern with short range connections. Furthermore, we observed from our results that during anaesthesia the brain is characterized by minimal temporal exploration of the different brain configurations. Conversely, in awake state we observed the opposite. The brain pattern with long range connections are frequent in wakefulness. In addition, wakefulness is characterized by somewhat frequent temporal exploration of brain states. Our results show that analysis of functional connectivity patterns can be a useful tool for identifying specific and generalizable fingerprints of wakefulness and anaesthesia
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Kundu, Prantik. "Physical analysis of BOLD fMRI signals for functional brain mapping and connectomics." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648842.

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Melozzi, Francesca. "Simulated switching of the resting state functional connectivity in mouse brain using a real mesoscale connectome." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8319/.

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Capire come modellare l'attività del cervello a riposo, resting state, è il primo passo necessario per avvicinarsi a una reale comprensione della dinamica cerebrale. Sperimentalmente si osserva che, quando il cervello non è soggetto a stimoli esterni, particolari reti di regioni cerebrali presentano un'attività neuronale superiore alla media. Nonostante gli sforzi dei ricercatori, non è ancora chiara la relazione che sussiste tra le connessioni strutturali e le connessioni funzionali del sistema cerebrale a riposo, organizzate nella matrice di connettività funzionale. Recenti studi sperimentali mostrano la natura non stazionaria della connettività funzionale in disaccordo con i modelli in letteratura. Il modello implementato nella presente tesi per simulare l'evoluzione temporale del network permette di riprodurre il comportamento dinamico della connettività funzionale. Per la prima volta in questa tesi, secondo i lavori a noi noti, un modello di resting state è implementato nel cervello di un topo. Poco è noto, infatti, riguardo all'architettura funzionale su larga scala del cervello dei topi, nonostante il largo utilizzo di tale sistema nella modellizzazione dei disturbi neurologici. Le connessioni strutturali utilizzate per definire la topologia della rete neurale sono quelle ottenute dall'Allen Institute for Brain Science. Tale strumento fornisce una straordinaria opportunità per riprodurre simulazioni realistiche, poiché, come affermato nell'articolo che presenta tale lavoro, questo connettoma è il più esauriente disponibile, ad oggi, in ogni specie vertebrata. I parametri liberi del modello sono stati scelti in modo da inizializzare il sistema nel range dinamico ottimale per riprodurre il comportamento dinamico della connettività funzionale. Diverse considerazioni e misure sono state effettuate sul segnale BOLD simulato per meglio comprenderne la natura. L'accordo soddisfacente fra i centri funzionali calcolati nel network cerebrale simulato e quelli ottenuti tramite l'indagine sperimentale di Mechling et al., 2014 comprovano la bontà del modello e dei metodi utilizzati per analizzare il segnale simulato.
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Melozzi, Francesca. "The role of structural brain features on resting-state functional organization : a large-scale computational study in mice." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0771.

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Il est possible d’aborder l'organisation fonctionnelle du cerveau en modélisant le cerveau comme un système dynamique, ce qui permet d'étudier comment l'architecture fonctionnelle dépend du squelette structurel sous-jacent. En combinant approches expérimentales et théoriques chez la souris, nous avons étudié de façon systématique comment le connectome structurel contraint le connectome fonctionnel.Dans une première partie nous avons généralisé à la souris le logiciel open source The Virtual Brain (Sanz-Leon et al., 2013, Melozzi et al., 2017).En utilisant les données d'IRM de diffusion (IRMd) de 19 souris, nous avons virtualisé leur cerveau pour générer un signal BOLD in silico que nous avons comparé aux données d'IRM fonctionnelle enregistrées chez les mêmes souris pendant la veille passive. Nous montrons que les prédictions du modèle basé sur le connectome dépendent strictement de la structure du réseau (Melozzi et al., en révision). Nous démontrons que les variations individuelles définissent une empreinte structurelle spécifique ayant un impact direct sur l'organisation fonctionnelle des cerveaux individuels. Ces résultats démontrent l’existence d’un lien causal entre le connectome structurel et le connectome fonctionnel.Finalement, nous confirmons certaines de nos conclusions en utilisant l’approche inverse: nous avons étudié s’il était possible de déduire le connectome structurel à partir du connectome fonctionnel en utilisant la méthode d'inférence Bayésienne (Melozzi et al., en préparation).Nos résultats aux futures études testant la causalité entre structure et fonction, au niveau du cerveau entier individuel, en conditions physiologique et pathologique
The connectome-based model approach aims to understand the functional organization of the brain by modeling the brain as a dynamical system and then studying how the functional architecture rises from the underlying structural skeleton. In this thesis, taking advantage of mice studies, we investigated the informative content of different structural features in explaining the functional ones.First, we extended the open-source software TVB (Leon et al., 2013), originally designed for humans, to accommodate the connectome-based model approach in mice (Melozzi et al., 2017).Using diffusionMRI (dMRI) data from 19 mice, we virtualised their brains to generate in silico fMRI that we compared to functional MRI data recorded in the same mice during passive wakefulness. We show that the predictions of the connectome-based model strictly depend on the structure of the underlying network (Melozzi et al., under review). We demonstrate that individual variations define a specific structural fingerprint with a direct impact upon the functional organization of individual brains. Comparing the predictive power of the tracer-based and the dMRI-based connectome we identify how the limitations of the dMRI method restrict our comprehension of the structural-functional relation. Together, these results strongly support the existence of a causal link between the structural and the functional connectomes.Finally, we infer the connectome form resting state dynamics by inferring the structural connectome using the Bayesian inference (Melozzi et al., in prep).Our results pave the way to future studies focusing on the causal link between structure and function at the individual brain level
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Hart, Michael Gavin. "Network approaches to understanding the functional effects of focal brain lesions." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274018.

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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.
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Váša, František. "Characterising disease-related and developmental changes in correlation-derived structural and functional brain networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277816.

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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.
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Colclough, Giles. "Methods for modelling human functional brain networks with MEG and fMRI." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:ef1dc66e-f142-4cdc-8177-5d040c94b964.

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MEG and fMRI offer complementary insights into connected human brain function. Evidence from the use of both techniques in the study of networked activity indicates that functional connectivity reflects almost every measurable aspect of human reality, being indicative of ability and deteriorating with disease. Functional network analyses may offer improved prediction of dysfunction and characterisation of cognition. Three factors holding back progress are the difficulty in synthesising information from multiple imaging modalities; a need for accurate modelling of connectivity in individual subjects, not just average effects; and a lack of scalable solutions to these problems that are applicable in a big-data setting. I propose two methodological advances that tackle these issues. A confound to network analysis in MEG, the artificial correlations induced across the brain by the process of source reconstruction, prevents the transfer of connectivity models from fMRI to MEG. The first advance is a fast correction for this confound, allowing comparable analyses to be performed in both modalities. A comparative study demonstrates that this new approach for MEG shows better repeatability for connectivity estimation, both within and between subjects, than a wide range of alternative models in popular use. A case-study analysis uses both fMRI and MEG recordings from a large dataset to determine the genetic basis for functional connectivity in the human brain. Genes account for 20% - 65% of the variation in connectivity, and outweigh the influence of the developmental environment. The second advance is a Bayesian hierarchical model for sparse functional networks that is applicable to both modalities. By sharing information over a group of subjects, more accurate estimates can be constructed for individuals' connectivity patterns. The approach scales to large datasets, outperforms state-of-the-art methods, and can provide a 50% noise reduction in MEG resting-state networks.
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Suprano, Ilaria. "Étude de la connectivité cérébrale par IRM fonctionnelle et de diffusion dans l’intelligence." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1282.

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L'idée que l'intelligence s’appuie non seulement sur des régions spécifiques du cerveau, mais également sur des réseaux cérébraux efficaces s’est récemment affirmée. En effet, on pense que l'organisation du cerveau humain repose sur des réseaux complexes et dynamiques dans lesquels la communication entre les régions cérébrales garantit un transfert efficace d'informations. Ces concepts nous ont amené à explorer les bases neurales de l'intelligence en combinant des techniques avancées d'IRM et la théorie des graphes. D'un côté, les techniques avancées d'IRM, telles que l'IRM fonctionnelle au repos (IRMf-rs) et l'IRM par diffusion (IRMd), permettent d'explorer respectivement la connectivité cérébrale fonctionnelle et structurale, tandis que la théorie des graphes permettent la caractérisation des propriétés des réseaux à différentes échelles, grâce à des métriques globales et locales. L'objectif de cette thèse est de caractériser la topologie des réseaux cérébraux fonctionnels et structurels chez les enfants et les adultes avec un quotient intellectuel supérieur (HIQ) par rapport aux sujets de niveau standard (SIQ). Premièrement, nous avons concentré notre attention sur une population d’enfants présentant différentes caractéristiques cognitives. Deux profils HIQ, à savoir homogène (Hom-HIQ) et hétérogène HIQ (Het-HIQ), ont été définis sur la base d'observations cliniques et de sous-tests du quotient intellectuel (QI). En utilisant des techniques d’IRMf-rs, nous avons examiné la topologie du réseau fonctionnel par « l’indice de rupture de nœud ». Nous avons trouvé des différences topologiques significatives dans les propriétés d'intégration et de ségrégation des réseaux chez les enfants HIQ par rapport aux enfants SIQ, pour le graphe cérébral entier, pour chaque graphe hémisphérique et pour la connectivité homotopique. De plus, ces changements de topologie étaient plus prononcés dans le sous-groupe Het-HIQ. Enfin, nous avons trouvé des corrélations significatives entre les changements des métriques de graphes et le QI total et d’autres indices du QI. Ces résultats ont démontré pour la première fois que les deux profils HIQ sont liés à une organisation différente du substrat neuronal. Ensuite, la connectivité structurale du réseau cérébral, mesurée par IRMd chez l’ensemble des enfants HIQ, est significativement différente de celle des enfants SIQ. Nous avons également aussi de fortes corrélations entre la densité des réseaux cérébraux des enfants et leurs scores d'intelligence. De plus, plusieurs corrélations ont été trouvées entre les métriques de graphe d'intégration suggérant que les performances de l'intelligence peuvent être liées à une organisation homogène des réseaux. Ces résultats ont démontré que le substrat neuronal de l'intelligence repose sur une microarchitecture de la substance blanche de forte densité et sur une organisation homogène des réseaux. Cette population a finalement été étudiée par IRMf avec une tâche de mémorisation de mots. Des changements significatifs ont été observés entre les groupes HIQ et SIQ. Cette étude confirme notre hypothèse selon laquelle les deux profils HIQ sont caractérisés par une activité cérébrale différente, avec un effet plus prononcé chez les enfants Het-HIQ. Enfin, nous avons étudié la connectivité fonctionnelle et structurale dans une population d’adultes HIQ. Nous avons trouvé plusieurs corrélations entre les métriques de graphe et les autres indices du QI. De même que pour la population d’enfants, les capacités cognitives élevées des adultes sont corrélées à une organisation homogène des réseaux structurels et fonctionnels et une modularité réduite. En conclusion, on a démontré que la sensibilité des métriques de graphes basées sur des techniques 'IRM avancées et de connectivité, telles que l’IRMf-rs et l'IRMd, était très utile pour mieux caractériser les réseaux cérébraux des enfants et des adultes, ainsi que pour distinguer différents profils d'intelligence chez les enfants
The idea that intelligence is embedded not only in specific brain regions, but also in efficient brain networks has grown up. Indeed, human brain organization is believed to rely on complex and dynamic networks in which the communication between cerebral regions guarantees an efficient transfer of information. These recent concepts have led us to explore the neural bases of intelligence using both advanced MRI techniques in combination with graph analysis. On one hand, advanced MRI techniques, such as resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) allow the exploration of respectively the functional and the structural brain connectivity while on the other hand, graph theory models allow the characterization of brain networks properties at different scales, thanks to global and local metrics. The aim of this thesis is to characterize the topology of functional and structural brain networks in children and in adults with an intelligence quotient higher (HIQ) than standard levels (SIQ). First, we focused our attention on a children population with different cognitive characteristics. Two HIQ profiles, namely homogeneous (Hom-HIQ) and heterogeneous HIQ (Het-HIQ), have been defined based on clinical observations and Intelligence Quotient (IQ) sub-tests. Using resting-state fMRI techniques, we examined the functional network topology changes, estimating the "hub disruption index", in these two HIQ profiles. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to SIQ children, for the whole brain graph, for each hemispheric graph, and for the homotopic connectivity. These brain networks changes resulted to be more pronounced in Het-HIQ subgroup. Finally, we found significant correlations between the graph networks’ changes and the full-scale IQ, as well as some intelligence subscales. These results demonstrated for the first time, that different HIQ profiles are related to a different neural substrate organization. Then, the structural brain network connectivity, measured by dMRI in all HIQ children, were significantly different than in SIQ children. Also, we found strong correlations between the children brain networks density and their intelligence scores. Furthermore, several correlations were found between integration graph metrics suggesting that intelligence performances are probably related to a homogeneous network organization. These findings demonstrated that intelligence neural substrate is based on a strong white matter microarchitecture of the major fiber-bundles and a well-balanced network organization between local and global scales. This children population was finally studied using a memory-word task of fMRI. Significant changes were observed between both HIQ and SIQ groups. This study confirms our hypothesis that both HIQ profiles are characterized by a different brain activity, with stronger evidences in Het-HIQ children. Finally, we investigated both functional and structural connectivity in a population of adults HIQ. We found several correlations between graph metrics and intelligence sub-scores. As well as for the children population, high cognitive abilities of adults seem to be related brain structural and functional networks organization with a decreased modularity. In conclusion, the sensitivity of graph metrics based on advanced MRI techniques, such as rs-fMRI and dMRI, was demonstrated to be very helpful to provide a better characterization of children and adult HIQ, and further, to distinguish different intelligence profiles in children
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Books on the topic "Functional connectomes"

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Shamlan, Muhsin Ahmad Bin. The function of Arabic connectors in clause relations. Salford: University of Salford, 1987.

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Zuo, Xi-Nian, Bharat B. Biswal, and Russell A. Poldrack, eds. Reliability and Reproducibility in Functional Connectomics. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-821-9.

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von Philipsborn, Anne C. Neurobiology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0003.

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Behavioral neurobiology aims at explaining behavior at the level of neurons and neuronal circuits, based on linking comparative anatomy, and the observation and manipulation of nervous system activity with animal behavior. The numerical simplicity and the presence of identified neurons in insect nervous systems make them outstanding model systems for neurobiology. The insect nervous system has a common ground plan with functionally specialized regions for sensory processing, integration, and motor control. In holometabolous species, the nervous system is restructured during metamorphosis to support new behavior. Different forms of plasticity allow for behavioral adaptations in the adult stage. Neuronal circuits for behavior in Drosophila melanogaster can be effectively analysed with genetic tools, as exemplified for courtship and mating behavior. Recent developments in connectomics and genome editing are expected to further behavioral neurobiology in a broad range of species and permit a comprehensive comparative approach to the neurobiology of behavioral ecology.
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Book chapters on the topic "Functional connectomes"

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Carlson, Kristen W., Jay L. Shils, Longzhi Mei, and Jeffrey E. Arle. "Functional Requirements of Small- and Large-Scale Neural Circuitry Connectome Models." In Brain and Human Body Modeling 2020, 249–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_14.

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AbstractWe have truly entered the Age of the Connectome due to a confluence of advanced imaging tools, methods such as the flavors of functional connectivity analysis and inter-species connectivity comparisons, and computational power to simulate neural circuitry. The interest in connectomes is reflected in the exponentially rising number of articles on the subject. What are our goals? What are the “functional requirements” of connectome modelers? We give a perspective on these questions from our group whose focus is modeling neurological disorders, such as neuropathic back pain, epilepsy, Parkinson’s disease, and age-related cognitive decline, and treating them with neuromodulation.
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Chuhma, Nao. "Optogenetic Analysis of Striatal Connections to Determine Functional Connectomes." In Optogenetics, 265–77. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55516-2_18.

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Dadashkarimi, Javid, Amin Karbasi, and Dustin Scheinost. "Data-Driven Mapping Between Functional Connectomes Using Optimal Transport." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 293–302. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87199-4_28.

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Khosla, Meenakshi, Keith Jamison, Amy Kuceyeski, and Mert R. Sabuncu. "3D Convolutional Neural Networks for Classification of Functional Connectomes." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 137–45. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00889-5_16.

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Chung, Ai Wern, and Markus D. Schirmer. "Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism." In Connectomics in NeuroImaging, 126–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_13.

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Schirmer, Markus D., and Ai Wern Chung. "Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism." In Connectomics in NeuroImaging, 54–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32391-2_6.

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Lisowska, Anna, and Islem Rekik. "Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes." In PRedictive Intelligence in MEdicine, 103–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00320-3_13.

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Kozlov, Stanislav, Alexey Poyda, Vyacheslav Orlov, Maksim Sharaev, and Vadim Ushakov. "Selection of Functionally Homogeneous Human Brain Regions for Functional Connectomes Building Based on fMRI Data." In Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics, 709–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71637-0_82.

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Dadashkarimi, Javid, Amin Karbasi, and Dustin Scheinost. "Combining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport." In Lecture Notes in Computer Science, 386–95. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16431-6_37.

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Svaldi, Diana O., Joaquín Goñi, Apoorva Bharthur Sanjay, Enrico Amico, Shannon L. Risacher, John D. West, Mario Dzemidzic, Andrew Saykin, and Liana Apostolova. "Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes." In Lecture Notes in Computer Science, 74–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00689-1_8.

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Conference papers on the topic "Functional connectomes"

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Li, Xiang, Dajiang Zhu, Xi Jiang, Changfeng Jin, Lei Guo, Lingjiang Li, and Tianming Liu. "Discovering common functional connectomics signatures." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556551.

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Chen, Hanbo, Kaiming Li, Dajiang Zhu, and Tianming Liu. "Identifying consistent brain networks via maximizing predictability of functional connectome from structural connectome." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556640.

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Sachdeva, Pratik S., Sharmodeep Bhattacharyya, and Kristofer E. Bouchard. "Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856316.

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Chen, Bo, and Xiang Li. "Temporal functional connectomics in schizophrenia and healthy controls." In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8123054.

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Wang, Dong, Zhenyue Shi, Xu Zhang, DongYa Jing, Zhongjing Hu, and Hongxu Su. "Research on shear performance of new prefabricated sandwich thermal insulation exterior wall connectors." In 2022 International Conference on Optoelectronic Information and Functional Materials (OIFM 2022), edited by Chao Zuo. SPIE, 2022. http://dx.doi.org/10.1117/12.2638716.

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Xing, Xiaodan, Lili Jin, Feng Shi, and Ziwen Peng. "Diagnosis of OCD using functional connectome and Riemann kernel PCA." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512316.

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Iakovidou, Nantia D., Stavros I. Dimitriadis, Nikos A. Laskaris, and Kostas Tsichlas. "Querying functional brain connectomics to discover consistent subgraph patterns." In 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2013. http://dx.doi.org/10.1109/bibe.2013.6701655.

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Wang, Peng, Dajiang Zhu, Xiang Li, Hanbo Chen, Xi Jiang, Li Sun, Qingjiu Cao, Li An, Tianming Liu, and Yufeng Wang. "Identifying functional connectomics abnormality in attention deficit hyperactivity disorder." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556532.

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Di Marco, Antinisca, Paola Inverardi, and Romina Spalazzese. "Synthesizing self-adaptive connectors meeting functional and performance concerns." In 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 2013. http://dx.doi.org/10.1109/seams.2013.6595500.

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Taya, Fumihiko, Yu Sun, Nitish Thakor, and Anastasios Bezerianos. "Information transfer efficiency during rest and task a functional connectome approach." In 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2014. http://dx.doi.org/10.1109/biocas.2014.6981655.

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