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Journal articles on the topic 'Functional connectomes'

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

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1

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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2

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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5

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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7

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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8

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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9

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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10

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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Schmitt, Oliver, Christian Nitzsche, Peter Eipert, Vishnu Prathapan, Marc-Thorsten Hütt, and Claus Hilgetag. "Reaction-diffusion models in weighted and directed connectomes." PLOS Computational Biology 18, no. 10 (October 28, 2022): e1010507. http://dx.doi.org/10.1371/journal.pcbi.1010507.

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Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances. Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway. In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.
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12

Byrge, Lisa, and Daniel P. Kennedy. "High-accuracy individual identification using a “thin slice” of the functional connectome." Network Neuroscience 3, no. 2 (January 2019): 363–83. http://dx.doi.org/10.1162/netn_a_00068.

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Connectome fingerprinting—a method that uses many thousands of functional connections in aggregate to identify individuals—holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance—how many and which functional connections are necessary and/or sufficient for high accuracy—will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small “thin slice” of the connectome—as few as 40 out of 64,620 functional connections—was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.
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Amico, Enrico, and Joaquín Goñi. "Mapping hybrid functional-structural connectivity traits in the human connectome." Network Neuroscience 2, no. 3 (September 2018): 306–22. http://dx.doi.org/10.1162/netn_a_00049.

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One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common “hybrid” connectivity patterns that represent the connectivity fingerprint of a subject. We tested this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracts two main task-sensitive hybrid traits. The first trait encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second trait mainly encompasses the connectivity between visual, attentional, default mode network (DMN), and subcortical network. Overall, these findings confirm the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.
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Melozzi, Francesca, Eyal Bergmann, Julie A. Harris, Itamar Kahn, Viktor Jirsa, and Christophe Bernard. "Individual structural features constrain the mouse functional connectome." Proceedings of the National Academy of Sciences 116, no. 52 (December 11, 2019): 26961–69. http://dx.doi.org/10.1073/pnas.1906694116.

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Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure–function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.
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Di, Xin, and Bharat B. Biswal. "Toward Task Connectomics: Examining Whole-Brain Task Modulated Connectivity in Different Task Domains." Cerebral Cortex 29, no. 4 (June 21, 2018): 1572–83. http://dx.doi.org/10.1093/cercor/bhy055.

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Abstract Human brain anatomical and resting-state functional connectivity have been comprehensively portrayed using MRI, which are termed anatomical and functional connectomes. A systematic examination of tasks modulated whole brain functional connectivity, which we term as task connectome, is still lacking. We analyzed 6 block-designed and 1 event-related designed functional MRI data, and examined whole-brain task modulated connectivity in various task domains, including emotion, reward, language, relation, social cognition, working memory, and inhibition. By using psychophysiological interaction between pairs of regions from the whole brain, we identified statistically significant task modulated connectivity in 4 tasks between their experimental and respective control conditions. Task modulated connectivity was found not only between regions that were activated during the task but also regions that were not activated or deactivated, suggesting a broader involvement of brain regions in a task than indicated by simple regional activations. Decreased functional connectivity was observed in all the 4 tasks and sometimes reduced connectivity was even between regions that were both activated during the task. This suggests that brain regions that are activated together do not necessarily work together. The current study demonstrates the comprehensive task connectomes of 4 tasks, and suggested complex relationships between regional activations and connectivity changes.
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Conti, Allegra, Andrea Duggento, Maria Guerrisi, Luca Passamonti, Iole Indovina, and Nicola Toschi. "Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain." Entropy 21, no. 7 (July 6, 2019): 661. http://dx.doi.org/10.3390/e21070661.

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A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.
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Varoquaux, Gaël, and R. Cameron Craddock. "Learning and comparing functional connectomes across subjects." NeuroImage 80 (October 2013): 405–15. http://dx.doi.org/10.1016/j.neuroimage.2013.04.007.

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Seguin, Caio, Martijn P. van den Heuvel, and Andrew Zalesky. "Navigation of brain networks." Proceedings of the National Academy of Sciences 115, no. 24 (May 30, 2018): 6297–302. http://dx.doi.org/10.1073/pnas.1801351115.

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Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse, and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45–60% reductions in navigation performance. We found that the human connectome cannot be progressively randomized or clusterized to result in topologies with substantially improved navigation performance (>5%), suggesting a topological balance between regularity and randomness that is conducive to efficient navigation. Navigation was also found to (i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks, and (ii) explain significant variation in functional connectivity. Unlike commonly studied communication strategies in connectomics, navigation does not mandate assumptions about global knowledge of network topology. We conclude that the topology and geometry of brain networks are conducive to efficient decentralized communication.
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Rilling, James K., and Martijn P. van den Heuvel. "Comparative Primate Connectomics." Brain, Behavior and Evolution 91, no. 3 (2018): 170–79. http://dx.doi.org/10.1159/000488886.

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A connectome is a comprehensive map of neural connections of a species nervous system. While recent work has begun comparing connectomes across a wide breadth of species, we present here a more detailed and specific comparison of connectomes across the primate order. Long-range connections are thought to improve communication efficiency and thus brain function but are costly in terms of energy and space utilization. Methods for measuring connectivity in the brain include measuring white matter volume, histological cell counting, anatomical tract tracing, diffusion-weighted imaging and tractography, and functional connectivity in MRI. Comparisons of global white matter connectivity suggest that larger primate brains are less well connected than smaller primate brains, but that humans have more connections than expected for our cortical neuron number, which may be concentrated in the prefrontal cortex. Although there is significant overlap in structural connectivity between humans and nonhuman primates, human-specific connections are found in cortical areas involved with language, imitation, and tool use. Similar to structural connectivity, there is also widespread overlap between humans and macaques in resting state functional connectivity. However, there are again a number of human-specific connections in cortical regions involved in language, tool use, and empathy. Comparative connectomics also offers the opportunity to detect specializations of connectivity in other primate species besides humans. Future research should capitalize on the ability of diffusion tractography to measure connectivity in postmortem brains that could expand the representation of species beyond humans, chimpanzees, and rhesus macaques, and facilitate identification of connectivity-based adaptations to different social and ecological niches. This work will require careful attention to establishing cortical homologies across species and to improving tractography methods to limit detection of false-positive and false-negative connections. Finally, it will be important to attempt to establish the functional significance of variation in connectivity profiles by examining how these covary with behavior and cognition both across and within species.
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Binding, Lawrence, Peter Taylor, Sallie Baxendale, Andrew McEvoy, Anna Miserocchi, John Duncan, and Sjoerd Vos. "Network changes predicting language decline following anterior temporal lobe resection." Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (August 12, 2022): e2.178. http://dx.doi.org/10.1136/jnnp-2022-abn2.32.

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Anterior temporal lobe resection (ATLR), while successful can result in lasting impairment of language function. White matter bundles have been shown to explain some of the variance seen in language decline after ATLR. Network analysis of the structural connectome has been shown superior in predicting preoperative language ability but remains unexplored in predicting postoperative ability.Diffusion MRI-based tractography was used to generate the preoperative connectome on 54 left-lat- eralised (as determined by functional MRI), left-hemisphere ATLR. Postoperative connectomes were estimated via manually drawn resection masks. Graded naming test (GNT), semantic, and letter fluency were binarised into significant decline or not (via their reliable change indices). Strength (sum of connec- tions) and betweenness centrality (interconnectivity) network changes were generated using pre- and postoperative connectomes as predictor variables. Each model was entered into a linear support vector machine incorporating synthetic minority over-sampling technique for class imbalances.Strength changes alone accurately predicted 81.6% of patients who had GNT decline. Betweenness centrality changes accurately predicted 73.3% of patients who had letter fluency decline. Patients with semantic decline were predicted equally as well by strength and betweenness centrality changes (accuracy=71.1%).These findings demonstrate the usefulness of the structural network in predicting and potentially prevent- ing postoperative language decline.
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Domhof, Justin W. M., Kyesam Jung, Simon B. Eickhoff, and Oleksandr V. Popovych. "Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels." Network Neuroscience 5, no. 3 (2021): 798–830. http://dx.doi.org/10.1162/netn_a_00202.

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Abstract Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.
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He, Li, Wei Liu, Kaixiang Zhuang, Jie Meng, and Jiang Qiu. "Executive function-related functional connectomes predict intellectual abilities." Intelligence 85 (March 2021): 101527. http://dx.doi.org/10.1016/j.intell.2021.101527.

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Smith, Adam S. "Decoding Population Variance in Resting-State Functional Connectomes." Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 7, no. 6 (June 2022): 534–35. http://dx.doi.org/10.1016/j.bpsc.2022.03.005.

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Váša, František, Edward T. Bullmore, and Ameera X. Patel. "Probabilistic thresholding of functional connectomes: Application to schizophrenia." NeuroImage 172 (May 2018): 326–40. http://dx.doi.org/10.1016/j.neuroimage.2017.12.043.

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Bielczyk, Natalia Z., Fabian Walocha, Patrick W. Ebel, Koen V. Haak, Alberto Llera, Jan K. Buitelaar, Jeffrey C. Glennon, and Christian F. Beckmann. "Thresholding functional connectomes by means of mixture modeling." NeuroImage 171 (May 2018): 402–14. http://dx.doi.org/10.1016/j.neuroimage.2018.01.003.

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Korgaonkar, Mayuresh, Cassandra Chakouch, May Erlinger, Isabella Breukelaar, Philip Boyce, Philip Hazell, Leanne Williams, Gin S. Malhi, and Anthony Harris. "F117. Intrinsic Brain Functional Connectomes in Bipolar Disorder." Biological Psychiatry 85, no. 10 (May 2019): S258—S259. http://dx.doi.org/10.1016/j.biopsych.2019.03.654.

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Bernhardt, Boris C., Fatemeh Fadaie, Min Liu, Benoit Caldairou, Shi Gu, Elizabeth Jefferies, Jonathan Smallwood, Danielle S. Bassett, Andrea Bernasconi, and Neda Bernasconi. "Temporal lobe epilepsy." Neurology 92, no. 19 (April 19, 2019): e2209-e2220. http://dx.doi.org/10.1212/wnl.0000000000007447.

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ObjectiveTo assess whether hippocampal sclerosis (HS) severity is mirrored at the level of large-scale networks.MethodsWe studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 temporal lobe epilepsy (TLE) patients with histopathologic diagnosis of HS (n = 25; TLE-HS) and isolated gliosis (n = 19; TLE-G) and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm that simulates functional dynamics from structural network data.ResultsCompared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from gray matter atrophy.ConclusionsSevere HS is associated with marked remodeling of connectome topology and structurally governed functional dynamics in TLE, as opposed to isolated gliosis, which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales.
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Maher, Christina, Arkiev D’Souza, Michael Barnett, Omid Kavehei, Chenyu Wang, and Armin Nikpour. "Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns." Applied Sciences 12, no. 20 (October 18, 2022): 10487. http://dx.doi.org/10.3390/app122010487.

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The implications of combining structural and functional connectivity to quantify the most active brain regions in seizure onset remain unclear. This study tested a new model that may facilitate the incorporation of diffusion MRI (dMRI) in clinical practice. We obtained structural connectomes from dMRI and functional connectomes from electroencephalography (EEG) to assess whether high structure-function coupling corresponded with the seizure onset region. We mapped individual electrodes to their nearest cortical region to allow for a one-to-one comparison between the structural and functional connectomes. A seizure laterality score and expected onset zone were defined. The patients with well-lateralised seizures revealed high structure-function coupling consistent with the seizure onset zone. However, a lower seizure lateralisation score translated to reduced alignment between the high structure-function coupling regions and the seizure onset zone. We illustrate that dMRI, in combination with EEG, can improve the identification of the seizure onset zone. Our model may be valuable in enhancing ultra-long-term monitoring by indicating optimal, individualised electrode placement.
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Tozzi, Leonardo, Scott L. Fleming, Zachary D. Taylor, Cooper D. Raterink, and Leanne M. Williams. "Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices." Network Neuroscience 4, no. 3 (January 2020): 925–45. http://dx.doi.org/10.1162/netn_a_00148.

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Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these “functional connectomes” are reliable over time. In a large public sample of healthy participants ( N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6–8%) to excellent (0.08–0.14%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.
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Dolgin, Elie. "This is your brain online: the Functional Connectomes Project." Nature Medicine 16, no. 4 (April 2010): 351. http://dx.doi.org/10.1038/nm0410-351b.

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Joseph, Jane E., Davy Vanderweyen, Joshua Swearingen, Brandon K. Vaughan, Derek Novo, Xun Zhu, Mulugeta Gebregziabher, et al. "Tracking the Development of Functional Connectomes for Face Processing." Brain Connectivity 9, no. 2 (March 2019): 231–39. http://dx.doi.org/10.1089/brain.2018.0607.

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Malagurski, Brigitta, Patrice Péran, Benjamine Sarton, Hélène Vinour, Edouard Naboulsi, Béatrice Riu, Fanny Bounes, et al. "Topological disintegration of resting state functional connectomes in coma." NeuroImage 195 (July 2019): 354–61. http://dx.doi.org/10.1016/j.neuroimage.2019.03.012.

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Abbas, Kausar, Mintao Liu, Manasij Venkatesh, Enrico Amico, Alan David Kaplan, Mario Ventresca, Luiz Pessoa, Jaroslaw Harezlak, and Joaquín Goñi. "Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints." Brain Connectivity 11, no. 5 (June 1, 2021): 333–48. http://dx.doi.org/10.1089/brain.2020.0881.

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Sato, João Ricardo, Cristiane Maria Sato, Marcel K. de Carli Silva, and Claudinei Eduardo Biazoli. "Commute Time as a Method to Explore Brain Functional Connectomes." Brain Connectivity 9, no. 2 (March 2019): 155–61. http://dx.doi.org/10.1089/brain.2018.0598.

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Bari, Sumra, Enrico Amico, Nicole Vike, Thomas M. Talavage, and Joaquín Goñi. "Uncovering multi-site identifiability based on resting-state functional connectomes." NeuroImage 202 (November 2019): 115967. http://dx.doi.org/10.1016/j.neuroimage.2019.06.045.

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Wirsich, Jonathan, Anne-Lise Giraud, and Sepideh Sadaghiani. "Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics." NeuroImage 219 (October 2020): 116998. http://dx.doi.org/10.1016/j.neuroimage.2020.116998.

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Cai, Biao, Gemeng Zhang, Wenxing Hu, Aiying Zhang, Pascal Zille, Yipu Zhang, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu‐Ping Wang. "Refined measure of functional connectomes for improved identifiability and prediction." Human Brain Mapping 40, no. 16 (July 29, 2019): 4843–58. http://dx.doi.org/10.1002/hbm.24741.

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Messaritaki, Eirini, Sonya Foley, Simona Schiavi, Lorenzo Magazzini, Bethany Routley, Derek K. Jones, and Krish D. Singh. "Predicting MEG resting-state functional connectivity from microstructural information." Network Neuroscience 5, no. 2 (2021): 477–504. http://dx.doi.org/10.1162/netn_a_00187.

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Abstract Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
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Morgan, Victoria L., Graham W. Johnson, Leon Y. Cai, Bennett A. Landman, Kurt G. Schilling, Dario J. Englot, Baxter P. Rogers, and Catie Chang. "MRI network progression in mesial temporal lobe epilepsy related to healthy brain architecture." Network Neuroscience 5, no. 2 (2021): 434–50. http://dx.doi.org/10.1162/netn_a_00184.

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Abstract We measured MRI network progression in mesial temporal lobe epilepsy (mTLE) patients as a function of healthy brain architecture. Resting-state functional MRI and diffusion-weighted MRI were acquired in 40 unilateral mTLE patients and 70 healthy controls. Data were used to construct region-to-region functional connectivity, structural connectivity, and streamline length connectomes per subject. Three models of distance from the presumed seizure focus in the anterior hippocampus in the healthy brain were computed using the average connectome across controls. A fourth model was defined using regions of transmodal (higher cognitive function) to unimodal (perceptual) networks across a published functional gradient in the healthy brain. These models were used to test whether network progression in patients increased when distance from the anterior hippocampus or along a functional gradient in the healthy brain decreases. Results showed that alterations of structural and functional networks in mTLE occur in greater magnitude in regions of the brain closer to the seizure focus based on healthy brain topology, and decrease as distance from the focus increases over duration of disease. Overall, this work provides evidence that changes across the brain in focal epilepsy occur along healthy brain architecture.
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Spronk, Marjolein, Brian P. Keane, Takuya Ito, Kaustubh Kulkarni, Jie Lisa Ji, Alan Anticevic, and Michael W. Cole. "A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction." Cerebral Cortex 31, no. 1 (September 10, 2020): 547–61. http://dx.doi.org/10.1093/cercor/bhaa242.

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Abstract A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach—connectome-wide similarity—we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.
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Vatansever, Deniz, Theodoros Karapanagiotidis, Daniel S. Margulies, Elizabeth Jefferies, and Jonathan Smallwood. "Distinct patterns of thought mediate the link between brain functional connectomes and well-being." Network Neuroscience 4, no. 3 (January 2020): 637–57. http://dx.doi.org/10.1162/netn_a_00137.

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Ongoing thought patterns constitute important aspects of both healthy and abnormal human cognition. However, the neural mechanisms behind these daily experiences and their contribution to well-being remain a matter of debate. Here, using resting-state fMRI and retrospective thought sampling in a large neurotypical cohort ( n = 211), we identified two distinct patterns of thought, broadly describing the participants’ current concerns and future plans, that significantly explained variability in the individual functional connectomes. Consistent with the view that ongoing thoughts are an emergent property of multiple neural systems, network-based analysis highlighted the central importance of both unimodal and transmodal cortices in the generation of these experiences. Importantly, while state-dependent current concerns predicted better psychological health, mediating the effect of functional connectomes, trait-level future plans were related to better social health, yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the link between brain physiology and well-being.
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42

Dufford, Alexander J., Stephanie Noble, Siyuan Gao, and Dustin Scheinost. "The instability of functional connectomes across the first year of life." Developmental Cognitive Neuroscience 51 (October 2021): 101007. http://dx.doi.org/10.1016/j.dcn.2021.101007.

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Preziosa, P., M. A. Rocca, G. A. Ramirez, E. P. Bozzolo, V. Canti, E. Pagani, P. Valsasina, et al. "Structural and functional brain connectomes in patients with systemic lupus erythematosus." European Journal of Neurology 27, no. 1 (August 8, 2019): 113. http://dx.doi.org/10.1111/ene.14041.

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Friedman, Eric J., Adam S. Landsberg, Julia P. Owen, Yi-Ou Li, and Pratik Mukherjee. "Stochastic geometric network models for groups of functional and structural connectomes." NeuroImage 101 (November 2014): 473–84. http://dx.doi.org/10.1016/j.neuroimage.2014.07.039.

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Itahashi, Takashi, Naohiro Okada, Shuntaro Ando, Syudo Yamasaki, Daisuke Koshiyama, Kentaro Morita, Noriaki Yahata, et al. "Functional connectomes linking child-parent relationships with psychological problems in adolescence." NeuroImage 219 (October 2020): 117013. http://dx.doi.org/10.1016/j.neuroimage.2020.117013.

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Zhang, Xin, Xiang Li, Changfeng Jin, Hanbo Chen, Kaiming Li, Dajiang Zhu, Xi Jiang, et al. "Identifying and Characterizing Resting State Networks in Temporally Dynamic Functional Connectomes." Brain Topography 27, no. 6 (June 6, 2014): 747–65. http://dx.doi.org/10.1007/s10548-014-0357-7.

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Korgaonkar, Mayuresh S., Andrea N. Goldstein-Piekarski, Alexander Fornito, and Leanne M. Williams. "Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder." Molecular Psychiatry 25, no. 7 (November 6, 2019): 1537–49. http://dx.doi.org/10.1038/s41380-019-0574-2.

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Abstract Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.
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Kim, Jinseop S., and Marcus Kaiser. "From Caenorhabditis elegans to the human connectome: a specific modular organization increases metabolic, functional and developmental efficiency." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1653 (October 5, 2014): 20130529. http://dx.doi.org/10.1098/rstb.2013.0529.

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The connectome, or the entire connectivity of a neural system represented by a network, ranges across various scales from synaptic connections between individual neurons to fibre tract connections between brain regions. Although the modularity they commonly show has been extensively studied, it is unclear whether the connection specificity of such networks can already be fully explained by the modularity alone. To answer this question, we study two networks, the neuronal network of Caenorhabditis elegans and the fibre tract network of human brains obtained through diffusion spectrum imaging. We compare them to their respective benchmark networks with varying modularities, which are generated by link swapping to have desired modularity values. We find several network properties that are specific to the neural networks and cannot be fully explained by the modularity alone. First, the clustering coefficient and the characteristic path length of both C. elegans and human connectomes are higher than those of the benchmark networks with similar modularity. High clustering coefficient indicates efficient local information distribution, and high characteristic path length suggests reduced global integration. Second, the total wiring length is smaller than for the alternative configurations with similar modularity. This is due to lower dispersion of connections, which means each neuron in the C. elegans connectome or each region of interest in the human connectome reaches fewer ganglia or cortical areas, respectively. Third, both neural networks show lower algorithmic entropy compared with the alternative arrangements. This implies that fewer genes are needed to encode for the organization of neural systems. While the first two findings show that the neural topologies are efficient in information processing, this suggests that they are also efficient from a developmental point of view. Together, these results show that neural systems are organized in such a way as to yield efficient features beyond those given by their modularity alone.
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Osmanlıoğlu, Yusuf, Birkan Tunç, Drew Parker, Mark A. Elliott, Graham L. Baum, Rastko Ciric, Theodore D. Satterthwaite, Raquel E. Gur, Ruben C. Gur, and Ragini Verma. "System-level matching of structural and functional connectomes in the human brain." NeuroImage 199 (October 2019): 93–104. http://dx.doi.org/10.1016/j.neuroimage.2019.05.064.

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Cai, Biao, Zhongxing Zhou, Aiying Zhang, Gemeng Zhang, Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu-Ping Wang. "Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences." Journal of Neuroscience Methods 372 (April 2022): 109539. http://dx.doi.org/10.1016/j.jneumeth.2022.109539.

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