Relevant bibliographies by topics / Connectome

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Connectome'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 April 2025

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Journal articles on the topic "Connectome"

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Catani, Marco, Michel Thiebaut de Schotten, David Slater, and Flavio Dell'Acqua. "Connectomic approaches before the connectome." NeuroImage 80 (October 2013): 2–13. http://dx.doi.org/10.1016/j.neuroimage.2013.05.109.

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Kumar, Sawan, Varsha Sreenivasan, Partha Talukdar, Franco Pestilli, and Devarajan Sridharan. "ReAl-LiFE: Accelerating the Discovery of Individualized Brain Connectomes on GPUs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 630–38. http://dx.doi.org/10.1609/aaai.v33i01.3301630.

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Diffusion imaging and tractography enable mapping structural connections in the human brain, in-vivo. Linear Fascicle Evaluation (LiFE) is a state-of-the-art approach for pruning spurious connections in the estimated structural connectome, by optimizing its fit to the measured diffusion data. Yet, LiFE imposes heavy demands on computing time, precluding its use in analyses of large connectome databases. Here, we introduce a GPU-based implementation of LiFE that achieves 50-100x speedups over conventional CPU-based implementations for connectome sizes of up to several million fibers. Briefly, the algorithm accelerates generalized matrix multiplications on a compressed tensor through efficient GPU kernels, while ensuring favorable memory access patterns. Leveraging these speedups, we advance LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights during connectome pruning. Our regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal. We demonstrate the utility of our approach by classifying pathological signatures of structural connectivity in patients with Alzheimer’s Disease (AD). We estimated million fiber whole-brain connectomes, followed by pruning with ReAl-LiFE, for 90 individuals (45 AD patients and 45 healthy controls). Linear classifiers, based on support vector machines, achieved over 80% accuracy in classifying AD patients from healthy controls based on their ReAl-LiFE pruned structural connectomes alone. Moreover, classification based on the ReAl-LiFE pruned connectome outperformed both the unpruned connectome, as well as the LiFE pruned connectome, in terms of accuracy. We propose our GPU-accelerated approach as a widely relevant tool for non-negative least squares optimization, across many domains.
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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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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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Szalkai, Balázs, Csaba Kerepesi, Bálint Varga, and Vince Grolmusz. "Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0." Cognitive Neurodynamics 11, no. 1 (September 15, 2016): 113–16. http://dx.doi.org/10.1007/s11571-016-9407-z.

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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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Boshkovski, Tommy, Ljupco Kocarev, Julien Cohen-Adad, Bratislav Mišić, Stéphane Lehéricy, Nikola Stikov, and Matteo Mancini. "The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure." Network Neuroscience 5, no. 2 (2021): 358–72. http://dx.doi.org/10.1162/netn_a_00179.

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Abstract Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.
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Coletta, Ludovico, Marco Pagani, Jennifer D. Whitesell, Julie A. Harris, Boris Bernhardt, and Alessandro Gozzi. "Network structure of the mouse brain connectome with voxel resolution." Science Advances 6, no. 51 (December 2020): eabb7187. http://dx.doi.org/10.1126/sciadv.abb7187.

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Fine-grained descriptions of brain connectivity are required to understand how neural information is processed and relayed across spatial scales. Previous investigations of the mouse brain connectome have used discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to connectome organization. Here, we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We report a number of previously unappreciated organizational principles in the mammalian brain, including a directional segregation of hub regions into neural sink and sources, and a strategic wiring of neuromodulatory nuclei as connector hubs and critical orchestrators of network communication. We also find that the mouse cortical connectome is hierarchically organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis, recapitulating a phylogenetically conserved feature of higher mammals. These findings advance our understanding of the foundational wiring principles of the mammalian connectome.
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Nair, P. "Connectome." Proceedings of the National Academy of Sciences 110, no. 15 (April 9, 2013): 5739. http://dx.doi.org/10.1073/pnas.1304921110.

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Sporns, Olaf. "Connectome." Scholarpedia 5, no. 2 (2010): 5584. http://dx.doi.org/10.4249/scholarpedia.5584.

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Dissertations / Theses on the topic "Connectome"

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Talmi, Sydney. "The Rhesus Macaque Corticospinal Connectome." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2087.

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The corticospinal tract (CST), which carries commands from the cerebral cortex to the spinal cord, is vital to fine motor control. Spinal cord injury (SCI) often damages CST axons, causing loss of motor function, most notably in the hands and legs. Our preliminary work in rats suggests that CST circuitry is complex: neurons whose axons project to the lower cervical spinal cord, which directly controls hand function, also send axon collaterals to other locations in the nervous system and may engage parallel motor systems. To inform research into repair of SCI, we therefore aimed to map the entire projection pattern, or “connectome,” of such cervically-projecting CST axons. In this study, we mapped the corticospinal connectome of the Rhesus macaque - an animal model more similar to humans, and therefore more clinically relevant for examining SCI. Comparison of the Rhesus macaque and rat CST connectome, and extrapolation to the human CST connectome, may improve targeting of treatments and rehabilitation after human SCI. To selectively trace cervically-projecting CST motor axons, a virus encoding a Cre-recombinase-dependent tracer (AAV-DIO-gCOMET) was injected into the hand motor cortex, and a virus encoding Cre-recombinase (AAV-Cre) was injected into the C8 level of the spinal cord. In this intersectional approach, the gCOMET virus infects many neurons in the cortex, but gCOMET expression is not turned on unless the nucleus also contains Cre-recombinase, which must be retrogradely transported from axon terminals in the C8 spinal cord. Thus, gCOMET is only expressed in neurons that project to the C8 spinal cord, and it proceeds to fill the entire neuron, including all axon collaterals. Any gCOMET-labeled axon segments observed in other regions of the nervous system are therefore collaterals of cervically-projecting axons. gCOMET-positive axons were immunohistochemically labeled, and axon density was quantified using a fluorescence microscope and Fiji/ImageJ software. Specific regions of interest were chosen for analysis because of their known relevance in motor function in humans, and for comparison to results of a similar study in rats. Results in the first monkey have revealed both similarities and differences between the monkey and rodent CST connectome. Analyses of additional monkeys are ongoing. The final results will provide detailed information about differences between rodent and primate CST, will serve as a baseline for examining changes in the CST connectome after SCI, and will provide guidance for studies targeting treatment and functional recovery after SCI.
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Coletta, Ludovico. "Mapping the mouse connectome with voxel resolution." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/335245.

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Fine-grained descriptions of brain connectivity are required to understand how neural information is processed and relayed across spatial scales. Prior investigations of the mouse brain connectome have employed discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to connectome organization. In this work, we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We found that hub regions and core network components of the voxel-wise mouse connectome exhibit a rich topography encompassing key cortical and subcortical relay regions. We also typified regional substrates based on their directional topology into sink or source regions, and reported a previously unappreciated role of modulatory nuclei as critical effectors of inter-modular and network communicability. Finally, we demonstrated a close spatial correspondence between the mesoscale topography of the mouse connectome and its functional macroscale organization, showing that, like in primates and humans, the mouse cortical connectome is organized along two major topographical axes that can be linked to hierarchical patterns of laminar connectivity, and shape the topography of fMRI dynamic states, respectively. This investigation was paralleled by further studies aimed to more closely relate structural connectome features to the corresponding large scale functional networks of the mouse brain. We first focused on the mouse default mode network (DMN), describing its axonal substrates with sublaminar precision and cell-type specificity. We found that regions of the mouse DMN are predominantly located within the isocortex and exhibit preferential connectivity. Dedicated tract tracing experiments carried out by the Allen Brain Institute revealed that layer 2/3 DMN neurons projected mostly in the DMN, whereas layer 5 neurons project both in and out. Further analyses revealed the presence of separate in-DMN and out-DMN-projecting cell types with distinct genetic profiles. Lastly, we carried out a fine-grained comparison of functional topography and dynamic organization of large-scale fMRI networks in wakeful and anesthetized mice, relating the corresponding functional networks to the underlying architecture of structural connectivity. Recapitulating prior observations in conscious primates, we found that the awake mouse brain is subjected to a profound topological reconfiguration such to maximize cross-talk between cortical and subcortical neural systems, departing from the underlying structure of the axonal connectome. Taken together, these results advance our understanding of the foundational wiring principles of the mammalian connectome, and create opportunities for identifying targets of interventions to modulate brain function and its network structure in a physiologically-accessible species.
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Jakubiuk, Wiktor. "High performance data processing pipeline for connectome segmentation." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/106122.

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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February 2016.
"December 2015." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-88).
By investigating neural connections, neuroscientists try to understand the brain and reconstruct its connectome. Automated connectome reconstruction from high resolution electron miscroscopy is a challenging problem, as all neurons and synapses in a volume have to be detected. A mm3 of a high-resolution brain tissue takes roughly a petabyte of space that the state-of-the-art pipelines are unable to process to date. A high-performance, fully automated image processing pipeline is proposed. Using a combination of image processing and machine learning algorithms (convolutional neural networks and random forests), the pipeline constructs a 3-dimensional connectome from 2-dimensional cross-sections of a mammal's brain. The proposed system achieves a low error rate (comparable with the state-of-the-art) and is capable of processing volumes of 100's of gigabytes in size. The main contributions of this thesis are multiple algorithmic techniques for 2- dimensional pixel classification of varying accuracy and speed trade-off, as well as a fast object segmentation algorithm. The majority of the system is parallelized for multi-core machines, and with minor additional modification is expected to work in a distributed setting.
by Wiktor Jakubiuk.
M. Eng. in Computer Science and Engineering
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Imms, Phoebe. "Dynamics of the structural connectome in traumatic brain injury." Phd thesis, Australian Catholic University, 2021. https://acuresearchbank.acu.edu.au/download/6b488e54a2b520f99bb0da2a3aa712de0a75b8ae25432bbd064934fb20f7b90c/16615424/Imms_2021_Dynamics_of_the_structural_connectome_in.pdf.

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Traumatic Brain Injury (TBI) is a leading cause of death and disability globally, with survivors often experiencing ongoing and debilitating cognitive impairments (e.g., slowed processing speed, poor attention, and executive functioning deficits). These impairments are often linked to focal lesions in regions of the cerebral cortex thought to uphold each cognitive function. However, the spectrum of impairments experienced by individual patients are not fully explained by focal lesions of the grey matter; instead, emerging theories suggest that many cognitive burdens result from disconnections in the white matter of the brain. With the advent of diffusion MRI (dMRI), new techniques are available to study how TBI disrupts the white matter pathways that connect brain regions (structural connectomics). Structural connectomics allows the quantification of network disruption in TBI patients using graph theoretical analyses, with studies reporting alterations in brain network integration and segregation. These studies suggest that graph metrics may be used as a ‘biomarker’ for TBI patients’ cognitive impairments, by linking changes in brain derived graph metrics to cognitive symptoms. However, challenges remain in ascribing behavioural relevance to graph metrics in this newly emerging field. This thesis critically evaluates the use of graph theoretical measures of the structural connectome in moderate-severe TBI, and their use at a single-subject level. First, a meta-analysis of studies comparing healthy controls and TBI patients using graph metrics is used to demonstrate that communication metrics are most robustly linked to brain injury. This review also highlights issues with the over-interpretation of the relationship between graph metrics such as path-length and the efficiency of cognitive processes. Second, a study in healthy adults shows that communication metrics are related to processing speed. This relationship between cognitive performance and measures of network alteration is underpinned by biologically plausible models of cognition and brain structure. Third, a profile of graph theoretical properties and alterations in six TBI patients is explored using a personalised connectomics approach. Spiderplots are used to represent graph metric alterations in each patient compared to healthy controls. Profiling individual patients in this way provides new insights into how graph metrics relate to lesion characteristics and TBI subtypes. Taken together, this thesis explores 1) how structural network topology is altered in patients with TBI, 2) how graph metrics can be interpreted, 3) how a personalised connectomics approach to TBI can be implemented, and 4) the methodological considerations for studying TBI using graph theory. The collective results of thesis indicate that graph metrics display potential for characterising network alterations in patients with brain injury; specifically, a profiling approach can account for heterogeneity in the TBI population, informing clinical decision making.
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Fountain-Zaragoza, Stephanie M. "Defining a Connectome-Based Neuromarker of Healthy Cognitive Aging." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1580068220500903.

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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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Blesa, Cábez Manuel. "Effect of perinatal adversity on structural connectivity of the developing brain." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33229.

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Globally, preterm birth (defined as birth at < 37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants.
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Nguyen, Quan M. Eng (Quan T. ). Massachusetts Institute of Technology. "Parallel and scalable neural image segmentation for connectome graph extraction." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100644.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Title as it appears in MIT Commencement Exercises program, June 5, 2015: Connectomics project : performance engineering neural image segmentation. Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
Segmentation of images, the process of grouping together pixels of the same object, is one of the major challenges in connectome extraction. Since connectomics data consist of large quantity of digital information generated by the electron microscope, there is a necessity for a highly scalable system that performs segmentation. To date, the state-of-the-art segmentation libraries such as GALA and NeuroProof lack parallel capability to be run on multicore machines in a distributed setting in order to achieve the scalability desired. Employing many performance engineering techniques, I parallelize a pipeline that uses the existing segmentation algorithms as building blocks to perform segmentation on EM grayscale images. For an input image stack of dimensions 1024 x 1024 x 100, the parallel segmentation program achieves a speedup of 5.3 counting I/O and 9.4 not counting I/O running on an 18-core machine. The program has become I/O bound, which is a better fit to run on a distributed computing framework. In this thesis, the contribution includes coming up with parallel algorithms for constructing a regional adjacency graph from labeled pixels and agglomerating an over-segmentation to obtain the final segmentation. The agglomeration process in particular is challenging to parallelize because most graph-based segmentation libraries entail very complex dependency. This has led many people to believe that the process is inherently sequential. However, I found a way to get good speedup by sacrificing some segmentation quality. It turns out that one could trade o a negligible amount in quality for a large gain in parallelism.
by Quan Nguyen.
M. Eng.
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Laurence, Edward. "Étude des systèmes complexes : des réseaux au connectome du cerveau." Master's thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/27149.

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La connectomique est l’étude des cartes de connectivité du cerveau (animal ou humain), qu’on nomme connectomes. À l’aide des outils développés par la science des réseaux complexes, la connectomique tente de décrire la complexité fonctionnelle et structurelle du cerveau. L’organisation des connexions du connectome, particulièrement la hiérarchie sous-jacente, joue un rôle majeur. Jusqu’à présent, les modèles hiérarchiques utilisés en connectomique sont pauvres en propriétés émergentes et présentent des structures régulières. Or, la complexité et la richesse hiérarchique du connectome et de réseaux réels ne sont pas saisies par ces modèles. Nous introduisons un nouveau modèle de croissance de réseaux hiérarchiques basé sur l’attachement préférentiel (HPA - Hierarchical preferential attachment). La calibration du modèle sur les propriétés structurelles de réseaux hiérarchiques réels permet de reproduire plusieurs propriétés émergentes telles que la navigabilité, la fractalité et l’agrégation. Le modèle permet entre autres de contrôler la structure hiérarchique et apporte un support supplémentaire quant à l’influence de la structure sur les propriétés émergentes. Puisque le cerveau est continuellement en activité, nous nous intéressons également aux propriétés dynamiques sur des structures hiérarchiques produites par HPA. L’existence d’états dynamiques d’activité soutenue, analogues à l’état minimal de l’activité cérébrale, est étudiée en imposant une dynamique neuronale binaire. Bien que l’organisation hiérarchique favorise la présence d’un état d’activité minimal, l’activité persistante émerge du contrôle de la propagation par la structure du réseau.
Connectomics is the study of the brain connectivity maps (animal or human), described as complex networks and named connectomes. The organization of the connections, including the network’s hidden hierarchy, plays a major role in our understanding of the functional and structural complexity of the brain. Until now, the hierarchical models in connectomics have exhibited few emergent properties and have proposed regular structures whereas conectomes and real networks show complex structures. We introduce a new growth model of hierarchical networks based on preferential attachment (HPA - hierarchical preferential attachment). The structure can be controlled by a small set of parameters to fit real networks. We show how functional properties emerge from the projection of the hierarchical organization. Furthermore, we use HPA to investigate the minimum level of activity of the brain. The network response under binary dynamics shows evidence of persistent activity, similar to the resting-state of the brain. Even though hierarchical organization is beneficial for sustained activity, we show that persistent activity emerges from the control of the structure over the dynamics.
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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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Books on the topic "Connectome"

1

Fountoulakis, Kostas N. The Human Connectome. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10351-3.

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Hu, Dewen, and Ling-Li Zeng. Pattern Analysis of the Human Connectome. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9523-0.

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Turkcan, Mehmet Kerem. Sensory Processing and Associative Learning in Connectome-Based Neural Circuits. [New York, N.Y.?]: [publisher not identified], 2022.

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Yi, Sun Tianqi, ed. Lian jie zu: Zao jiu du yi wu er de ni = Connectome. Beijing: Qing hua da xue chu ban she, 2015.

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Mroczkowski, Robert S. Trilogy of connectors: Basic principles and connector design explanations. Waldenburg, Germany: Würth Elektronik, 2010.

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Fisher-Buttinger, Claudia, and Christine Vallaster, eds. Connective Branding. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119208396.

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Kuzmeski, Maribeth. The Connectors. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009. http://dx.doi.org/10.1002/9781118257890.

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S, Kaderlan Norman, ed. Connective planning. New York: McGraw-Hill, 1994.

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Kuzmeski, Maribeth. The Connectors. New York: John Wiley & Sons, Ltd., 2009.

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Massachusetts. Administering Agency for Developmental Disabilities. Community connectors. Yarmouthport, Mass: Community Connections, 1994.

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Book chapters on the topic "Connectome"

1

Bharioke, Arjun, Louis K. Scheffer, Dmitri B. Chklovskii, and Ian A. Meinertzhagen. "Connectome, Drosophila." In Encyclopedia of Computational Neuroscience, 793–98. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_275.

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Choe, Yoonsuck, Jaerock Kwon, David Mayerich, and Louise C. Abbott. "Connectome, Mouse." In Encyclopedia of Computational Neuroscience, 807–10. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_276.

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Choe, Yoonsuck. "Connectome, General." In Encyclopedia of Computational Neuroscience, 798–806. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_277.

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Bharioke, Arjun, Louis K. Scheffer, Dmitri B. Chklovskii, and Ian A. Meinertzhagen. "Drosophila Connectome." In Encyclopedia of Computational Neuroscience, 1–6. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_275-1.

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Bharioke, Arjun, Louis K. Scheffer, Dmitri B. Chklovskii, and Ian A. Meinertzhagen. "Connectome, Drosophila." In Encyclopedia of Computational Neuroscience, 1–5. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7320-6_275-2.

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Choe, Yoonsuck, Jaerock Kwon, David Mayerich, and Louise C. Abbott. "Connectome, Mouse." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_276-1.

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Choe, Yoonsuck. "Connectome, General." In Encyclopedia of Computational Neuroscience, 1–11. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_277-1.

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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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Elam, Jennifer Stine, and David Van Essen. "Human Connectome Project." In Encyclopedia of Computational Neuroscience, 1408–11. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_592.

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Elam, Jennifer Stine, and David Van Essen. "Human Connectome Project." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_592-1.

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Conference papers on the topic "Connectome"

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Bolton, Thomas A. W., Mikkel Schöttner, Jagruti Patel, and Patric Hagmann. "Introducing Edge-Wise Graph Signal Processing: Application to Connectome Fingerprinting." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635106.

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Yuan, Yue, and Yanjiang Wang. "Structure-Function Coupling in the Human Connectome with Hypergraph Neural Networks." In 2024 IEEE 17th International Conference on Signal Processing (ICSP), 713–18. IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846404.

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Wu, Dongya, and Xin Li. "Connectome-based prediction of individual behaviors via convolutional graph propagation network." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–4. IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781694.

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Nishimura, Ryo, and Makoto Fukushima. "Comparing Connectivity-To-Reservoir Conversion Methods for Connectome-Based Reservoir Computing." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650803.

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Kim, Byung-Hoon, Jungwon Choi, EungGu Yun, Kyungsang Kim, Xiang Li, and Juho Lee. "Learning Dynamic Brain Connectome with Graph Transformers for Psychiatric Diagnosis Classification." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635508.

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Lyu, Yanjun, Lu Zhang, Xiaowei Yu, Chao Cao, Tianming Liu, and Dajiang Zhu. "Mild Cognitive Impairment Classification Using A Novel Finer-Scale Brain Connectome." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635558.

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Liu, Scotte, Wei-Kun Chang, Ming-Chin Wu, Yi-Hao Lin, Po-Jui Chen, Wei-Jei Peng, Ming-Fu Chen, Ann-Shyn Chiang, and Fu-Jen Kao. "Visualizing Drosophila connectome with multiview light-sheet macrophotography and iterative expansion microscopy." In Multiphoton Microscopy in the Biomedical Sciences XXV, edited by Ammasi Periasamy, Peter T. So, and Karsten König, 40. SPIE, 2025. https://doi.org/10.1117/12.3047770.

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Fukushima, Makoto, and Kenji Leibnitz. "Comparison of Message-Switched and Packet-Switched Communication Simulated on the Human Connectome." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–4. IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782030.

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Scipioni, M., J. Corbeil, M. S. Allen, A. C. Moos, A. Mareyam, J. Kirsch, L. Byars, L. L. Wald, M. Judenhofer, and C. Catana. "Updates on the Gantry Design and Manufacturing for the Human Dynamic NeuroChemical Connectome." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10658075.

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Allen, M. S., L. Byars, L. Rauscher, F. P. Schmidt, M. Scipioni, M. Puryear, J. M. Udias, M. Judenhofer, and C. Catana. "Coincidence Timing Performance Optimization of the Detector for the Human Dynamic NeuroChemical Connectome Scanner." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657258.

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Reports on the topic "Connectome"

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Oostrom, Marjolein, Rogene Eichler West, Moses Obiri, Michael Muniak, Paritosh Pande, Sarah Akers, Tianyi Mao, and Bobbie-Jo Webb-Robertson. Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1985702.

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Allen, Robert, and David Garlan. Formal Connectors. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada277611.

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Drapela, Timothy J. Optical fiber connectors :. Gaithersburg, MD: National Bureau of Standards, 1998. http://dx.doi.org/10.6028/nist.tn.1503.

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Kurita, C. H. High Voltage Connector. Office of Scientific and Technical Information (OSTI), March 1987. http://dx.doi.org/10.2172/1030738.

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Litzelfelner. L51573c Pipe Connection Methods and Effects on Deepwater Construction C. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1988. http://dx.doi.org/10.55274/r0010684.

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A study to review and evaluate the various mechanical pipeline connectors currently available for J-curve pipelay methods offshore. The connectors were compared by listing the advantages and disadvantages of each. A test program to evaluate connectors was developed.
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Litzelfelner. L51573e Pipe Connection Methods and Effects on Deepwater Construction E. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1988. http://dx.doi.org/10.55274/r0010689.

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A study to review and evaluate the various mechanical pipeline connectors currently available for J-curve pipelay methods offshore. The connectors were compared by listing the advantages and disadvantages of each. A test program to evaluate connectors was developed.
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Litzelfelner. L51573a Pipe Connection Methods and Effects on Deepwater Construction. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1988. http://dx.doi.org/10.55274/r0010531.

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A study to review and evaluate the various mechanical pipeline connectors currently available for J-curve pipelay methods offshore. The connectors were compared by listing the advantages and disadvantages of each. A test program to evaluate connectors was developed.
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Litzelfelner. L51573d Pipe Connection Methods and Effects on Deepwater Construction D. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1988. http://dx.doi.org/10.55274/r0010685.

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A study to review and evaluate the various mechanical pipeline connectors currently available for J-curve pipelay methods offshore. The connectors were compared by listing the advantages and disadvantages of each. A test program to evaluate connectors was developed.
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Litzelfelner. L51573b Pipe Connection Methods and Effects on Deepwater Construction B. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1988. http://dx.doi.org/10.55274/r0010690.

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A study to review and evaluate the various mechanical pipeline connectors currently available for J-curve pipelay methods offshore. The connectors were compared by listing the advantages and disadvantages of each. A test program to evaluate connectors was developed.
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10

Parazin, R. J. Remote connector development study. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/94614.

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