{"topic":"connectomics","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"doi:10.1016/j.neuroimage.2015.10.019","kind":"article","title":"An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging","authors":["Andersson JLR","Sotiropoulos SN"],"author_count":2,"year":2016,"published":"2015-10-20","venue":"NeuroImage","cited_by":3382,"doi":"10.1016/j.neuroimage.2015.10.019","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.neuroimage.2015.10.019","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.neuroimage.2015.10.019","updated":null,"pmid":"26481672","pmcid":"PMC4692656","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC4692656/pdf","abstract":"In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal-Tanner gradients on a 3T Siemens Verio, a 3T Siemens Connectome Skyra or a 7T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging\" by Andersson JLR, Sotiropoulos SN, https://doi.org/10.1016/j.neuroimage.2015.10.019, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1371/journal.pone.0068910","kind":"article","title":"BrainNet Viewer: a network visualization tool for human brain connectomics","authors":["Xia M","Wang J","He Y"],"author_count":3,"year":2013,"published":"2013-07-04","venue":"PloS one","cited_by":3349,"doi":"10.1371/journal.pone.0068910","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1371/journal.pone.0068910","archive_url":"https://space-kitty.com/papers/doi:10.1371/journal.pone.0068910","updated":null,"pmid":"23861951","pmcid":"PMC3701683","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC3701683/pdf","abstract":"The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"BrainNet Viewer: a network visualization tool for human brain connectomics\" by Xia M, Wang J, He Y, https://doi.org/10.1371/journal.pone.0068910, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-023-06812-z","kind":"article","title":"A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain","authors":["Zizhen Yao","Cindy T. J. van Velthoven","Michael Kunst","Meng Zhang","Delissa McMillen","Changkyu Lee","Won Jung","Jeff Goldy","Aliya Abdelhak","Matthew Aitken","Katherine S. Baker","Pamela M. Baker","Eliza R. Barkan","Darren Bertagnolli","Ashwin A. Bhandiwad","Cameron Bielstein","Prajal Bishwakarma","Jazmin Campos","Daniel Carey","Tamara Casper","Anish Bhaswanth Chakka","Rushil Chakrabarty","Sakshi Chavan","Min Chen","Michael Clark"],"author_count":100,"year":2023,"published":"2023-12-13","venue":"Nature","cited_by":1325,"doi":"10.1038/s41586-023-06812-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-023-06812-z","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-023-06812-z","updated":null,"pmid":"37034735","pmcid":"PMC10719114","openalex_id":"W4389670555","pdf_url":"https://europepmc.org/articles/PMC10719114/pdf","abstract":". Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain\" by Zizhen Yao, Cindy T. J. van Velthoven, Michael Kunst et al., https://doi.org/10.1038/s41586-023-06812-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-024-07558-y","kind":"article","title":"Neuronal wiring diagram of an adult brain","authors":["Sven Dorkenwald","Arie Matsliah","Amy Robinson Sterling","Philipp Schlegel","Szi-chieh Yu","Claire E. McKellar","Albert Lin","Marta Costa","Katharina Eichler","Yijie Yin","William Silversmith","Casey M Schneider-Mizell","Chris S. Jordan","Derrick Brittain","Akhilesh Halageri","Kai Kuehner","Oluwaseun Ogedengbe","Ryan Morey","Jay Gager","Krzysztof Kruk","Eric Perlman","Runzhe Yang","David S. Deutsch","Doug Bland","Marissa Sorek"],"author_count":100,"year":2024,"published":"2024-10-02","venue":"Nature","cited_by":574,"doi":"10.1038/s41586-024-07558-y","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-07558-y","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-07558-y","updated":null,"pmid":"39358518","pmcid":"PMC11446842","openalex_id":"W4403062911","pdf_url":"https://europepmc.org/articles/PMC11446842/pdf","abstract":"Abstract Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative 1–6 , but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 10 7 chemical synapses 7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster 8,9 . The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities 10–12 . Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome—a map of projections between regions—from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Neurobiology and Insect Physiology Research"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Neuronal wiring diagram of an adult brain\" by Sven Dorkenwald, Arie Matsliah, Amy Robinson Sterling et al., https://doi.org/10.1038/s41586-024-07558-y, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-024-07686-5","kind":"article","title":"Whole-brain annotation and multi-connectome cell typing of Drosophila","authors":["Philipp Schlegel","Yijie Yin","Alexander Shakeel Bates","Sven Dorkenwald","Katharina Eichler","Paul S Brooks","Daniel Han","Marina Gkantia","Marcia dos Santos","Eva J Munnelly","Griffin S. Badalamente","Laia Serratosa Capdevila","Varun Aniruddha Sane","Alexandra M. C. Fragniere","Ladann Kiassat","Markus William Pleijzier","Tomke Stürner","Imaan FM Tamimi","Christopher R Dunne","Irene Salgarella","Alexandre Javier","Siqi Fang","Eric Perlman","Tom Kazimiers","Sridhar R. Jagannathan"],"author_count":100,"year":2024,"published":"2024-10-02","venue":"Nature","cited_by":412,"doi":"10.1038/s41586-024-07686-5","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-07686-5","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-07686-5","updated":null,"pmid":"39358521","pmcid":"PMC11446831","openalex_id":"W4403062913","pdf_url":"https://europepmc.org/articles/PMC11446831/pdf","abstract":"Abstract The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome 1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome 2 , and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Neurobiology and Insect Physiology Research"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Whole-brain annotation and multi-connectome cell typing of Drosophila\" by Philipp Schlegel, Yijie Yin, Alexander Shakeel Bates et al., https://doi.org/10.1038/s41586-024-07686-5, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41467-017-01285-x","kind":"article","title":"The challenge of mapping the human connectome based on diffusion tractography","authors":["Maier-Hein KH","Neher PF","Houde JC","Côté MA","Garyfallidis E","Zhong J","Chamberland M","Yeh FC","Lin YC","Ji Q","Reddick WE","Glass JO","Chen DQ","Feng Y","Gao C","Wu Y","Ma J","He R","Li Q","Westin CF","Deslauriers-Gauthier S","González JOO","Paquette M","St-Jean S","Girard G"],"author_count":77,"year":2017,"published":"2017-11-07","venue":"Nature communications","cited_by":1123,"doi":"10.1038/s41467-017-01285-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-017-01285-x","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-017-01285-x","updated":null,"pmid":"29116093","pmcid":"PMC5677006","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5677006/pdf","abstract":"Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"The challenge of mapping the human connectome based on diffusion tractography\" by Maier-Hein KH, Neher PF, Houde JC et al., https://doi.org/10.1038/s41467-017-01285-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.7554/elife.57443","kind":"review","title":"A connectome and analysis of the adult <i>Drosophila</i> central brain","authors":["Scheffer LK","Xu CS","Januszewski M","Lu Z","Takemura SY","Hayworth KJ","Huang GB","Shinomiya K","Maitlin-Shepard J","Berg S","Clements J","Hubbard PM","Katz WT","Umayam L","Zhao T","Ackerman D","Blakely T","Bogovic J","Dolafi T","Kainmueller D","Kawase T","Khairy KA","Leavitt L","Li PH","Lindsey L"],"author_count":108,"year":2020,"published":"2020-09-07","venue":"eLife","cited_by":968,"doi":"10.7554/elife.57443","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.7554/elife.57443","archive_url":"https://space-kitty.com/papers/doi:10.7554/elife.57443","updated":null,"pmid":"32880371","pmcid":"PMC7546738","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC7546738/pdf","abstract":"The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"A connectome and analysis of the adult <i>Drosophila</i> central brain\" by Scheffer LK, Xu CS, Januszewski M et al., https://doi.org/10.7554/elife.57443, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1371/journal.pone.0080713","kind":"article","title":"Deterministic diffusion fiber tracking improved by quantitative anisotropy","authors":["Yeh FC","Verstynen TD","Wang Y","Fernández-Miranda JC","Tseng WY"],"author_count":5,"year":2013,"published":"2013-11-15","venue":"PloS one","cited_by":962,"doi":"10.1371/journal.pone.0080713","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1371/journal.pone.0080713","archive_url":"https://space-kitty.com/papers/doi:10.1371/journal.pone.0080713","updated":null,"pmid":"24348913","pmcid":"PMC3858183","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC3858183/pdf","abstract":"Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Deterministic diffusion fiber tracking improved by quantitative anisotropy\" by Yeh FC, Verstynen TD, Wang Y et al., https://doi.org/10.1371/journal.pone.0080713, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/brain/awu132","kind":"article","title":"The hubs of the human connectome are generally implicated in the anatomy of brain disorders","authors":["Crossley NA","Mechelli A","Scott J","Carletti F","Fox PT","McGuire P","Bullmore ET"],"author_count":7,"year":2014,"published":"2014-06-19","venue":"Brain : a journal of neurology","cited_by":959,"doi":"10.1093/brain/awu132","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/brain/awu132","archive_url":"https://space-kitty.com/papers/doi:10.1093/brain/awu132","updated":null,"pmid":"25057133","pmcid":"PMC4107735","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC4107735/pdf","abstract":"Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"The hubs of the human connectome are generally implicated in the anatomy of brain disorders\" by Crossley NA, Mechelli A, Scott J et al., https://doi.org/10.1093/brain/awu132, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fnhum.2015.00386","kind":"article","title":"GRETNA: a graph theoretical network analysis toolbox for imaging connectomics","authors":["Wang J","Wang X","Xia M","Liao X","Evans A","He Y"],"author_count":6,"year":2015,"published":"2015-06-30","venue":"Frontiers in human neuroscience","cited_by":958,"doi":"10.3389/fnhum.2015.00386","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnhum.2015.00386","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnhum.2015.00386","updated":null,"pmid":"26175682","pmcid":"PMC4485071","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC4485071/pdf","abstract":"Recent studies have suggested that the brain's structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"GRETNA: a graph theoretical network analysis toolbox for imaging connectomics\" by Wang J, Wang X, Xia M et al., https://doi.org/10.3389/fnhum.2015.00386, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.cell.2024.03.016","kind":"article","title":"Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster","authors":["Nils Eckstein","Alexander Shakeel Bates","Andrew S Champion","Michelle Du","Yijie Yin","Philipp Schlegel","Alicia Kun-Yang Lu","Jon Thomson Rymer","Samantha Finley-May","Tyler Paterson","Ruchi Parekh","Sven Dorkenwald","Arie Matsliah","Szi-chieh Yu","Claire E. McKellar","Amy Robinson Sterling","Katharina Eichler","Marta Costa","H. Sebastian Seung","Mala Murthy","Volker Hartenstein","Gregory S.X.E. Jefferis","Jan Funke"],"author_count":23,"year":2024,"published":"2024-05-01","venue":"Cell","cited_by":214,"doi":"10.1016/j.cell.2024.03.016","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2024.03.016","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2024.03.016","updated":null,"pmid":"38729112","pmcid":null,"openalex_id":"W4396780652","pdf_url":"http://www.cell.com/article/S0092867424003076/pdf","abstract":"High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Neurobiology and Insect Physiology Research"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster\" by Nils Eckstein, Alexander Shakeel Bates, Andrew S Champion et al., https://doi.org/10.1016/j.cell.2024.03.016, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/cercor/bhy109","kind":"article","title":"Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants","authors":["Ritchie SJ","Cox SR","Shen X","Lombardo MV","Reus LM","Alloza C","Harris MA","Alderson HL","Hunter S","Neilson E","Liewald DCM","Auyeung B","Whalley HC","Lawrie SM","Gale CR","Bastin ME","McIntosh AM","Deary IJ"],"author_count":18,"year":2018,"published":"2018-08-01","venue":"Cerebral cortex (New York, N.Y. : 1991)","cited_by":649,"doi":"10.1093/cercor/bhy109","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/cercor/bhy109","archive_url":"https://space-kitty.com/papers/doi:10.1093/cercor/bhy109","updated":null,"pmid":"29771288","pmcid":"PMC6041980","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC6041980/pdf","abstract":"Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44-77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants\" by Ritchie SJ, Cox SR, Shen X et al., https://doi.org/10.1093/cercor/bhy109, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41592-021-01330-0","kind":"article","title":"FlyWire: online community for whole-brain connectomics","authors":["Sven Dorkenwald","Claire E. McKellar","Thomas Macrina","Nico Kemnitz","Kisuk Lee","Ran Lu","Jingpeng Wu","Sergiy Popovych","Eric Mitchell","Barak Nehoran","Zhen Jia","J. Alexander Bae","Shang Mu","Dodam Ih","Manuel A Castro","Oluwaseun Ogedengbe","Akhilesh Halageri","Kai Kuehner","Amy Robinson Sterling","Zoe C. Ashwood","Jonathan Zung","Derrick Brittain","Forrest Collman","Casey M Schneider-Mizell","Chris A. Jordan"],"author_count":43,"year":2021,"published":"2021-12-23","venue":"Nature Methods","cited_by":289,"doi":"10.1038/s41592-021-01330-0","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41592-021-01330-0","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41592-021-01330-0","updated":null,"pmid":"34949809","pmcid":null,"openalex_id":"W4206755867","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2020/08/30/2020.08.30.274225.full.pdf","abstract":null,"abstract_license":null,"license":"cc-by-nd","is_open_access":true,"peer_reviewed":null,"categories":["Cell Image Analysis Techniques"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s42003-020-0794-7","kind":"article","title":"BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets","authors":["Vos de Wael R","Benkarim O","Paquola C","Lariviere S","Royer J","Tavakol S","Xu T","Hong SJ","Langs G","Valk S","Misic B","Milham M","Margulies D","Smallwood J","Bernhardt BC"],"author_count":15,"year":2020,"published":"2020-03-05","venue":"Communications biology","cited_by":580,"doi":"10.1038/s42003-020-0794-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s42003-020-0794-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s42003-020-0794-7","updated":null,"pmid":"32139786","pmcid":"PMC7058611","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC7058611/pdf","abstract":"Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets\" by Vos de Wael R, Benkarim O, Paquola C et al., https://doi.org/10.1038/s42003-020-0794-7, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.neuroimage.2018.07.070","kind":"article","title":"TractSeg - Fast and accurate white matter tract segmentation","authors":["Wasserthal J","Neher P","Maier-Hein KH"],"author_count":3,"year":2018,"published":"2018-08-04","venue":"NeuroImage","cited_by":582,"doi":"10.1016/j.neuroimage.2018.07.070","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.neuroimage.2018.07.070","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.neuroimage.2018.07.070","updated":null,"pmid":"30086412","pmcid":null,"openalex_id":null,"pdf_url":null,"abstract":"The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines is a unique combination of tools which enables the in-vivo delineation and analysis of anatomically well-known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation. We demonstrate that the proposed approach is much faster than existing methods while providing unprecedented accuracy, using a population of 105 subjects from the Human Connectome Project. We also show initial evidence that TractSeg is able to generalize to differently acquired data sets for most of the bundles. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"TractSeg - Fast and accurate white matter tract segmentation\" by Wasserthal J, Neher P, Maier-Hein KH, https://doi.org/10.1016/j.neuroimage.2018.07.070, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fnhum.2013.00042","kind":"article","title":"PANDA: a pipeline toolbox for analyzing brain diffusion images","authors":["Cui Z","Zhong S","Xu P","He Y","Gong G"],"author_count":5,"year":2013,"published":"2013-02-21","venue":"Frontiers in human neuroscience","cited_by":614,"doi":"10.3389/fnhum.2013.00042","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnhum.2013.00042","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnhum.2013.00042","updated":null,"pmid":"23439846","pmcid":"PMC3578208","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC3578208/pdf","abstract":"Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named \"Pipeline for Analyzing braiN Diffusion imAges\" (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"PANDA: a pipeline toolbox for analyzing brain diffusion images\" by Cui Z, Zhong S, Xu P et al., https://doi.org/10.3389/fnhum.2013.00042, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-024-07968-y","kind":"article","title":"Network statistics of the whole-brain connectome of Drosophila","authors":["Albert Lin","Runzhe Yang","Sven Dorkenwald","Arie Matsliah","Amy Robinson Sterling","Philipp Schlegel","Szi-chieh Yu","Claire E. McKellar","Marta Costa","Katharina Eichler","Alexander Shakeel Bates","Nils Eckstein","Jan Funke","Gregory S.X.E. Jefferis","Mala Murthy"],"author_count":15,"year":2024,"published":"2024-10-02","venue":"Nature","cited_by":147,"doi":"10.1038/s41586-024-07968-y","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-07968-y","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-07968-y","updated":null,"pmid":"37547019","pmcid":null,"openalex_id":"W4403062850","pdf_url":null,"abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"peer_reviewed":null,"categories":["Functional Brain Connectivity Studies"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1101/2023.06.27.546656","kind":"article","title":"Neuronal wiring diagram of an adult brain","authors":["Sven Dorkenwald","Arie Matsliah","Amy Robinson Sterling","Philipp Schlegel","Szi-chieh Yu","Claire E. McKellar","Albert Lin","Marta Costa","Katharina Eichler","Yijie Yin","William Silversmith","Casey M Schneider-Mizell","Chris S. Jordan","Derrick Brittain","Akhilesh Halageri","Kai Kuehner","Oluwaseun Ogedengbe","Ryan Morey","Jay Gager","Krzysztof Kruk","Eric Perlman","Runzhe Yang","David S. Deutsch","Doug Bland","Marissa Sorek"],"author_count":46,"year":2023,"published":"2023-06-30","venue":"bioRxiv (Cold Spring Harbor Laboratory)","cited_by":149,"doi":"10.1101/2023.06.27.546656","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1101/2023.06.27.546656","archive_url":"https://space-kitty.com/papers/doi:10.1101/2023.06.27.546656","updated":null,"pmid":"37425937","pmcid":null,"openalex_id":"W4382684379","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2023/07/11/2023.06.27.546656.full.pdf","abstract":"Abstract Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×10 7 chemical synapses between ∼130,000 neurons reconstructed from a female Drosophila melanogaster . The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.","abstract_license":"CC-BY-ND (verbatim) (publisher license, via OpenAlex)","license":"cc-by-nd","is_open_access":true,"peer_reviewed":null,"categories":["Neurobiology and Insect Physiology Research"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Neuronal wiring diagram of an adult brain\" by Sven Dorkenwald, Arie Matsliah, Amy Robinson Sterling et al., https://doi.org/10.1101/2023.06.27.546656, used under its Creative Commons Attribution license (CC-BY-ND); markup removed, otherwise unmodified."},{"id":"doi:10.1038/sdata.2017.10","kind":"article","title":"Enhancing studies of the connectome in autism using the autism brain imaging data exchange II","authors":["Di Martino A","O'Connor D","Chen B","Alaerts K","Anderson JS","Assaf M","Balsters JH","Baxter L","Beggiato A","Bernaerts S","Blanken LM","Bookheimer SY","Braden BB","Byrge L","Castellanos FX","Dapretto M","Delorme R","Fair DA","Fishman I","Fitzgerald J","Gallagher L","Keehn RJ","Kennedy DP","Lainhart JE","Luna B"],"author_count":39,"year":2017,"published":"2017-03-14","venue":"Scientific data","cited_by":536,"doi":"10.1038/sdata.2017.10","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/sdata.2017.10","archive_url":"https://space-kitty.com/papers/doi:10.1038/sdata.2017.10","updated":null,"pmid":"28291247","pmcid":"PMC5349246","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5349246/pdf","abstract":"The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Enhancing studies of the connectome in autism using the autism brain imaging data exchange II\" by Di Martino A, O'Connor D, Chen B et al., https://doi.org/10.1038/sdata.2017.10, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1506.06795","kind":"article","title":"Generative models of the human connectome","authors":["Richard F. Betzel","Andrea Avena-Koenigsberger","Joaquín Goñi","Ye He","Marcel A. de Reus","Alessandra Griffa","Petra E. Vértes","Bratislav Mišić","Jean-Philippe Thiran","Patric Hagmann","Martijn van den Heuvel","Xi-Nian Zuo","Edward T. Bullmore","Olaf Sporns"],"author_count":14,"year":2015,"published":"2015-06-22","venue":"NeuroImage","cited_by":303,"doi":"10.1016/j.neuroimage.2015.09.041","arxiv_id":"1506.06795","nct_id":null,"source_url":"https://arxiv.org/abs/1506.06795","archive_url":"https://space-kitty.com/papers/arxiv:1506.06795","updated":"2015-09-19","pmid":"26427642","pmcid":"PMC4655950","openalex_id":"W2182367256","pdf_url":"https://arxiv.org/pdf/1506.06795","abstract":"The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":["q-bio.NC"],"details":null,"provenance":"arxiv+europepmc","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.neuroimage.2016.12.036","kind":"article","title":"Hand classification of fMRI ICA noise components","authors":["Griffanti L","Douaud G","Bijsterbosch J","Evangelisti S","Alfaro-Almagro F","Glasser MF","Duff EP","Fitzgibbon S","Westphal R","Carone D","Beckmann CF","Smith SM"],"author_count":12,"year":2017,"published":"2016-12-16","venue":"NeuroImage","cited_by":500,"doi":"10.1016/j.neuroimage.2016.12.036","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.neuroimage.2016.12.036","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.neuroimage.2016.12.036","updated":null,"pmid":"27989777","pmcid":"PMC5489418","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5489418/pdf","abstract":"We present a practical \"how-to\" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Hand classification of fMRI ICA noise components\" by Griffanti L, Douaud G, Bijsterbosch J et al., https://doi.org/10.1016/j.neuroimage.2016.12.036, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41467-019-08944-1","kind":"article","title":"Atypical functional connectome hierarchy in autism","authors":["Hong SJ","Vos de Wael R","Bethlehem RAI","Lariviere S","Paquola C","Valk SL","Milham MP","Di Martino A","Margulies DS","Smallwood J","Bernhardt BC"],"author_count":11,"year":2019,"published":"2019-03-04","venue":"Nature communications","cited_by":460,"doi":"10.1038/s41467-019-08944-1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-019-08944-1","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-019-08944-1","updated":null,"pmid":"30833582","pmcid":"PMC6399265","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC6399265/pdf","abstract":"One paradox of autism is the co-occurrence of deficits in sensory and higher-order socio-cognitive processing. Here, we examined whether these phenotypical patterns may relate to an overarching system-level imbalance-specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Combining connectome gradient and stepwise connectivity analysis based on task-free functional magnetic resonance imaging (fMRI), we demonstrated atypical connectivity transitions between sensory and higher-order default mode regions in a large cohort of individuals with autism relative to typically-developing controls. Further analyses indicated that reduced differentiation related to perturbed stepwise connectivity from sensory towards transmodal areas, as well as atypical long-range rich-club connectivity. Supervised pattern learning revealed that hierarchical features predicted deficits in social cognition and low-level behavioral symptoms, but not communication-related symptoms. Our findings provide new evidence for imbalances in network hierarchy in autism, which offers a parsimonious reference frame to consolidate its diverse features.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Atypical functional connectome hierarchy in autism\" by Hong SJ, Vos de Wael R, Bethlehem RAI et al., https://doi.org/10.1038/s41467-019-08944-1, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.schres.2016.07.014","kind":"review","title":"The dysconnection hypothesis (2016)","authors":["Friston K","Brown HR","Siemerkus J","Stephan KE"],"author_count":4,"year":2016,"published":"2016-07-20","venue":"Schizophrenia research","cited_by":476,"doi":"10.1016/j.schres.2016.07.014","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.schres.2016.07.014","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.schres.2016.07.014","updated":null,"pmid":"27450778","pmcid":"PMC5147460","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5147460/pdf","abstract":"Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience. In particular, we examine the dysconnection hypothesis in the context of (i) theoretical neurobiology and computational psychiatry; (ii) the empirical insights afforded by neuroimaging and associated connectomics - and (iii) how bottom-up (molecular biology and genetics) and top-down (systems biology) perspectives are converging on the mechanisms and nature of dysconnections in schizophrenia.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"The dysconnection hypothesis (2016)\" by Friston K, Brown HR, Siemerkus J et al., https://doi.org/10.1016/j.schres.2016.07.014, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fnins.2019.00585","kind":"review","title":"Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review","authors":["Farahani FV","Karwowski W","Lighthall NR"],"author_count":3,"year":2019,"published":"2019-06-06","venue":"Frontiers in neuroscience","cited_by":458,"doi":"10.3389/fnins.2019.00585","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnins.2019.00585","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnins.2019.00585","updated":null,"pmid":"31249501","pmcid":"PMC6582769","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC6582769/pdf","abstract":"Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review\" by Farahani FV, Karwowski W, Lighthall NR, https://doi.org/10.3389/fnins.2019.00585, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1608.08793","kind":"article","title":"The multilayer connectome of Caenorhabditis elegans","authors":["Barry Bentley","Robyn Branicky","Christopher L. Barnes","Edward T. Bullmore","Petra E. Vértes","William R. Schafer"],"author_count":6,"year":2016,"published":"2016-08-31","venue":"PLoS Computational Biology","cited_by":269,"doi":"10.1371/journal.pcbi.1005283","arxiv_id":"1608.08793","nct_id":null,"source_url":"https://arxiv.org/abs/1608.08793","archive_url":"https://space-kitty.com/papers/arxiv:1608.08793","updated":"2016-08-31","pmid":null,"pmcid":null,"openalex_id":"W2514803758","pdf_url":"https://arxiv.org/pdf/1608.08793","abstract":"Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function. Here we present a draft monoamine connectome, along with a partial neuropeptide connectome, for the nematode C. elegans, based on new and published expression data for biosynthetic genes and receptors. Thus, the neuronal connectome can be represented as a multiplex network, with synaptic, gap junction, and neuromodulatory layers representing alternative modes of interneuronal interaction and with distinct network structures. In particular, the monoamine network exhibits novel topological properties, with a highly disassortative star-like structure and a rich-club of interconnected broadcasting hubs. Despite the low degree of overlap between layers, we find highly significant modes of interaction, pinpointing network locations and multilink motifs where aminergic and neuropeptide signalling modulate synaptic activity. The multilayer connectome of C. elegans represents a clear exemplar of a biological multiplex network and provides a prototype for understanding how extrasynaptic signalling can be integrated into the wired circuitry in larger brains.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"peer_reviewed":null,"categories":["q-bio.NC"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."}],"next":"https://space-kitty.com/api/v1/topics/connectomics/papers?sort=top&limit=25&offset=25"}