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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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McKinstry-Wu, Andrew R., and Max B. Kelz. "Connectome." Anesthesia & Analgesia 117, no. 6 (December 2013): 1513–14. http://dx.doi.org/10.1213/ane.0b013e3182a8af6a.
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Byrge, Lisa, and Daniel P. Kennedy. "High-accuracy individual identification using a “thin slice” of the functional connectome." Network Neuroscience 3, no. 2 (January 2019): 363–83. http://dx.doi.org/10.1162/netn_a_00068.
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Connectome fingerprinting—a method that uses many thousands of functional connections in aggregate to identify individuals—holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance—how many and which functional connections are necessary and/or sufficient for high accuracy—will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small “thin slice” of the connectome—as few as 40 out of 64,620 functional connections—was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.
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Rubinov, Mikail, Rolf J. F. Ypma, Charles Watson, and Edward T. Bullmore. "Wiring cost and topological participation of the mouse brain connectome." Proceedings of the National Academy of Sciences 112, no. 32 (July 27, 2015): 10032–37. http://dx.doi.org/10.1073/pnas.1420315112.
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Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of “wiring cost” explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain. We estimated brain-wide weights, distances, and wiring costs of axonal projections and performed a multiscale topological and spatial analysis of the resulting weighted and directed mouse brain connectome. Our analysis showed that the mouse connectome has small-world properties, a hierarchical modular structure, and greater-than-minimal wiring costs. High-participation hubs of this connectome mediated communication between functionally specialized and anatomically localized modules, had especially high wiring costs, and closely corresponded to regions of the default mode network. Analyses of independently acquired histological and gene-expression data showed that nodal participation colocalized with low neuronal density and high expression of genes enriched for cognition, learning and memory, and behavior. The mouse connectome contains high-participation hubs, which are not explained by wiring-cost minimization but instead reflect competitive selection pressures for integrated network topology as a basis for higher cognitive and behavioral functions.
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Campbell, Kristen M., Haocheng Dai, Zhe Su, Martin Bauer, P. Thomas Fletcher, and Sarang C. Joshi. "Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics." Machine Learning for Biomedical Imaging 1, IPMI 2021 (June 16, 2022): 1–25. http://dx.doi.org/10.59275/j.melba.2022-a871.
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The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitative insights into brain structure, there is controversy over whether they can provide quantitative information, especially at the population level. In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold. We equip this manifold with the Ebin metric, a natural metric structure for this space, to get a Riemannian manifold along with its associated geometric properties. We then use this Riemannian framework to apply object-oriented statistical analysis to define an atlas as the Fréchet mean of a population of Riemannian metrics. This formulation ties into the existing framework for diffeomorphic construction of image atlases, allowing us to construct a multimodal atlas by simultaneously integrating complementary white matter structure details from DWMRI and cortical details from T1-weighted MRI. We illustrate our framework with 2D data examples of connectome registration and atlas formation. Finally, we build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.
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Bu, Xuan, Miao Cao, Xiaoqi Huang, and Yong He. "The structural connectome in ADHD." Psychoradiology 1, no. 4 (December 2021): 257–71. http://dx.doi.org/10.1093/psyrad/kkab021.
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Abstract Attention-deficit/hyperactivity disorder (ADHD) has been conceptualized as a brain dysconnectivity disorder. In the past decade, noninvasive diffusion magnetic resonance imaging (dMRI) studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome, and that these alterations are associated with core symptoms and cognitive deficits in patients. This review aims to summarize recent dMRI-based structural connectome studies in ADHD from voxel-, tractography-, and network-based perspectives. Voxel- and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts, the corpus callosum, the corticospinal tracts, and the cingulum bundle in patients with ADHD. Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes. The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification. These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation. However, given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations, these ADHD-related white matter alterations are still far from informing clinical practice. Future studies with larger and more homogeneous samples are needed to validate the consistency of current results; advanced dMRI techniques can help to generate much more precise estimation of white matter pathways and assure specific fiber configurations; and finally, dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.
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Colon-Perez, Luis M., Jared J. Tanner, Michelle Couret, Shelby Goicochea, Thomas H. Mareci, and Catherine C. Price. "Cognition and connectomes in nondementia idiopathic Parkinson’s disease." Network Neuroscience 2, no. 1 (March 2018): 106–24. http://dx.doi.org/10.1162/netn_a_00027.
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In this study, we investigate the organization of the structural connectome in cognitively well participants with Parkinson’s disease (PD-Well; n = 31) and a subgroup of participants with Parkinson’s disease who have amnestic disturbances (PD-MI; n = 9). We explore correlations between connectome topology and vulnerable cognitive domains in Parkinson’s disease relative to non-Parkinson’s disease peers (control, n = 40). Diffusion-weighted MRI data and deterministic tractography were used to generate connectomes. Connectome topological indices under study included weighted indices of node strength, path length, clustering coefficient, and small-worldness. Relative to controls, node strength was reduced 4.99% for PD-Well ( p = 0.041) and 13.2% for PD-MI ( p = 0.004). We found bilateral differences in the node strength between PD-MI and controls for inferior parietal, caudal middle frontal, posterior cingulate, precentral, and rostral middle frontal. Correlations between connectome and cognitive domains of interest showed that topological indices of global connectivity negatively associated with working memory and displayed more and larger negative correlations with neuropsychological indices of memory in PD-MI than in PD-Well and controls. These findings suggest that indices of network connectivity are reduced in PD-MI relative to PD-Well and control participants.
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Dadario, Nicholas B., Charles Teo, and Michael E. Sughrue. "Insular gliomas and tractographic visualization of the connectome." Neurosurgical Focus: Video 6, no. 1 (January 2022): V4. http://dx.doi.org/10.3171/2021.10.focvid21194.
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In this video, the authors present a connectome-guided surgical resection of an insular glioma in a 39-year-old woman. Preoperative study with constrained spherical deconvolution (CSD)–based tractography revealed the surrounding brain connectome architecture around the tumor relevant for safe surgical resection. Connectomic information provided detailed maps of the surrounding language and salience networks, including eloquent white matter fibers and cortical regions, which were visualized intraoperatively with image guidance and artificial intelligence (AI)–based brain mapping software. Microsurgical dissection is presented with detailed discussion of the safe boundaries and angles of resection when entering the insular operculum defined by connectomic information. The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21194
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Shao, Junming, Qinli Yang, Afra Wohlschläger, and Christian Sorg. "Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining." International Journal of Knowledge Discovery in Bioinformatics 3, no. 1 (January 2012): 23–38. http://dx.doi.org/10.4018/jkdb.2012010102.
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Alzheimer’s disease (AD) is the most common cause of age-related dementia, which prominently affects the human connectome. In this paper, the authors focus on the question how they can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: fiber density and fractional anisotropy, to represent the structural brain connectivity patterns. After frequent subgraph mining, the abnormal score was finally defined to identify disrupted subgraph patterns in patients. Experiments demonstrated that our data-driven approach, for the first time, allows identifying selective spatial pattern changes of the human connectome in AD that perfectly matched grey matter changes of the disease. Their findings also bring new insights into how AD propagates and disrupts the regional integrity of large-scale structural brain networks in a fiber connectivity-based way.
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Harrison, Rebecca A., Rongjie Liu, Vikram Rao, Melissa Petersen, Hannah Dyson, Shiao-Pei S. Weathers, Kristin Alfaro-Munoz, John Frederick De Groot, and Shelli Kesler. "Evaluating the capacity of connectome analysis to predict survival in high-grade astrocytoma." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 2049. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.2049.
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2049 Background: While factors such as age, histology and tumor molecular variants (e.g. IDH status) contribute to prognosis in patients with high grade astrocytoma (HGA), there remains a wide variability in patient survival outcomes. The connectome, or brain network organization, incorporates biologic, molecular and environmental processes providing a uniquely parsimonious summary of key prognostic factors. This study compared the capacity of machine learning (ML) models based on baseline connectomics and clinical variables to predict patient survival in HGA. Methods: Patients with a new diagnosis of HGA and a presurgical 3D, T1-weighted MRI available were retrospectively identified. Individual patient connectomes were derived from MRI with 90 cortical/subcortical features. Presurgical clinical features included age, gender, histology, tumor grade and IDH status. Three ML algorithms were implemented: extreme learning machine with Buckley–James estimator (ELMBJ), random survival forest (RSF) with logrank splitting and RSF with concordance index (CI) splitting. For each algorithm, we used a 60/40 training/testing split with 50 iterations and CI as the performance metric. We tested three models: 1) connectome only, 2) clinical only, and 3) connectome plus clinical variables. Results: Of patients identified (n = 105), 66 had glioblastoma and 39 had anaplastic astrocytoma. Thirty-eight harbored IDH mutation. Median overall survival was 27.43 months (SD 39.57). Connectome-only models showed better prediction performance compared to clinical-only models across all algorithms. ELMBJ showed the best performance (connectome median CI = 0.522, clinical CI = 0.201). Connectome models also performed as well as combined models (e.g. median CI = 0.523 for ELMBJ). Conclusions: This study demonstrates the potential of a connectome model to predict survival of patients with HGA. Replication in a larger sample is required to validate these results and refine ML models including examination of additional clinical features. If successful, use of a simple T1 MRI could provide additional variables to augment existing prognostic prediction, especially in scenarios where tumor genotyping is not available.
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Pike, Ashley, Ashley Pike, Tatiana Wolfe, G. Andrew James, Sienna Colonese, Maegan Calvert, Chrystal Fullen, et al. "511 Functional link between myelination integrity in the connectome of the cingulum bundle and information processing speed in RRMS." Journal of Clinical and Translational Science 9, s1 (March 26, 2025): 149. https://doi.org/10.1017/cts.2024.1092.
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Objectives/Goals: This study tests how fiber microstructural integrity and myelination levels within the cingulum connectome are associated with information processing speed (IPS) in relapsing-remitting multiple sclerosis (RRMS). We investigate the functional impact of structural coherence, myelin content, and white matter hyperintensities (WMH) load on IPS. Methods/Study Population: Data from 63 RRMS and 25 healthy controls (HC) were used. We hypothesize that the structural integrity of the cingulum bundle and its structural network – or connectome – is distinctly associated with IPS function in people with RRMS (vs. HC) due to myelin-related plasticity across the wiring. Using diffusion spectrum imaging and high-resolution tract segmentation, we constructed individualized white matter connectomes. Diffusion quantitative anisotropy (QA) and myelin fractions (MWF) were used to quantify structural coherence and myelination. WMH load was measured with T2-FLAIR imaging. Bayesian–Pearson correlations, mixed-linear, and moderation models explored how fiber-specific QA, MWF, and WMH load relate to IPS function in RRMS, as measured by Symbol Digit Modalities Test (SDMT). Results/Anticipated Results: We theorize that (1) QA in the cingulum connectome correlates with SDMT performance dimensionally, indicating that structural coherence in the white matter supports IPS function among both groups; (2) increased myelination will strengthen the positive association between QA and SDMT scores, suggesting that connectome-specific myelin content facilitates IPS; (3) conversely, WMH load within the cingulum connectome is expected to inversely correlate with SDMT scores, reflecting the detrimental impact of lesion burden on IPS function; (4) myelination in specialized tracts within the cingulum connectome play a compensatory role to support IPS function in the RRMS group. These investigations can offer a mechanistic clue to potential neuroplastic targets for cognitive interventions in MS. Discussion/Significance of Impact: By linking white matter integrity to cognitive function at the connectome level, this study can support neuroregenerative strategies to mitigate cognitive burden in RRMS. Our findings may advance understanding of how structural coherence, tract myelination, and WMH affect IPS, shaping personalized prognostic and therapeutic interventions.
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Szalkai, Balázs, Csaba Kerepesi, Bálint Varga, and Vince Grolmusz. "High-resolution directed human connectomes and the Consensus Connectome Dynamics." PLOS ONE 14, no. 4 (April 16, 2019): e0215473. http://dx.doi.org/10.1371/journal.pone.0215473.
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Garzón, Benjamín, Martin Lövdén, Lieke de Boer, Jan Axelsson, Katrine Riklund, Lars Bäckman, Lars Nyberg, and Marc Guitart-Masip. "Role of dopamine and gray matter density in aging effects and individual differences of functional connectomes." Brain Structure and Function 226, no. 3 (January 9, 2021): 743–58. http://dx.doi.org/10.1007/s00429-020-02205-4.
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AbstractWith increasing age, functional connectomes become dissimilar across normal individuals, reflecting heterogenous aging effects on functional connectivity (FC). We investigated the distribution of these effects across the connectome and their relationship with age-related differences in dopamine (DA) D1 receptor availability and gray matter density (GMD). With this aim, we determined aging effects on mean and interindividual variance of FC using fMRI in 30 younger and 30 older healthy subjects and mapped the contribution of each connection to the patterns of age-related similarity loss. Aging effects on mean FC accounted mainly for the dissimilarity between connectomes of younger and older adults, and were related, across brain regions, to aging effects on DA D1 receptor availability. Aging effects on the variance of FC indicated a global increase in variance with advancing age, explained connectome dissimilarity among older subjects and were related to aging effects on variance of GMD. The relationship between aging and the similarity of connectomes can thus be partly explained by age differences in DA modulation and gray matter structure.
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Galanin, I. V., A. G. Naryshkin, Т. А. Skoromets, А. М. Sarkisian, and I. А. Orlov. "CONNECTOME AND MICROGRAVITY." Aerospace and Environmental Medicine 58, no. 4 (2024): 5–14. http://dx.doi.org/10.21687/0233-528x-2024-58-4-5-14.
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The paper surveys the current views on formation, development and activity of the connectome under the influence of gravity. The neuroglia role and in formation and functioning of both the normal and disordered brain is considered. Connectome significance for the brain functioning is discussed. Analyzed is an input of the vestibular system, the key gravity-sensitive organ, in connectome formation and functioning. Changes in the connectome due to microgravity are described.
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Bergman, Jerry. "Seung, Sebastian. Connectome." Journal of Interdisciplinary Studies 28, no. 1 (2016): 193–94. http://dx.doi.org/10.5840/jis2016281/220.
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Vázquez-Reina, Amelio, Won-Ki Jeong, Jeff Lichtman, and Hanspeter Pfister. "The connectome project." XRDS: Crossroads, The ACM Magazine for Students 18, no. 1 (September 2011): 8–13. http://dx.doi.org/10.1145/2000775.2000782.
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Narr, Katherine L., and Amber M. Leaver. "Connectome and schizophrenia." Current Opinion in Psychiatry 28, no. 3 (May 2015): 229–35. http://dx.doi.org/10.1097/yco.0000000000000157.
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Dick, Anthony Steven, Byron Bernal, and Pascale Tremblay. "The Language Connectome." Neuroscientist 20, no. 5 (December 15, 2013): 453–67. http://dx.doi.org/10.1177/1073858413513502.
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Jbabdi, Saad, Stamatios N. Sotiropoulos, and Timothy E. Behrens. "The topographic connectome." Current Opinion in Neurobiology 23, no. 2 (April 2013): 207–15. http://dx.doi.org/10.1016/j.conb.2012.12.004.
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Rumpel, Simon, and Jochen Triesch. "The dynamic connectome." e-Neuroforum 7, no. 3 (August 16, 2016): 48–53. http://dx.doi.org/10.1007/s13295-016-0026-2.
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Zador, Anthony M., Joshua Dubnau, Hassana K. Oyibo, Huiqing Zhan, Gang Cao, and Ian D. Peikon. "Sequencing the Connectome." PLoS Biology 10, no. 10 (October 23, 2012): e1001411. http://dx.doi.org/10.1371/journal.pbio.1001411.
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Lichtman, Jeff. "Imaging the Connectome." Biophysical Journal 108, no. 2 (January 2015): 23a. http://dx.doi.org/10.1016/j.bpj.2014.11.148.
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Rajapandian, Meenusree, Enrico Amico, Kausar Abbas, Mario Ventresca, and Joaquín Goñi. "Uncovering differential identifiability in network properties of human brain functional connectomes." Network Neuroscience 4, no. 3 (January 2020): 698–713. http://dx.doi.org/10.1162/netn_a_00140.
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The identifiability framework (𝕀 f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀 f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀 f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀 f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.
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Chen, Yuhan, Qixiang Lin, Xuhong Liao, Changsong Zhou, and Yong He. "Association of aerobic glycolysis with the structural connectome reveals a benefit–risk balancing mechanism in the human brain." Proceedings of the National Academy of Sciences 118, no. 1 (December 21, 2020): e2013232118. http://dx.doi.org/10.1073/pnas.2013232118.
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Aerobic glycolysis (AG), that is, the nonoxidative metabolism of glucose, contributes significantly to anabolic pathways, rapid energy generation, task-induced activity, and neuroprotection; yet high AG is also associated with pathological hallmarks such as amyloid-β deposition. An important yet unresolved question is whether and how the metabolic benefits and risks of brain AG is structurally shaped by connectome wiring. Using positron emission tomography and magnetic resonance imaging techniques as well as computational models, we investigate the relationship between brain AG and the macroscopic connectome. Specifically, we propose a weighted regional distance-dependent model to estimate the total axonal projection length of a brain node. This model has been validated in a macaque connectome derived from tract-tracing data and shows a high correspondence between experimental and estimated axonal lengths. When applying this model to the human connectome, we find significant associations between the estimated total axonal projection length and AG across brain nodes, with higher levels primarily located in the default-mode and prefrontal regions. Moreover, brain AG significantly mediates the relationship between the structural and functional connectomes. Using a wiring optimization model, we find that the estimated total axonal projection length in these high-AG regions exhibits a high extent of wiring optimization. If these high-AG regions are randomly rewired, their total axonal length and vulnerability risk would substantially increase. Together, our results suggest that high-AG regions have expensive but still optimized wiring cost to fulfill metabolic requirements and simultaneously reduce vulnerability risk, thus revealing a benefit–risk balancing mechanism in the human brain.
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Sreenivasan, Varsha, Sawan Kumar, Franco Pestilli, Partha Talukdar, and Devarajan Sridharan. "GPU-accelerated connectome discovery at scale." Nature Computational Science 2, no. 5 (May 2022): 298–306. http://dx.doi.org/10.1038/s43588-022-00250-z.
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AbstractDiffusion magnetic resonance imaging and tractography enable the estimation of anatomical connectivity in the human brain, in vivo. Yet, without ground-truth validation, different tractography algorithms can yield widely varying connectivity estimates. Although streamline pruning techniques mitigate this challenge, slow compute times preclude their use in big-data applications. We present ‘Regularized, Accelerated, Linear Fascicle Evaluation’ (ReAl-LiFE), a GPU-based implementation of a state-of-the-art streamline pruning algorithm (LiFE), which achieves >100× speedups over previous CPU-based implementations. Leveraging these speedups, we overcome key limitations with LiFE’s algorithm to generate sparser and more accurate connectomes. We showcase ReAl-LiFE’s ability to estimate connections with superlative test–retest reliability, while outperforming competing approaches. Moreover, we predicted inter-individual variations in multiple cognitive scores with ReAl-LiFE connectome features. We propose ReAl-LiFE as a timely tool, surpassing the state of the art, for accurate discovery of individualized brain connectomes at scale. Finally, our GPU-accelerated implementation of a popular non-negative least-squares optimization algorithm is widely applicable to many real-world problems.
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Baek, Hyeon-Man. "Diffusion Measures of Subcortical Structures Using High-Field MRI." Brain Sciences 13, no. 3 (February 24, 2023): 391. http://dx.doi.org/10.3390/brainsci13030391.
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The pathology of Parkinson’s disease (PD) involves the death of dopaminergic neurons in the substantia nigra (SN), which slowly influences downstream basal ganglia pathways as dopamine transport diminishes. Diffusion magnetic resonance imaging (MRI) has been used to diagnose PD by assessing white matter connectivity in some brain areas. For this study, we applied Lead-DBS to human connectome project data to automatically segment 11 subcortical structures of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. The Lead-connectome pipeline, which utilizes DSI Studio to generate structural connectomes from each 3T and 7T diffusion image, was applied to 3T and 7T data to investigate possible differences in diffusion measures due to different acquisition protocols. Significantly higher fractional anisotropy (FA) values were found in the 3T left SN; significantly higher MD values were found in the 3T left SN and the right amygdala, SN, and subthalamic nucleus (STN); significantly higher AD values were found in the right RN and STN; and significantly higher RD values were found in the left RN and right amygdala. We illustrate a methodology for obtaining diffusion measures of basal ganglia and basal ganglia connectivity using diffusion images, as well as show possible differences in diffusion measures that can arise due to the differences in MRI acquisitions.
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Poologaindran, Anujan, Mike Hart, Tom Santarius, Stephen Price, Rohit Sinha, Mike Sughrue, Yaara Erez, Rafael Romero-Garcia, and John Suckling. "Longitudinal Connectome Analyses following Low-Grade Glioma Neurosurgery: Implications for Cognitive Rehabilitation." Neuro-Oncology 23, Supplement_4 (October 1, 2021): iv8. http://dx.doi.org/10.1093/neuonc/noab195.015.
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Abstract Aims Low-grade gliomas (LGG) slowly grow and infiltrate the brain's network architecture (the connectome). Unlike strokes that acutely damage the connectome, LGGs intricately remodel it, leading to varying deficits in executive function (i.e. attention, concentration, working memory). By longitudinally mapping the “mesoscale” architecture of the connectome, we may begin to systematically accelerate domain-general cognitive rehabilitation in LGG patients. In this study, we pursued the following aims: 1) track cognitive and connectome trajectories following LGG surgery, 2) determine optimal time period for cognitive rehabilitation, and 3) distinguish patients with perioperative predictors of long-term cognitive deficits (>1 year). Method With MRI and cognitive data from n=629 individuals across the lifespan, we first validated the structural, functional, and topological relevance of the multiple demand (MD) system for higher-order cognition. Next, in n=17 patients undergoing glioma surgery, we longitudinally acquired connectome and cognitive data: pre-surgery, post-surgery Day 1, Month 3, & 12. We assessed how glioma infiltration, surgery, and rehabilitation affected MD system trajectories at the single-subject level. Deploying transcriptomic and graph theoretical analyses, we tested if perioperative connectome modularity can accurately distinguish long-term cognitive trajectories. Results Controlling for age and sex, the MD system’s multi-scale architecture in health was positively associated with higher-order cognition (Catell’s fluid intelligence). Pre-operative glioma infiltration into the MD system was negatively associated with the number of long-term cognitive deficits (OCS-Bridge cognitive battery), suggesting its functional reorganisation. Mixed-effects modelling demonstrated the resilience of the MD system to infiltration and resection, while the early post-operative period was critical for effective neurorehabilitation. Graph analyses revealed perioperative modularity can distinguish patients with long-term cognitive deficits at one-year follow-up. Transcriptomic analyses of inter-module connector hubs revealed increased gene expression for mitochondrial metabolism and synaptic plasticity. Conclusion This is the first serial functional mapping of LGG patient trajectories for domain-general cognition. By assessing the mesoscale architecture, we demonstrate how connectomics can help overcome the intrinsic heterogeneity in LGG patients and predict long-term rehabilitation trajectories. We discuss how to identify neurobiologically-grounded personalised targets for 'interventional neurorehabilitation' following LGG surgery.
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Melozzi, Francesca, Eyal Bergmann, Julie A. Harris, Itamar Kahn, Viktor Jirsa, and Christophe Bernard. "Individual structural features constrain the mouse functional connectome." Proceedings of the National Academy of Sciences 116, no. 52 (December 11, 2019): 26961–69. http://dx.doi.org/10.1073/pnas.1906694116.
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Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure–function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.
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Moron-Fernández, María José, Ludovica Maria Amedeo, Alberto Monterroso Muñoz, Helena Molina-Abril, Fernando Díaz-del-Río, Fabiano Bini, Franco Marinozzi, and Pedro Real. "Analysis of Connectome Graphs Based on Boundary Scale." Sensors 23, no. 20 (October 20, 2023): 8607. http://dx.doi.org/10.3390/s23208607.
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The purpose of this work is to advance in the computational study of connectome graphs from a topological point of view. Specifically, starting from a sequence of hypergraphs associated to a brain graph (obtained using the Boundary Scale model, BS2), we analyze the resulting scale-space representation using classical topological features, such as Betti numbers and average node and edge degrees. In this way, the topological information that can be extracted from the original graph is substantially enriched, thus providing an insightful description of the graph from a clinical perspective. To assess the qualitative and quantitative topological information gain of the BS2 model, we carried out an empirical analysis of neuroimaging data using a dataset that contains the connectomes of 96 healthy subjects, 52 women and 44 men, generated from MRI scans in the Human Connectome Project. The results obtained shed light on the differences between these two classes of subjects in terms of neural connectivity.
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Duong-Tran, Duy, Nghi Nguyen, Shizhuo Mu, Jiong Chen, Jingxuan Bao, Frederick H. Xu, Sumita Garai, et al. "A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits." Mathematics 12, no. 19 (September 24, 2024): 2967. http://dx.doi.org/10.3390/math12192967.
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In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects’ functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations.
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Pospisil, Dean A., Max J. Aragon, Sven Dorkenwald, Arie Matsliah, Amy R. Sterling, Philipp Schlegel, Szi-chieh Yu, et al. "The fly connectome reveals a path to the effectome." Nature 634, no. 8032 (October 2, 2024): 201–9. http://dx.doi.org/10.1038/s41586-024-07982-0.
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AbstractA goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1–3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the ‘effectome’. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons—thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.
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Lobov, Sergey A., Ekaterina S. Berdnikova, Alexey I. Zharinov, Dmitry P. Kurganov, and Victor B. Kazantsev. "STDP-Driven Rewiring in Spiking Neural Networks under Stimulus-Induced and Spontaneous Activity." Biomimetics 8, no. 3 (July 20, 2023): 320. http://dx.doi.org/10.3390/biomimetics8030320.
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Mathematical and computer simulation of learning in living neural networks have typically focused on changes in the efficiency of synaptic connections represented by synaptic weights in the models. Synaptic plasticity is believed to be the cellular basis for learning and memory. In spiking neural networks composed of dynamical spiking units, a biologically relevant learning rule is based on the so-called spike-timing-dependent plasticity or STDP. However, experimental data suggest that synaptic plasticity is only a part of brain circuit plasticity, which also includes homeostatic and structural plasticity. A model of structural plasticity proposed in this study is based on the activity-dependent appearance and disappearance of synaptic connections. The results of the research indicate that such adaptive rewiring enables the consolidation of the effects of STDP in response to a local external stimulation of a neural network. Subsequently, a vector field approach is used to demonstrate the successive “recording” of spike paths in both functional connectome and synaptic connectome, and finally in the anatomical connectome of the network. Moreover, the findings suggest that the adaptive rewiring could stabilize network dynamics over time in the context of activity patterns’ reproducibility. A universal measure of such reproducibility introduced in this article is based on similarity between time-consequent patterns of the special vector fields characterizing both functional and anatomical connectomes.
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Binding, Lawrence, Peter Taylor, Sallie Baxendale, Andrew McEvoy, Anna Miserocchi, John Duncan, and Sjoerd Vos. "Network changes predicting language decline following anterior temporal lobe resection." Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (August 12, 2022): e2.178. http://dx.doi.org/10.1136/jnnp-2022-abn2.32.
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Anterior temporal lobe resection (ATLR), while successful can result in lasting impairment of language function. White matter bundles have been shown to explain some of the variance seen in language decline after ATLR. Network analysis of the structural connectome has been shown superior in predicting preoperative language ability but remains unexplored in predicting postoperative ability.Diffusion MRI-based tractography was used to generate the preoperative connectome on 54 left-lat- eralised (as determined by functional MRI), left-hemisphere ATLR. Postoperative connectomes were estimated via manually drawn resection masks. Graded naming test (GNT), semantic, and letter fluency were binarised into significant decline or not (via their reliable change indices). Strength (sum of connec- tions) and betweenness centrality (interconnectivity) network changes were generated using pre- and postoperative connectomes as predictor variables. Each model was entered into a linear support vector machine incorporating synthetic minority over-sampling technique for class imbalances.Strength changes alone accurately predicted 81.6% of patients who had GNT decline. Betweenness centrality changes accurately predicted 73.3% of patients who had letter fluency decline. Patients with semantic decline were predicted equally as well by strength and betweenness centrality changes (accuracy=71.1%).These findings demonstrate the usefulness of the structural network in predicting and potentially prevent- ing postoperative language decline.
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Xu, Zhilei, Mingrui Xia, Xindi Wang, Xuhong Liao, Tengda Zhao, and Yong He. "Meta-connectomic analysis maps consistent, reproducible, and transcriptionally relevant functional connectome hubs in the human brain." Communications Biology 5, no. 1 (October 4, 2022). http://dx.doi.org/10.1038/s42003-022-04028-x.
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AbstractHuman brain connectomes include sets of densely connected hub regions. However, the consistency and reproducibility of functional connectome hubs have not been established to date and the genetic signatures underlying robust hubs remain unknown. Here, we conduct a worldwide harmonized meta-connectomic analysis by pooling resting-state functional MRI data of 5212 healthy young adults across 61 independent cohorts. We identify highly consistent and reproducible connectome hubs in heteromodal and unimodal regions both across cohorts and across individuals, with the greatest effects observed in lateral parietal cortex. These hubs show heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using post-mortem transcriptome datasets, we show that as compared to non-hubs, connectome hubs have a spatiotemporally distinctive transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and metabolic processes. These results highlight the robustness of macroscopic connectome hubs and their potential cellular and molecular underpinnings, which markedly furthers our understanding of how connectome hubs emerge in development, support complex cognition in health, and are involved in disease.
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Li, Zhensheng, Jie Ma, Hongmin Bai, Bingmei Deng, Jian Lin, and Weimin Wang. "Brain local structural connectomes and the subtypes of the medial temporal lobe parcellations." Frontiers in Neuroscience 19 (February 12, 2025). https://doi.org/10.3389/fnins.2025.1529123.
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ObjectiveTo investigate the quantitative characteristics and major subtypes of local structural connectomes for medial temporal lobe (MTL) parcellations.MethodsThe Q-Space Diffeomorphic Reconstruction (QSDR) method was used to track white matter fibers for the ROIs within MTL based on the integrating high-resolution T1 structural MR imaging and diffusion MR imaging of 100 adult Chinese individuals. Graph theoretical analysis was employed to construct the local structural connectome models for ROIs within MTL and acquire the network parameters. These connectivity matrices of these connectomes were classified into major subtypes undergoing hierarchical clustering.Results(1) In the local brain connectomes, the overall network features exhibited a low characteristic path length paired with moderate to high global efficiency, suggesting the effectiveness of the local brain connectome construction. The amygdala connectomes exhibited longer characteristic path length and weaker global efficiency than the ipsilateral hippocampus and parahippocampal connectomes. (2) The hubs of the amygdala connectomes were dispersed across the ventral frontal, olfactory area, limbic, parietal regions and subcortical nuclei, and the hubs the hippocampal connectomes were mainly situated within the limbic, parietal, and subcortical regions. The hubs distribution of the parahippocampal connectomes resembled the hippocampal structural connectomes, but lacking interhemispheric connections and connectivity with subcortical nuclei. (3) The subtypes of the brain local structural connectomes for each ROI were classified by hierarchical clustering, The subtypes of the bilateral amygdala connectomes were the amygdala-prefrontal connectome; the amygdala-ipsilateral or contralateral limbic connectome and the amygdala-posterior connectome. The subtypes of the bilateral hippocampal connectomes primarily included the hippocampus-ipsilateral or contralateral limbic connectome and the anterior temporal-hippocampus-ventral temporal-occipital connectome in the domain hemisphere. The subtypes of the parahippocampal connectomes exhibited resemblances to those of the hippocampus.ConclusionWe have constructed the brain local connectomes of the MTL parcellations and acquired the network parameters to delineate the hubs distribution through graph theory analysis. The connectomes can be classified into different major subtypes, which were closely related to the functional connectivity.
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Onuchin, Arsenii A., Alina V. Chernizova, Mikhail A. Lebedev, and Kirill E. Polovnikov. "Communities in C. elegans connectome through the prism of non-backtracking walks." Scientific Reports 13, no. 1 (December 21, 2023). http://dx.doi.org/10.1038/s41598-023-49503-5.
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AbstractThe fundamental relationship between the mesoscopic structure of neuronal circuits and organismic functions they subserve is one of the major challenges in contemporary neuroscience. Formation of structurally connected modules of neurons enacts the conversion from single-cell firing to large-scale behaviour of an organism, highlighting the importance of their accurate profiling in the data. While connectomes are typically characterized by significant sparsity of neuronal connections, recent advances in network theory and machine learning have revealed fundamental limitations of traditionally used community detection approaches in cases where the network is sparse. Here we studied the optimal community structure in the structural connectome of Caenorhabditis elegans, for which we exploited a non-conventional approach that is based on non-backtracking random walks, virtually eliminating the sparsity issue. In full agreement with the previous asymptotic results, we demonstrated that non-backtracking walks resolve the ground truth annotation into clusters on stochastic block models (SBM) with the size and density of the connectome better than the spectral methods related to simple random walks. Based on the cluster detectability threshold, we determined that the optimal number of modules in a recently mapped connectome of C. elegans is 10, which precisely corresponds to the number of isolated eigenvalues in the spectrum of the non-backtracking flow matrix. The discovered communities have a clear interpretation in terms of their functional role, which allows one to discern three structural compartments in the worm: the Worm Brain (WB), the Worm Movement Controller (WMC), and the Worm Information Flow Connector (WIFC). Broadly, our work provides a robust network-based framework to reveal mesoscopic structures in sparse connectomic datasets, paving way to further investigation of connectome mechanisms for different functions.
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Busch, Erica L., Kristina M. Rapuano, Kevin M. Anderson, Monica D. Rosenberg, Richard Watts, BJ Casey, James V. Haxby, and Ma Feilong. "Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development." Journal of Neuroscience, December 26, 2023, JN—RM—0735–23. http://dx.doi.org/10.1523/jneurosci.0735-23.2023.
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The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer–scale, vertex–wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine-scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9–10 years;n= 1,115 individuals, both sexes, 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex–wise) connectivity information and compare the fine-scale connectome with the traditional parcel–wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.Significance StatementYears of human magnetic resonance imaging (MRI) research demonstrate that individual variability in resting-state functional connectivity relates to genetics and cognition. However, the various spatial scales where individual differences in connectivity could occur have yet to be considered in childhood brain–behavior association studies. Here, we use novel machine learning approaches to examine the reliability, heritability, and behavioral relevance of different spatial scales of the resting-state functional connectome during childhood. We show that broad features of the connectome are strongly related to heritability, whereas fine details are more reliable and strongly associated with neurocognitive performance. These data indicate that reliable, heritable, and behaviorally–relevant individual differences exist at dissociable scales of the functional connectome.
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Pokorny, Christoph, Omar Awile, James B. Isbister, Kerem Kurban, Matthias Wolf, and Michael W. Reimann. "A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations." Network Neuroscience, December 2, 2024, 1–51. https://doi.org/10.1162/netn_a_00429.
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Abstract Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which non-random trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in SONATA format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in a detailed model of rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.
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Jun, Suhnyoung, Stephen M. Malone, Thomas H. Alderson, Jeremy Harper, Ruskin H. Hunt, Kathleen M. Thomas, Sylia Wilson, William G. Iacono, and Sepideh Sadaghiani. "Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states." Network Neuroscience, May 28, 2024, 1–35. http://dx.doi.org/10.1162/netn_a_00390.
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Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (> 1 Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N = 926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals’ cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.
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Kaiser, Marcus. "Connectomes: from a sparsity of networks to large-scale databases." Frontiers in Neuroinformatics 17 (June 12, 2023). http://dx.doi.org/10.3389/fninf.2023.1170337.
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The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity in a single human subject. Thanks to non-invasive methods such as diffusion imaging, we now know about connectivity in many species and, for some species, in many individuals. To illustrate the rapid change in availability of connectome data, the UK Biobank is on track to record structural and functional connectivity in 100,000 human subjects. Moreover, connectome data from a range of species is now available: from Caenorhabditis elegans and the fruit fly to pigeons, rodents, cats, non-human primates, and humans. This review will give a brief overview of what structural connectivity data is now available, how connectomes are organized, and how their organization shows common features across species. Finally, I will outline some of the current challenges and potential future work in making use of connectome information.
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Yeh, Fang-Cheng. "Population-based tract-to-region connectome of the human brain and its hierarchical topology." Nature Communications 13, no. 1 (August 22, 2022). http://dx.doi.org/10.1038/s41467-022-32595-4.
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AbstractConnectome maps region-to-region connectivities but does not inform which white matter pathways form the connections. Here we constructed a population-based tract-to-region connectome to fill this information gap. The constructed connectome quantifies the population probability of a white matter tract innervating a cortical region. The results show that ~85% of the tract-to-region connectome entries are consistent across individuals, whereas the remaining (~15%) have substantial individual differences requiring individualized mapping. Further hierarchical clustering on cortical regions revealed dorsal, ventral, and limbic networks based on the tract-to-region connective patterns. The clustering results on white matter bundles revealed the categorization of fiber bundle systems in the association pathways. This tract-to-region connectome provides insights into the connective topology between cortical regions and white matter bundles. The derived hierarchical relation further offers a categorization of gray and white matter structures.