Optimization of graph construction can significantly increase the power of structural brain network studies
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[1] Ricardo Bruña,et al. How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters , 2018, Front. Neurosci..
[2] Morten L. Kringelbach,et al. Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data , 2017, NeuroImage.
[3] Derek K. Jones,et al. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph , 2017, Front. Neurosci..
[4] J. Helpern,et al. Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.
[5] Nitish Thakor,et al. Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions , 2014, Annals of Biomedical Engineering.
[6] Alan Connelly,et al. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome , 2015, NeuroImage.
[7] C. J. Honeya,et al. Predicting human resting-state functional connectivity from structural connectivity , 2009 .
[8] O. Sporns,et al. Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.
[9] M. V. D. Heuvel,et al. Brain Networks in Schizophrenia , 2014, Neuropsychology Review.
[10] Timothy P. L. Roberts,et al. Test-Retest Reliability of Computational Network Measurements Derived from the Structural Connectome of the Human Brain , 2013, Brain Connect..
[11] Mark W. Woolrich,et al. Discovering dynamic brain networks from big data in rest and task , 2017, NeuroImage.
[12] Derek K. Jones,et al. The structural connectome in traumatic brain injury: A meta-analysis of graph metrics , 2019, Neuroscience & Biobehavioral Reviews.
[13] M. Gazzaniga,et al. Understanding complexity in the human brain , 2011, Trends in Cognitive Sciences.
[14] Wilfried Philips,et al. Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks , 2019, Medical Image Anal..
[15] Chun-Hung Yeh,et al. Evaluation of the accuracy and angular resolution of q-ball imaging , 2008, NeuroImage.
[16] Yong He,et al. Convergence and divergence across construction methods for human brain white matter networks: An assessment based on individual differences , 2015, Human brain mapping.
[17] Stavros I. Dimitriadis. Complexity of Brain Activity and Connectivity in Functional Neuroimaging , 2018 .
[18] L. Passamonti,et al. Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging , 2016, Human brain mapping.
[19] Mark Jenkinson,et al. The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.
[20] Derek K. Jones,et al. Dynamics of the Human Structural Connectome Underlying Working Memory Training , 2016, The Journal of Neuroscience.
[21] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[22] Stephen M. Smith,et al. Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.
[23] Mark E. Bastin,et al. Test–retest reliability of structural brain networks from diffusion MRI , 2014, NeuroImage.
[24] J. Mumford. A power calculation guide for fMRI studies. , 2012, Social cognitive and affective neuroscience.
[25] Steen Moeller,et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.
[26] Alan Connelly,et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.
[27] Jean-Philippe Thiran,et al. Structural connectomics in brain diseases , 2013, NeuroImage.
[28] N. Makris,et al. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.
[29] David K. Hammond,et al. Graph diffusion distance: A difference measure for weighted graphs based on the graph Laplacian exponential kernel , 2013, 2013 IEEE Global Conference on Signal and Information Processing.
[30] Derek K. Jones,et al. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging , 2013, Human brain mapping.
[31] Alexandre Hyafil,et al. Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions , 2015, Trends in Neurosciences.
[32] Marcus Kaiser,et al. This Work Is Licensed under a Creative Commons Attribution 4.0 International License Structural Connectivity Changes in Temporal Lobe Epilepsy: Spatial Features Contribute More than Topological Measures , 2022 .
[33] J. Polimeni,et al. Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty , 2012, Magnetic resonance in medicine.
[34] Chun-Hung Yeh,et al. Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data , 2008, NeuroImage.
[35] Anirban Dutt,et al. Schizophrenia‐like topological changes in the structural connectome of individuals with subclinical psychotic experiences , 2015, Human brain mapping.
[36] R. Ophoff,et al. Brain network analysis reveals affected connectome structure in bipolar I disorder , 2016, Human brain mapping.
[37] O. Sporns,et al. Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.
[38] Dorothee Auer,et al. Reproducibility of Graph-Theoretic Brain Network Metrics: A Systematic Review , 2014, Brain Connect..
[39] Paul M. Thompson,et al. Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity , 2012, MICCAI.
[40] Matthew J. Brookes,et al. How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes , 2019, NeuroImage.
[41] A. Leemans,et al. Hemispheric lateralization of topological organization in structural brain networks , 2014, Human brain mapping.
[42] Steen Moeller,et al. Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.
[43] Stavros I. Dimitriadis,et al. Mining Time-Resolved Functional Brain Graphs to an EEG-Based Chronnectomic Brain Aged Index (CBAI) , 2017, Front. Hum. Neurosci..
[44] Jan Sijbers,et al. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data , 2009 .
[45] Daniel Rueckert,et al. Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.
[46] Edward T. Bullmore,et al. Comparison of large-scale human brain functional and anatomical networks in schizophrenia , 2017, NeuroImage: Clinical.
[47] Ben D. Fulcher,et al. Developmental Changes in Brain Network Hub Connectivity in Late Adolescence , 2015, The Journal of Neuroscience.
[48] Ioannis Tarnanas,et al. Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs) , 2017, Front. Neuroinform..
[49] Krish D. Singh,et al. Assessment and elimination of the effects of head movement on MEG resting-state measures of oscillatory brain activity , 2017, NeuroImage.
[50] Timothy Edward John Behrens,et al. Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE , 2013, Magnetic resonance in medicine.
[51] J. Helpern,et al. MRI quantification of non‐Gaussian water diffusion by kurtosis analysis , 2010, NMR in biomedicine.
[52] N. Tzourio-Mazoyer,et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.
[53] Weihong Yuan,et al. Structural connectivity abnormality in children with acute mild traumatic brain injury using graph theoretical analysis , 2015, Human brain mapping.
[54] Wim Fias,et al. Brain networks under attack: robustness properties and the impact of lesions. , 2016, Brain : a journal of neurology.
[55] Armin Scheurich,et al. Association of Structural Global Brain Network Properties with Intelligence in Normal Aging , 2014, PloS one.
[56] Colm G. Connolly,et al. Test-Retest Reliability of Graph Theoretic Metrics in Adolescent Brains , 2019, Brain Connect..
[57] Darren Price,et al. Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.
[58] B. Ardekani,et al. Estimation of tensors and tensor‐derived measures in diffusional kurtosis imaging , 2011, Magnetic resonance in medicine.
[59] Michael W. Cole,et al. From connectome to cognition: The search for mechanism in human functional brain networks , 2017, NeuroImage.
[60] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[61] Jack M. Fletcher,et al. Data-driven Topological Filtering based on Orthogonal Minimal Spanning Trees: Application to Multi-Group MEG Resting-State Connectivity , 2017, bioRxiv.