Mapping brain–behavior networks using functional and structural connectome fingerprinting in the HCP dataset

Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets.

[1]  R. Gur,et al.  A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation , 2010, Journal of Neuroscience Methods.

[2]  Dustin Scheinost,et al.  Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms , 2011, Neuroinformatics.

[3]  Alejandro Ribeiro,et al.  A Graph Signal Processing Perspective on Functional Brain Imaging , 2018, Proceedings of the IEEE.

[4]  Alberto Bizzi,et al.  Aphasia induced by gliomas growing in the ventrolateral frontal region: Assessment with diffusion MR tractography, functional MR imaging and neuropsychology , 2012, Cortex.

[5]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[6]  L. Green,et al.  Area under the curve as a measure of discounting. , 2001, Journal of the experimental analysis of behavior.

[7]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[8]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[9]  Xavier Bresson,et al.  Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems , 2017, NeuroImage.

[10]  Timothy E. J. Behrens,et al.  Measuring macroscopic brain connections in vivo , 2015, Nature Neuroscience.

[11]  Richard F. Betzel,et al.  Structure–function relationships during segregated and integrated network states of human brain functional connectivity , 2018, Brain Structure and Function.

[12]  L. Cammoun,et al.  The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI , 2012, PloS one.

[13]  Scott T. Grafton,et al.  Structural foundations of resting-state and task-based functional connectivity in the human brain , 2013, Proceedings of the National Academy of Sciences.

[14]  Johann Daniel Kruschwitz,et al.  Evaluating the replicability, specificity, and generalizability of connectome fingerprints , 2017, NeuroImage.

[15]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[16]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[17]  Stamatios N. Sotiropoulos,et al.  Mapping Connections in Humans and Non-Human Primates , 2014 .

[18]  O. Sporns,et al.  From regions to connections and networks: new bridges between brain and behavior , 2016, Current Opinion in Neurobiology.

[19]  S. Weintraub,et al.  NIH Toolbox Cognition Battery (CB): Validation of Executive Function Measures in Adults , 2014, Journal of the International Neuropsychological Society.

[20]  Dustin Scheinost,et al.  Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets , 2018, NeuroImage.

[21]  Maxime Descoteaux,et al.  Recognition of white matter bundles using local and global streamline-based registration and clustering , 2017, NeuroImage.

[22]  Xenophon Papademetris,et al.  Groupwise whole-brain parcellation from resting-state fMRI data for network node identification , 2013, NeuroImage.

[23]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[24]  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.

[25]  Danielle S. Bassett,et al.  Structurally-Constrained Relationships between Cognitive States in the Human Brain , 2014, PLoS Comput. Biol..

[26]  Fang-Cheng Yeh,et al.  Generalized ${ q}$-Sampling Imaging , 2010, IEEE Transactions on Medical Imaging.

[27]  Ricardo Otazo,et al.  Low Rank plus Sparse decomposition of ODFs for improved detection of group-level differences and variable correlations in white matter , 2018, NeuroImage.

[28]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[29]  R. Gur,et al.  Development of Abbreviated Nine-Item Forms of the Raven’s Standard Progressive Matrices Test , 2012, Assessment.

[30]  Jean M. Vettel,et al.  Structural Pathways Supporting Swift Acquisition of New Visuomotor Skills , 2016, Cerebral cortex.

[31]  Olaf Sporns,et al.  Network-Level Structure-Function Relationships in Human Neocortex , 2016, Cerebral cortex.

[32]  J. Zimmermann,et al.  Subject specificity of the correlation between large-scale structural and functional connectivity , 2018, Network Neuroscience.

[33]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[34]  Jennifer M. D. Yoon,et al.  Functionally Defined White Matter Reveals Segregated Pathways in Human Ventral Temporal Cortex Associated with Category-Specific Processing , 2015, Neuron.

[35]  Richard C. Gershon,et al.  Language Measures of the NIH Toolbox Cognition Battery , 2014, Journal of the International Neuropsychological Society.

[36]  Patric Hagmann,et al.  Mapping the human connectome at multiple scales with diffusion spectrum MRI , 2012, Journal of Neuroscience Methods.

[37]  Peter F. Neher,et al.  The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.

[38]  Dustin Scheinost,et al.  The impact of image smoothness on intrinsic functional connectivity and head motion confounds , 2014, NeuroImage.

[39]  Fang-Cheng Yeh,et al.  Local connectome phenotypes predict social, health, and cognitive factors , 2017, bioRxiv.

[40]  Fang-Cheng Yeh,et al.  Connectometry: A statistical approach harnessing the analytical potential of the local connectome , 2016, NeuroImage.

[41]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[42]  Chris Rorden,et al.  Mapping Language Networks Using the Structural and Dynamic Brain Connectomes , 2017, eNeuro.

[43]  B. Mazoyer,et al.  AICHA: An atlas of intrinsic connectivity of homotopic areas , 2015, Journal of Neuroscience Methods.

[44]  Timothy D. Verstynen,et al.  Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy , 2013, PloS one.

[45]  Heidi Johansen-Berg,et al.  Diffusion MRI at 25: Exploring brain tissue structure and function , 2012, NeuroImage.

[46]  Dustin Scheinost,et al.  Ten simple rules for predictive modeling of individual differences in neuroimaging , 2019, NeuroImage.

[47]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[48]  Jean-Philippe Thiran,et al.  Quantitative Analysis of Myelin and Axonal Remodeling in the Uninjured Motor Network After Stroke , 2015, Brain Connect..

[49]  Dustin Scheinost,et al.  Using connectome-based predictive modeling to predict individual behavior from brain connectivity , 2017, Nature Protocols.

[50]  D. Tulsky,et al.  The NIH Toolbox Pattern Comparison Processing Speed Test: Normative Data. , 2015, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[51]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[52]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[53]  Julien Cohen-Adad,et al.  Improving diffusion MRI using simultaneous multi-slice echo planar imaging , 2012, NeuroImage.

[54]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[55]  G. Finnerty,et al.  Rewiring the connectome: Evidence and effects , 2018, Neuroscience & Biobehavioral Reviews.

[56]  O. Sporns Structure and function of complex brain networks , 2013, Dialogues in clinical neuroscience.

[57]  Maxime Chamberland,et al.  3D interactive tractography-informed resting-state fMRI connectivity , 2015, Front. Neurosci..

[58]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[59]  David C. Van Essen,et al.  The future of the human connectome , 2012, NeuroImage.

[60]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .