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Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex [article]

Salim Arslan
2018 arXiv   pre-print
Third, we present a multi-layer graphical model that combines within- and between-subject connectivity, which is then decomposed into a cortical parcellation that can represent the whole population, while  ...  In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information.  ...  Acknowledgements I would like to first of all thank my supervisor Daniel Rueckert for giving me the opportunity  ... 
arXiv:1802.06772v1 fatcat:zeu6okppcnbhjakwyoe3gf64pi

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

Salim Arslan, Sofia Ira Ktena, Antonios Makropoulos, Emma C. Robinson, Daniel Rueckert, Sarah Parisot
2018 NeuroImage  
This study provides a systematic comparison between anatomical, connectivitydriven and random parcellation methods proposed in the thriving field of brain parcellation.  ...  As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis.  ...  Acknowledgements 1360 The research leading to these results has received funding from NIH grant  ... 
doi:10.1016/j.neuroimage.2017.04.014 pmid:28412442 fatcat:yrf2qf42snfkjlec6twcsba3ja

Functional brain modules reconfigure at multiple scales across the human lifespan [article]

Richard F. Betzel, Bratislav Mišić, Ye He, Jeffrey Rumschlag, Xi-Nian Zuo, Olaf Sporns
2015 arXiv   pre-print
Finally, we show that, with age, some regions in the default mode network, specifically retrosplenial cortex, maintain a greater proportion of functional connections to their own module, while regions  ...  At fine scales the most flexible regions are associated with the default mode network.  ...  For each pair of parameters, we maximized Q multi once for each multi-layer network in the ensemble of networks (a total of 500 runs).  ... 
arXiv:1510.08045v1 fatcat:2yl5qkdvajb4xdsttbz5bvtpom

Multi-Modal Neuroimaging Analysis and Visualization Tool (MMVT) [article]

O. Felsenstein, N. Peled, E. Hahn, A. P. Rockhill, L. Folsom, T. Gholipour, K. Macadams, N. Rozengard, A. C. Paulk, D. Dougherty, S. S. Cash, A. S. Widge, M. Hämäläinen (+1 others)
2019 arXiv   pre-print
Here we present the Multi-Modal Visualization Tool (MMVT), which is designed for researchers to interact with their neuroimaging functional and anatomical data through simultaneous visualization of these  ...  It is an interactive graphical interface that enables users to simultaneously visualize multi-modality functional and statistical data on cortical and subcortical surfaces as well as MEEG sensors and intracranial  ...  The central purpose of our new Multi-Modal Visualization Tool (MMVT) is to act as a unified platform for exploring neuroimaging data.  ... 
arXiv:1912.10079v1 fatcat:dxnenqt44fadppj2wjbs7l4wmy

Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer's Disease in AIBL Data: Group and Individual Analyses

Vahab Youssofzadeh, Bernadette McGuinness, Liam P. Maguire, KongFatt Wong-Lin
2017 Frontiers in Human Neuroscience  
Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category.  ...  In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data  ...  For ease of comparison, the BAs obtained based on single-(GM or PiB) and multi-modal (GM + PiB) analysis are summarized in Figure 4F supporting the superiority of the multi-modal classification to single-modal  ... 
doi:10.3389/fnhum.2017.00380 pmid:28790908 pmcid:PMC5524673 fatcat:ftsrnn4kjfdvdp2ifueuhd436a

Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF

Thomas Vincent, Solveig Badillo, Laurent Risser, Lotfi Chaari, Christine Bakhous, Florence Forbes, Philippe Ciuciu
2014 Frontiers in Neuroscience  
Through a bilinear and time-invariant system, the JDE approach models an 44 unknown HRF at the level of a group of voxels (referred to as a parcel in the following) as well as voxel-45 and condition-specific  ...  Hence, the JDE approach is a spatially adaptive GLM 48 built on unknown parcel-dependent HRFs with spatio-temporal regularization. 49 The usage of each tool depends on a choice of model which is driven  ...  A dedicated XML editor is provided with a PyQt4 graphical interface for a quicker edition and also a better review of the treatment parameters.  ... 
doi:10.3389/fnins.2014.00067 pmid:24782699 pmcid:PMC3989728 fatcat:7traxbu3kzhhhngokuobsu7osq

The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

Alessandro Daducci, Stephan Gerhard, Alessandra Griffa, Alia Lemkaddem, Leila Cammoun, Xavier Gigandet, Reto Meuli, Patric Hagmann, Jean-Philippe Thiran, Christopher P. Hess
2012 PLoS ONE  
Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods  ...  ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts.  ...  Acknowledgments The authors would like to thank Christophe Chênes and Daniel Ginsburg for major code contributions. Special thanks also go to Cristina Granziera for all the fruitful discussions.  ... 
doi:10.1371/journal.pone.0048121 pmid:23272041 pmcid:PMC3525592 fatcat:naavh6nttrcorackmqsttvxihe

Big Data and Neuroimaging

Yenny Webb-Vargas, Shaojie Chen, Aaron Fisher, Amanda Mejia, Yuting Xu, Ciprian Crainiceanu, Brian Caffo, Martin A. Lindquist
2017 Statistics in Biosciences  
Big Data are of increasing importance in a variety of areas, especially in the biosciences.  ...  We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big  ...  Acknowledgments The projects described were supported by the NIH grants R01 EB012547 and R01 EB016061 from the National Institute of Biomedical Imaging And Bioengineering, and R01 NS060910 from the National  ... 
doi:10.1007/s12561-017-9195-y pmid:29335670 pmcid:PMC5766007 fatcat:b6wwfdd735cybdsmnbvaf63mha

Cortical cartography and Caret software

David C. Van Essen
2012 NeuroImage  
Caret software is widely used for analyzing and visualizing many types of fMRI data, often in conjunction with experimental data from other modalities.  ...  This article places Caret's development in a historical context that spans three decades of brain mapping -from the early days of manually generated flat maps to the nascent field of human connectomics  ...  The recent demonstration that cortical 'myelin maps' can be obtained using the ratio of T1-weighted and T2-weighted images provides another useful modality for cortical parcelation (Fig. 5E ).  ... 
doi:10.1016/j.neuroimage.2011.10.077 pmid:22062192 pmcid:PMC3288593 fatcat:3r4qn6sma5cnzmi3ee3fudnhwa

The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes

Stephan Gerhard, Alessandro Daducci, Alia Lemkaddem, Reto Meuli, Jean-Philippe Thiran, Patric Hagmann
2011 Frontiers in Neuroinformatics  
The efficiency of sharing data and source code would benefit if a transdisciplinary lingua franca for programming was available.  ...  On the macroscale level of description, diffusion-weighted magnetic resonance imaging (MRI) is the main imaging technology employed for mapping the structural connectivity of the human connectome (Hagmann  ...  The CFF connects multi-modal data sources and metadata in a comprehensive and flexible way.  ... 
doi:10.3389/fninf.2011.00003 pmid:21713110 pmcid:PMC3112315 fatcat:stufldro4zaahfx3x5hzfawvs4

Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling

Andrew J. Quinn, Diego Vidaurre, Romesh Abeysuriya, Robert Becker, Anna C. Nobre, Mark W. Woolrich
2018 Frontiers in Neuroscience  
The signatures of this process may be observable in electrophysiological data; yet robust modeling of rapidly changing functional network structure on rapid cognitive timescales remains a considerable  ...  We show how the HMM can be inferred on continuous, parcellated source-space Magnetoencephalography (MEG) task data in an unsupervised manner, without any knowledge of the task timings.  ...  AUTHOR CONTRIBUTIONS AQ and MW designed the study. AQ and RA carried out the data analysis and organized the scripts for sharing. AQ, DV, RA, RB, AN, and MW wrote the manuscript.  ... 
doi:10.3389/fnins.2018.00603 pmid:30210284 pmcid:PMC6121015 fatcat:clsswxbdxnacxgmh3ihl67ubju

How machine learning is shaping cognitive neuroimaging

Gael Varoquaux, Bertrand Thirion
2014 GigaScience  
We also give a statistical learning perspective on these progresses and on the remaining gaping holes.  ...  Can data mining leverage them to build models of cognition? Only if it is applied to well-posed questions, crafted to reveal cognitive mechanisms.  ...  Acknowledgements The authors acknowledge fruitful discussions with Catherine Wacongne and thank the two reviewers, Krzysztof Gorgolewski and Tal Yarkoni, for their review which improved the manuscript.  ... 
doi:10.1186/2047-217x-3-28 pmid:25405022 pmcid:PMC4234525 fatcat:s32o36avwfaftba4j7q526treu

Machine learning in resting-state fMRI analysis [article]

Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
2018 arXiv   pre-print
The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.  ...  Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data.  ...  Acknowledgements This work was supported by NIH R01 grants (R01LM012719 and R01AG053949), the NSF NeuroNex grant 1707312, and NSF CAREER grant (1748377).  ... 
arXiv:1812.11477v1 fatcat:nd6j5jbspzh2rmxyyufdyesxom

The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Casey Paquola, Jessica Royer, Lindsay B Lewis, Claude Lepage, Tristan Glatard, Konrad Wagstyl, Jordan DeKraker, Paule-Joanne Toussaint, Sofie Louise Valk, D Louis Collins, Ali Khan, Katrin Amunts (+3 others)
2021 eLife  
Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments.  ...  Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.  ...  Jessica Royer received support from a Canadian Institute of Health Research (CIHR) Fellowship.  ... 
doi:10.7554/elife.70119 pmid:34431476 pmcid:PMC8445620 fatcat:hdw4lzyayjcvfnp6ayx6plpkca

Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination

Michele Fratello, Giuseppina Caiazzo, Francesca Trojsi, Antonio Russo, Gioacchino Tedeschi, Roberto Tagliaferri, Fabrizio Esposito
2017 Neuroinformatics  
Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration  ...  These results highlight the potentials of mining complementary information from the integration of multiple data views in the classification of connectivity patterns from multi-modal brain images in the  ...  Acknowledgements The authors would like to thank A. Paccone, A. Serra and G. Luciano for their help in collecting data and running experiments.  ... 
doi:10.1007/s12021-017-9324-2 pmid:28210983 pmcid:PMC5443864 fatcat:5bvxad4ktbg7datb5e2ufq7myu
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