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Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data

M. Drakesmith, K. Caeyenberghs, A. Dutt, G. Lewis, A.S. David, D.K. Jones
2015 NeuroImage  
However, false positive (FP) connections arise frequently and influence the inferred topology of networks.  ...  Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics.  ...  The approach of graph theory, and particularly statistics in graph theory, is still in its infancy in neuroimaging, with little consensus or standardization of analyses.  ... 
doi:10.1016/j.neuroimage.2015.05.011 pmid:25982515 pmcid:PMC4558463 fatcat:mhtzrqeavfdsrowf5cbbwuf2rm

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

Samantha V. Abram, Nathaniel E. Helwig, Craig A. Moodie, Colin G. DeYoung, Angus W. MacDonald, Niels G. Waller
2016 Frontiers in Neuroscience  
Also, our simulation results reveal that the QNT method is effective under a variety of data conditions.  ...  In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data.  ...  ACKNOWLEDGMENTS This study was supported by grants to CD from the National Institute on Drug Abuse (NIDA) (R03 DA029177-01A1) and from the National Science Foundation (NSF) (SES-106 1817).  ... 
doi:10.3389/fnins.2016.00344 pmid:27516732 pmcid:PMC4964314 fatcat:naajm5hetngbforkhi34hremiu

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

L. Waller, A. Brovkin, L. Dorfschmidt, D. Bzdok, H. Walter, J.D. Kruschwitz
2018 Journal of Neuroscience Methods  
Results: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can  ...  Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications.  ...  graph theoretic metrics.  ... 
doi:10.1016/j.jneumeth.2018.07.001 pmid:30026069 fatcat:g3dv3kimuzha5lw2ufuhf4djna

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures [article]

Lea Waller, Anastasia Brovkin, Lena Dorfschmidt, Danilo Bzdok, Henrik Walter, Johann Daniel Kruschwitz
2018 arXiv   pre-print
Results: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can  ...  The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export.  ...  graph theoretic metrics.  ... 
arXiv:1803.00082v2 fatcat:slgntlansng73nb5c2ciofcpte

Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis

Mayuresh S. Korgaonkar, Alex Fornito, Leanne M. Williams, Stuart M. Grieve
2014 Biological Psychiatry  
These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures.  ...  Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization.  ...  This study used both graph theoretical and network-based statistical analyses of DTI data in characterizing structural organizational abnormalities in major depression.  ... 
doi:10.1016/j.biopsych.2014.02.018 pmid:24690111 fatcat:edraywi2nrbaxc6i4oba5xlnki

Improving Practices and Inferences in Developmental Cognitive Neuroscience

John C. Flournoy, Nandita Vijayakumar, Theresa W. Cheng, Danielle Cosme, Jessica E. Flannery, Jennifer H. Pfeifer
2020 Developmental Cognitive Neuroscience  
First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses.  ...  Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple  ...  Portions of this manuscript were presented at the 2019 Flux conference in New York, NY, and the authors are grateful for the interest in and support of this topic expressed by conference attendees.  ... 
doi:10.1016/j.dcn.2020.100807 pmid:32759026 pmcid:PMC7403881 fatcat:ytbkd3czrfabnifv4a73iityvq

Graph analysis and modularity of brain functional connectivity networks: searching for the optimal threshold [article]

Cécile Bordier, Carlo Nicolini, Angelo Bifone
2017 arXiv   pre-print
Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold.  ...  Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity.  ...  Acknowledgements This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement No 668863  ... 
arXiv:1705.06481v1 fatcat:p3p45i2utjep3dupnjs4adlnm4

Network analysis shows decreased ipsilesional structural connectivity in glioma patients [article]

Lucius Samo Fekonja, Ziqian Wang, Alberto Cacciola, Timo Roine, Baran D. Aydogan, Darius Mewes, Sebastian Vellmer, Peter Vajkoczy, Thomas Picht
2021 medRxiv   pre-print
Network-based statistics (NBS) allow to 25 assess local network differences (1) and graph theoretical analyses (2) enable investigation of global and local network properties.  ...  In network science, reduced global and local efficiency reflect the impairment of information transfer across different regions of a network.  ...  These graph theoretical network analyses were performed by GRETNA 2.0.0 (54).  ... 
doi:10.1101/2021.06.22.21259319 fatcat:iupa4ais5nd2dg2f54swgkwqai

Combining network topology and information theory to construct representative brain networks

Andrea I. Luppi, Emmanuel A. Stamatakis
2020 Network Neuroscience  
Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.  ...  Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data.  ...  ACKNOWLEDGMENTS The authors are grateful to Helena Gellersen for providing the motivation that inspired this work, and to members of the Cognition and Consciousness Imaging Group for helpful discussion  ... 
doi:10.1162/netn_a_00170 pmid:33688608 pmcid:PMC7935031 fatcat:53nyxduxuvdarnxwpb7elwkqnm

Altered topology of structural brain networks in patients with Gilles de la Tourette syndrome

E. Schlemm, B. Cheng, F. Fischer, C. Hilgetag, C. Gerloff, G. Thomalla
2017 Scientific Reports  
None of the previous studies assessed global changes of structural network integrity and topology by graph theoretical analysis.  ...  Graph theoretical measures therefore quantify specific organizational properties of its structure.  ...  Acknowledgements The research leading to these results has received funding from the German Research Foundation (DFG), SFB 936 "Multi-site Communication in the Brain" (Projects A1, C1, C2, Z3).  ... 
doi:10.1038/s41598-017-10920-y pmid:28878322 pmcid:PMC5587563 fatcat:jh4myec7vzemzpodnbd2f6tira

Tutorials in population neuroimaging: Using epidemiology in neuroimaging research

Sara Godina, Mini E. Jacob, Mary Ganguli
2022 Frontiers in Neuroimaging  
The neuroimaging investigator with a grasp of the principles of epidemiology is in a unique position to undertake valid clinical epidemiology and etiological research.  ...  Epidemiology serves as a framework to organize pieces of data and guide critical thinking in the research process from the early stages of study design to the end goal of reaching appropriate inferences  ...  "Truth" Null Null hypothesis hypothesis false true Conclusion Reject null True Positive False Positive hypothesis (Power, 1-β) (Type 1 error, α) Accept null False Negative True Negative hypothesis Type  ... 
doi:10.3389/fnimg.2022.934514 fatcat:2ujjd7ersvfafbbdlyycjqkxim

Scanning the Horizon: Towards transparent and reproducible neuroimaging research [article]

Russell Poldrack, Chris I Baker, Joke Durnez, Krzysztof Gorgolewski, Paul M Matthews, Marcus Munafo, Thomas Nichols, Jean-Baptiste Poline, Edward Vul, Tal Yarkoni
2016 bioRxiv   pre-print
Problems such as low statistical power, flexibility in data analysis, software errors, and lack of direct replication apply to many fields, but perhaps particularly to fMRI.  ...  Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community.  ...  at p < 0.001 and with a 10-voxel extent threshold (which is a common heuristic correction shown by Eklund et al. 44 to result in highly inflated levels of false positives).  ... 
doi:10.1101/059188 fatcat:7fdlzafrp5f5lasvzwtipzgriy

Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold

Cécile Bordier, Carlo Nicolini, Angelo Bifone
2017 Frontiers in Neuroscience  
Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold.  ...  Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity.  ...  Edward Bullmore and Prof. Nicolas Crossley for providing the weighted, unthresholded functional connectivity network derived from resting state fMRI in healthy volunteers.  ... 
doi:10.3389/fnins.2017.00441 pmid:28824364 pmcid:PMC5540956 fatcat:e3v6i6qghvdsbevpvoixtkvske

Models of Network Spread and Network Degeneration in Brain Disorders

Ashish Raj, Fon Powell
2018 Biological Psychiatry: Cognitive Neuroscience and Neuroimaging  
., 2017 found significant alteration in global network statistics in AD, the latter, using a data-driven event-based model for sequencing the progression of AD patients, demonstrated that node-level measures  ...  Briefly, tractography is known to yield false-positive and false-negative connections, where as a consequence, spurious tracts might be detected as plausible and genuine ones as invalid.  ...  Acknowledgements AR was supported in part by NIH grants R01NS092802 and R01 EB022717.  ... 
doi:10.1016/j.bpsc.2018.07.012 pmid:30170711 pmcid:PMC6219468 fatcat:kfszzb54jrgyjcfmkhmlrhjnni

Covariance statistics and network analysis of brain PET imaging studies

Mattia Veronese, Lucia Moro, Marco Arcolin, Ottavia Dipasquale, Gaia Rizzo, Paul Expert, Wasim Khan, Patrick M. Fisher, Claus Svarer, Alessandra Bertoldo, Oliver Howes, Federico E. Turkheimer
2019 Scientific Reports  
A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed.  ...  The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between  ...  Sensitivity of network metrics to thresholding in parametric and permutation tests. False positive rates (FPRs) are reported as function of different thresholding.  ... 
doi:10.1038/s41598-019-39005-8 pmid:30792460 pmcid:PMC6385265 fatcat:n3cicomuyzfoddetdnxhffyb7q
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