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Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth

Theodore D. Satterthwaite, Daniel H. Wolf, Kosha Ruparel, Guray Erus, Mark A. Elliott, Simon B. Eickhoff, Efstathios D. Gennatas, Chad Jackson, Karthik Prabhakaran, Alex Smith, Hakon Hakonarson, Ragini Verma (+3 others)
2013 NeuroImage  
Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion  ...  Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity.  ...  Acknowledgments Many thanks to the acquisition and recruitment team: Marisa Riley, Jack Keefe, Nick DeLeo, Raphael Gerraty, Elliott Yodh, and Rosetta Chiavacci. Thanks to Ewald Moser for discussion.  ... 
doi:10.1016/j.neuroimage.2013.06.045 pmid:23792981 pmcid:PMC3874413 fatcat:qp37br62ojhdlbj56q22ox7koe

The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity

Michael N. Hallquist, Kai Hwang, Beatriz Luna
2013 NeuroImage  
In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically  ...  Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach.  ...  Preparation of the manuscript was supported in part by NIMH Grant F32 MH090629 to Dr. Hallquist.  ... 
doi:10.1016/j.neuroimage.2013.05.116 pmid:23747457 pmcid:PMC3759585 fatcat:ja3lqpdx45bbblocsujesnxpxq

Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability [article]

Alba Xifra-Porxas, Michalis Kassinopoulos, Georgios D Mitsis
2020 bioRxiv   pre-print
Nuisance signatures exhibited above-chance levels of subject discriminability; however, fMRI data corrected for these confounds improved subject identifiability.  ...  Here, we capitalize on a large sample from the Human Connectome Project to provide a comprehensive investigation of the biases in functional connectivity (FC) that arise from head motion, breathing motion  ...  Acknowledgments Competing interests The authors declare that no competing interests exist.  ... 
doi:10.1101/2020.02.04.934554 fatcat:oi2fkr6vy5dvriv2hutsme2fem

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

Susan Whitfield-Gabrieli, Alfonso Nieto-Castanon
2012 Brain Connectivity  
standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data.  ...  Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain.  ...  The authors thank Shay Mozes for initial programming support and John Gabrieli for comments on the article.  ... 
doi:10.1089/brain.2012.0073 pmid:22642651 fatcat:cynvmokadjhf7eyqa4w32r7da4

Functional Connectivity Mapping in the Animal Model: Principles and Applications of Resting-State fMRI

Martin Gorges, Francesco Roselli, Hans-Peter Müller, Albert C. Ludolph, Volker Rasche, Jan Kassubek
2017 Frontiers in Neurology  
This review focuses on the principles of "resting-state" functional connectivity analysis and its applications to living animals.  ...  Resting-state" fMRI has substantially contributed to the understanding of human and non-human functional brain organization by the analysis of correlated patterns in spontaneous activity within dedicated  ...  ACKNOwLeDgMeNTS Small animal imaging is part of the core facilities at Ulm University, Ulm, Germany, and integrated in the center for translational imaging "From Molecule to Man" (MoMan).  ... 
doi:10.3389/fneur.2017.00200 pmid:28539914 pmcid:PMC5423907 fatcat:roufzeml7nh7dji77vh77qnuge

White Matter Denoising Improves the Identifiability of Large-Scale Networks and Reduces the Effects of Motion in fMRI Functional Connectivity [article]

Michalis Kassinopoulos, Georgios D Mitsis
2019 bioRxiv   pre-print
and biases in the preprocessed fMRI data.  ...  To this end, we propose a framework that summarizes the scores from eight QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio (SNR) and the reduction in motion artifacts  ...  we have rigorously examined the effects of different preprocessing steps on SNR and degree of motion artifacts and biases in resting-state fMRI data, focusing on functional networks.  ... 
doi:10.1101/837609 fatcat:us63nrunwjeclo6rwwgj3fejyu

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data [article]

Lea Waller, Susanne Erk, Elena Pozzi, Yara J. Toenders, Courtney C. Haswell, Marc Büttner, Paul M. Thompson, Lianne Schmaal, Rajendra A. Morey, Henrik Walter, Ilya M. Veer
2021 bioRxiv   pre-print
HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression.  ...  Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline.  ...  of task-based and resting-state fMRI analyses from raw data to group-level statistics, builds on the progress and contributions of fMRIPrep developers, and extends its functionality beyond preprocessing  ... 
doi:10.1101/2021.05.07.442790 fatcat:6seux5ybkncl7dpdlhj2ccsnx4

Functional Brain Network Estimation with Time Series Self-scrubbing [article]

Weikai Li, Lishan Qiao, Zhengxia Wang, Dinggang Shen
2017 bioRxiv   pre-print
including original artifacts (e.g., micro head motion), non-resting functional disturbing (e.g., mind-wandering), and new 'noises' caused by the preprocessing pipeline per se.  ...  scrubbing the data and estimating FBN simultaneously in a single framework.  ...  For example, the "resting-state" fMRI data tend to involve many different functional processes, e.g., mind-wandering [26] , thus resulting in non-resting-state time points in the fMRI data that cannot  ... 
doi:10.1101/191262 fatcat:gcg3olm72reixd5q7lwmwknste

Robust Motion Regression of Resting-State Data Using a Convolutional Neural Network Model

Zhengshi Yang, Xiaowei Zhuang, Karthik Sreenivasan, Virendra Mishra, Dietmar Cordes
2019 Frontiers in Neuroscience  
Head motion considerably confounds the interpretation of rs-fMRI data.  ...  Resting-state functional magnetic resonance imaging (rs-fMRI) based on the blood-oxygen-level-dependent (BOLD) signal has been widely used in healthy individuals and patients to investigate brain functions  ...  While the CNN network is developed based on resting-state data, this technique potentially can also be useful for reducing motion-related artifacts in task-based fMRI data, whereas an additional study  ... 
doi:10.3389/fnins.2019.00169 pmid:31057348 pmcid:PMC6482337 fatcat:osfkcm2cq5c2nhijhxiwqzvv4e

Deconvolution filtering: Temporal smoothing revisited

Keith Bush, Josh Cisler
2014 Magnetic Resonance Imaging  
Results for the estimation of functional connectivity of simulated BOLD data demonstrated that analysis (via standard estimation methods) using deconvolution filtered BOLD data achieved superior performance  ...  the true functional connectivity of a three-node neural system.  ...  that simulates the best possible FFTbased filter achievable in practice given an excellent simulation of resting state BOLD signal.  ... 
doi:10.1016/j.mri.2014.03.002 pmid:24768215 pmcid:PMC4111265 fatcat:hvnblu4q7rfgphsmipox2ybt3y

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

Deepthi P. Varikuti, Felix Hoffstaedter, Sarah Genon, Holger Schwender, Andrew T. Reid, Simon B. Eickhoff
2016 Brain Structure and Function  
Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology.  ...  The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity.  ...  Theme "Supercomputing and Modeling for the Human Brain" and the European Union Seventh Framework Program (FP7/2007.  ... 
doi:10.1007/s00429-016-1286-x pmid:27550015 pmcid:PMC5322256 fatcat:x3y67ywnvba4to32pg2fuilgd4

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, Meryem Ayşe Yücel
2020 Frontiers in Human Neuroscience  
In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields.  ...  Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted  ...  (2) a few minutes of individual resting state data without evoked hemodynamic responses are required to train the tCCA projection filters.  ... 
doi:10.3389/fnhum.2020.00030 pmid:32132909 pmcid:PMC7040364 fatcat:t67jpwxhmbhkjnkmgxcwn5hrqm

Identifying Rodent Resting-State Brain Networks with Independent Component Analysis

Dusica Bajic, Michael M. Craig, Chandler R. L. Mongerson, David Borsook, Lino Becerra
2017 Frontiers in Neuroscience  
in functional connectivity between experimental groups.  ...  Safe and efficient positioning of an animal in the MRI scanner (and subsequent scanning) is paramount during data acquisition and requires several steps.  ...  The funders had no role in study design, data collection, analysis, decision to publish, and/or preparation of the manuscript.  ... 
doi:10.3389/fnins.2017.00685 pmid:29311770 pmcid:PMC5733053 fatcat:mvmsbl4fxvegnhrph26ycwjjoi

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

David V. Smith, Amanda V. Utevsky, Amy R. Bland, Nathan Clement, John A. Clithero, Anne E.W. Harsch, R. McKell Carter, Scott A. Huettel
2014 NeuroImage  
for (via multiple regression) the influence of other networks and sources of variability.  ...  Our findings characterize robust-yet frequently ignoredneural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences.  ...  We thank Steve Stanton for hormone analyses and Edward McLaurin for assistance with data collection. We also thank Timothy Strauman and Jacob Young for feedback on previous drafts of the manuscript.  ... 
doi:10.1016/j.neuroimage.2014.03.042 pmid:24662574 pmcid:PMC4074548 fatcat:zdujhma7knad7ew5lvqj575t6a

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Stefan Frässle, Eduardo A. Aponte, Saskia Bollmann, Kay H. Brodersen, Cao T. Do, Olivia K. Harrison, Samuel J. Harrison, Jakob Heinzle, Sandra Iglesias, Lars Kasper, Ekaterina I. Lomakina, Christoph Mathys (+9 others)
2021 Frontiers in Psychiatry  
to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition.  ...  In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry.  ...  PREPROCESSING Data preprocessing is closely intertwined with quality control and artifact correction.  ... 
doi:10.3389/fpsyt.2021.680811 pmid:34149484 pmcid:PMC8206497 fatcat:ichfqltpdbdfflh2ozfk4lduda
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