Michigan Neuroimaging Initiative

A collection of readings

Under the headings you will find bibliographic information for readings in those respective neuroimaging topics. Where possible, we made the title a link to the .pdf of the cited document. To access those documents, you must be an academic affiliate of the University of Michigan with UM Library privileges; authnetication will be required. For those not UM affiliates, we provide, where possible, the DOI for the citation so you can find it elsewhere.

Many of these readings are from restricted access journals. Please check Tutorial materials page for links to freely available and open source/science articles. Some freely available articles are included here for logistical reasons, but the DOI citation should lead you to the free version readily.

 

Table of Contents
Click on a contents entry to jump to that section

Analysis packages Connectivity
Checking and improving data quality Default mode
Design issues Dynamic connectivity
Multivoxel pattern analysis (MVPA) Multiple comparisons and corrections
Reporting guidelines, data sharing, and data repositories Issues with older and younger subjects
Bayesian and spatial analysis Voodoo correlations
Pipeline software and organization Functional near-infrared spectroscopy

 

Analysis packages: Origins and overviews


AFNI: What a long strange trip it's been
Robert W. Cox,
NeuroImage 62 (2012) 743-747
doi:10.1016/j.neuroimage.2011.08.056

Brain templates and atlases
Alan C. Evans, et al.,
NeuroImage 62 (2012) 911-922
doi:10.1016/j.neuroimage.2012.01.024

BrainVoyager — Past, present, future
Rainer Goebel,
NeuroImage 62 (2012) 748-756
doi:10.1016/j.neuroimage.2012.01.024

Cortical cartography and Caret Software
David C. Van Essen
NeuroImage 62 (2012) 757-764
doi:10.1016/j.neuroimage.2011.10.077

FreeSurfer
Bruce Fischl
NeuroImage 62 (2012) 774-781
doi:10.1016/j.neuroimage.2012.01.021

FSL
Mark Jenkinson, et al.,
NeuroImage 62 (2012) 782-790
doi:10.1016/j.neuroimage.2011.09.015

Motivation and Synthesis of the FIAC Experiment: Reproducibility of fMRI Results Across Expert Analyses
Jean Baptiste Poline, et al.,
Human Brain Mapping, 27:351-359 (2006)
doi:10.1002/hbm.20268

SPM: A history
John Ashburner,
NeuroImage 62 (2012) 791-800
doi:10.1016/j.neuroimage.2011.10.025

Connectivity and resting state


The effect of scan length on the reliability of resting-state fMRI connectivity estimates
Rasmus M. Birn, et al.,
NeuroImage 83 (2013) 550-558
doi:10.1016/j.neuroimage.2013.05.099

Approaches for the Integrated Analysis of Structure, Function and Connectivity of the Human Brain
Simon B. Eickhoff and Christian Grefkes,
Clinical EEG and Neuroscience, Copyright 2011 VOL. 42 NO. 2
doi:10.1177/155005941104200211

Functional and Effective Connectivity: A Review
Karl J. Friston,
Brain Connectivity Volume 1, Number 1, 2011
doi:10.1089/brain.2011.0008

Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation,
Caterina Gratton, et al.,
Neuron, 98, 439–452
doi:10.1016/j.neuron.2018.03.035

Imaging structural and functional connectivity: towards a unified definition of human brain organization?
Maxime Guye, et al.,
Current Opinion in Neurology 2008, 21:393-403
10.1097/WCO.0b013e3283065cfb

Investigating white matter fibre density and morphology using fixel-based analysis,
David Raffelt, J-Donald Tournier, et al.,
NeuroImage 144 (2017) 58–73

MATLAB toolbox for functional connectivity
Dongli Zhou, Wesley K. Thompson, Greg Siegle,
NeuroImage 47 (2009) 1590-1607
doi:10.1016/j.neuroimage.2009.05.089

Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective
Xi-Nian Zuoa, Xiu-Xia Xing,
Neuroscience and Biobehavioral Reviews 45 (2014) 100-118
doi:10.1016/j.neubiorev.2014.05.009

Studying brain organization via spontaneous fMRI signal,
Jonathan D Power, Bradley L Schlaggar, Steven E Petersen,
Neuron Volume 1, Issue 4, 19 Nov 2014, 681–696
doi:10.1016/j.neuron.2014.09.007

Recent progress and outstanding issues in motion correction in resting state fMRI,
Jonathan D Power, Bradley L Schlaggar, Steven E Petersen,
Neuroimage 105 (2015) 536–551
doi:10.1016/j.neuroimage.2014.10.044

The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA,
Victor M Vergara, Andrew R Mayer, Eswar Damaraju, et al.,
Brain Connectivity Volume 1, Number 1, 2011
doi:10.1016/j.neuroimage.2016.03.038

Checking and improving data quality


Artifacts in Functional MRI and How to Mitigate Them,
L Hernandez-Garcia and M Muckley,
in Brain Mapping: An encyclopedic reference,
Academic Press, Volume 1, 2015, 231–243
doi:10.1016/B978-0-12-397025-1.00290-6

Recent progress and outstanding issues in motion correction in resting state fMRI,
Jonathan D Power, Bradley L Schlaggar, Steven E Petersen,
Neuroimage 105 (2015) 536–551
doi:10.1016/j.neuroimage.2014.10.044

The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA,
Victor M Vergara, Andrew R Mayer, Eswar Damaraju, et al.,
Brain Connectivity Volume 1, Number 1, 2011
doi:10.1016/j.neuroimage.2016.03.038

 

The following articles were selected from a Neuroimage special issue on “Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies”, which is also the title of the introductory essay by the issue editors. The full contents, which are worth scanning, can be found at
http://www.sciencedirect.com/science/journal/10538119/154

 

Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies,
Molly G Bright, Kevin Murphy
NeuroImage 154 (2017) 1–3

A simple but useful way to assess fMRI scan qualities
Jonathan D Power,
NeuroImage 154 (2017) 150 158
doi:10.1016/j.neuroimage.2016.08.009
Supplemental figures
The author has also made some videos available online to accompany this article. They can be found at
http://www.jonathanpower.net/2016-ni-the-plot.html

The global signal in fMRI: Nuisance or information?,
Thomas T Liu, Alican Nalci, Maryam Falahpour
NeuroImage 154 (2017) 150–158
doi:10.1016/j.neuroimage.2017.02.036

Towards a consensus regarding global signal regression for resting state functional connectivity MRI,
Kevin Murphy, Michael D Fox
NeuroImage 154 (2017) 169–173
doi:10.1016/j.neuroimage.2017.02.036
NOTE: Murphy and Fox, two of the leading figures in the controversy over GSR, have traditionally been on opposite sides of the global signal regression debate [C Lustig].

Default mode overview


Functional-Anatomic Fractionation of the Brain's Default Network,
Jessica R. Andrews-Hanna, Jay S. Reidler, Jorge Sepulcre, Renee Poulin, and Randy Buckner,
Neuron 65, 550-562, February 25, 2010
doi:10.1016/j.neuron.2010.02.005

The Brain’s Default Network Anatomy, Function, and Relevance to Disease,
Randy L. Buckner, Jessica R. Andrews-Hanna, Daniel L. Schacter,
Ann. N.Y. Acad. Sci. 1124: 1-38 (2008)
doi:10.1196/annals.1440.011

Design issues


Study design in fMRI: Basic principles,
Edson Amaro, Jr., Gareth J. Barker,
Brain and Cognition 60 (2006) 220-232
doi:10.1016/j.bandc.2005.11.009

A history of randomized task designs in fMRI,
Vincent P. Clark,
NeuroImage 62 (2012) 1190-1194
doi:10.1016/j.neuroimage.2012.01.010

Development of orthogonal task designs in fMRI studies of higher cognition: The NIMH experience,
Susan M. Courtney,
NeuroImage 62 (2012) 1185-1189
doi:10.1016/j.neuroimage.2012.01.007

The development and use of phase-encoded functional MRI designs,
Stephen A. Engel,
NeuroImage 62 (2012) 1195-1200
doi:10.1016/j.neuroimage.2011.09.059

The development of event-related fMRI designs,
Thomas T. Liu,
NeuroImage 62 (2012) 1157-1162
doi:10.1016/j.neuroimage.2011.10.008

Studying the freely-behaving brain with fMRI,
Eleanor A. Maguire,
NeuroImage 62 (2012) 1170-1176
doi:10.1016/j.neuroimage.2012.01.009

Targeting the functional properties of cortical neurons using fMR-adaptation,
Rafael Malach,
NeuroImage 62 (2012) 1163-1169
doi:10.1016/j.neuroimage.2012.01.002

The mixed block/event-related design,
Steven E. Petersen, Joseph W. Dubis
NeuroImage 62 (2012) 1177-1184
doi:10.1016/j.neuroimage.2011.09.084

Optimization of experimental design in fMRI: a general framework using a genetic algorithm,
Tor D. Wager and Thomas Nichols,
NeuroImage 18 (2003) 293-309
doi:10.1016/S1053-8119(02)00046-0

Dynamic Connectivity

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery,
Vince D. Calhoun, Robyn Miller, et al.
Neuron, Volume 84, Issue 2, p262–274, 22 October 2014
doi:10.1016/j.neuron.2014.10.015

Dynamic functional connectivity: Promise, issues, and interpretations.
R. Matthew Hutchison, Thilo Womelsdorf, et al.,
NeuroImage 80 (2013) 360- 378
doi:10.1016/j.neuroimage.2013.05.079

Functional interactions between intrinsic brain activity and behavior,
Sepideh Sadaghiani, Andreas Kleinschmidt,
Neuroimage 80 (2013) 379-386
doi:10.1016/j.neuroimage.2013.04.100

Multivoxel pattern analysis (MVPA)

What's in a pattern? Examining the type of signal multivariate analysis uncovers at the group level,
Roee Gilron, Jonathan Rosenblatt, et al.,
NeuroImage 146 (2017) 113–120
doi:10.1016/j.neuroimage.2010.11.004

What do differences between multi-voxel and univariate analysis mean?
How subject-, voxel-, and trial-level variance impact fMRI analysis
,
Tyler Davis, Karen F. LaRocque, , Jeanette A. Mumford, et al.
Neuroimage, 97 (2014) 271–283
doi:10.1016/j.neuroimage.2014.04.037

Introduction to machine learning for brain imaging,
Steven Lemm, Benjamin Blankertz, et al.,
NeuroImage 56 (2011) 387–399
doi:10.1016/j.neuroimage.2010.11.004

Machine learning classifiers and fMRI: A tutorial overview,
Francisco Pereira, Tom Mitchell, Matthew Botvinick,
Neuroimage 45 (2009) S199–S209
doi:10.1016/j.neuroimage.2008.11.007

Confounds in multivariate pattern analysis: Theory and rule representation case study,
Michael T. Todd, Leigh E. Nystrom, Jonathan D. Cohen
Neuroimage 77 (2013) 157–165
doi:10.1016/j.neuroimage.2013.03.039

Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines,
Gaël Varoquaux, Pradeep Reddy Raamana, et al.,
NeuroImage 145 B, (2017) 166–179
doi:10.1016/j.neuroimage.2016.10.038

fMRI: More Voxels, More Problems?
Neuroskeptic
4/18/2013

fMRI: Can MVPA Really Help Crack the Neural Code?
Neuroskeptic
6/21/2014

Single-Unit Recordings Reveal Limitations of fMRI MVPA?
Neuroskeptic
2/3/2015

Multiple comparisons and corrections


The principled control of false positives in neuroimaging
Craig M Bennet, George L Wolford, and Michael B Miller,
Social Cognitive and Affective Neuroscience (2009) 4, 417–422
doi:10.1093/scan/nsp053

How reliable are the results from functional magnetic resonance imaging?
Craig M Bennet and Michael B Miller,
Annals of the New York Academy of Sciences (2010) 1191, 133–155
doi:10.1111/j.1749-6632.2010.05446.x

Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction
Craig M Bennett, Abigail Bair, et al.,
Undated poster from an unknown source

Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
Anders Eklund, Thomas E Nichols, and Hans Knutsson,
Proceedings of the National Academy of Sciences (2016) 113 (28), 7900–7905
doi:10.1073/pnas.1602413113
Note: The PDF file contains the supplemental information and a correction after the main article.

Multivariate pattern analysis of fMRI: The early beginnings
James V Haxby
NeuroImage 62 (2012) 852–855
doi:10.1016/j.neuroimage.2012.03.016

Multiple testing corrections, nonparametric methods, and random field theory
Thomas E Nichols
NeuroImage 62 (2012) 811–815
doi:10.1016/j.neuroimage.2012.03.016

Controlling the familywise error rate in functional neuroimaging: a comparative review
Thomas Nichols and Satori Hayasaka
Statistical Methods in Medical Research 12 (2003) 419–446
doi:10.1191/0962280203sm341ra

Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations
Choong-Wan Woo, Anjali Krishnan, Tor D Wager
NeuroImage 91 (2014) 412–419

Bayesian inference in FMRI
Mark Woolrich
Neuroimage 62 (2012) 801–810
doi:10.1016/j.neuroimage.2011.10.047

A power calculation guide for fMRI studies
Jeanette Mumford
SCAN (2012) 7, 738–742
doi:10.1093/scan/nss059

Reporting guidelines, data sharing, and data repositories


Best practices in data analysis and sharing in neuroimaging using MRI,
Thomas E Nichols, Samir Das, Simon B Eickhoff, et al.,
bioRxiv (2016)
doi:10.1101/054262

The secret lives of experiments: Methods reporting in the fMRI literature,
Joshua Carp,
Neuroimage 63 (2012) 289–300
doi:10.1016/j.neuroimage.2012.07.004

Guideline for reporting an fMRI study,
Russell A Poldrack, Paul C Fletcher, Richard N Henson, et al.,
Neuroimage 40 (2008) 409–414
doi:10.1016/j.neuroimage.2007.11.048

Sharing the wealth:Neuroimaging data repositories
Simon Eickhoff, Thomas E Nichols, et al.,
Neuroimage 124 (2016) 1065‐1068
doi:10.1016/j.neuroimage.2015.10.079

Big data from small data: data-sharing in the ‘long tail’ of neuroscience,
Adam R Ferguson, Jessica L Nielson, et al.,
Nature Neuroscience 17(11) November 2014, 1442–1448
doi:10.1038/nn.3838

Making big data open: data sharing in neuroimaging,
Russell A Poldrack, Krzysztof J Gorgolewski
Nature Neuroscience 17(11) November 2014, 1510–1517
doi:10.1038/nn.3818

Issues with older and younger subjects


Hemodynamic responses in visual, motor, and somatosensory cortices in schizophrenia,
Deanna M Barch, Jennifer R Mathews, Randy L Buckner, et al.
Neuroimage 20 (2003) 1884–1893
doi:10.1016/S1053-8119(03)00449-X

Imaging the developing brain with fMRI,
MC Davidson, KM Thomas, and BJ Casey
Mental Retardation and Developmental Disabilities Research Reviews 9 (2003) 161–167
doi:10.1002/mrdd.10076

Neurovasular coupling in normal aging: A combined optical, ERP and fMRI study,
Monica Fabiani, Brian A Gorton, Edward L Maclin, et al.,
Neuroimage 85 (2014) 592–607
doi:10.1016/j.neuroimage.2013.04.113

Taking the pulse of aging: Mapping pulse pressur and elasticity in cerebral arteries with optical methods,
Monica Fabiani, Kathy A Low, Chin-Hong Tan, et al.,
Psychophysiology 51 (2014) 1072&ndahs;1088
doi:10.1111/psyp.12288

Considerations for imaging the adolescent brain,
Adrian Galván, Linda Van Leijenhorst, Kristine M McGlennen
Developmental Cognitive Neuroscience 2 (2012) 293–302
doi:10.1016/j.dcn.2012.02.002

Understanding variability in the BOLD signal and why it matters for aging,
Cheryl L Grady, Douglas D Garrett,
Brain Imaging and Behavior 8 (2014) 274–283
doi:10.1007/s11682-013-9253-0

The effects of aging upon the hemodynamic response measured by functional MRI,
Scott A Huettel, Jeffrey D Singerman, and Gregory McCarthy,
Neuroimage 13 (2001) 161–175
doi:

BOLD functional MRI in disease and pharacological studies: room for improvement?,
GD Iannetti, Richard G Wise
Magnetic Resonance Imaging 25 (2007) 978–988
doi:10.1016/j.mri.2007.03.018

Cardiorespiratory fitness mediates the effects of aging on cerebral blood flow,
Benjamin Zimmerman, Bradley P Sutton, Kathy A Low, et al.,
Frontiers in Aging Neuroscience 6 (2014) Article 59
doi:10.3389/fnagi.2014.00059

Neuroimaging of the developing brain,
John Darrell Van Horn, Kevin Archer Pelphrey,
Brain Imaging and Behavior 9 (2015) 1–4
doi:10.1007/s11682-015-9365-9

Bayesian and spatial analysis


Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia,
Alan Anticevic, Donna L Dierker, Sarah K Gillespie, et al.,
Neuroimage 41 (2008) 835‐848
doi:10.1016/j.neuroimage.2008.02.052

Introduction: Credibility, models, and parameters,
JK Kruschke
Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan, chap 2 (Waltham, MA: Academic Press / Elsevier, 2015)
doi:0.1016/B978-0-12-405888-0.00002-7

A parametric empirical Bayesian framework for fMRI-constraing MEG/EEG source reconstruction,
Richard N Henson, Guillaume Flandin, Karl J Friston, Jérémei Mattout,
Human Brain Mapping 31 (2010) 1512–1531
doi:10.1002/hbm.20956

A topographic latent source model for fMRI data,
Samuel Gershman, David M Blei, Francisco Pererira, Kenneth A Norman
Neuroimage 57 (2011) 89–100
doi:10.1016/j.neuroimage.2011.04.042

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data,
Donald J Hagler Jr, Ayse Pinar Saygin, and Martin I Sereno
Neuroimage 33 (2006) 1093–1103
doi:

Meta analysis of functional neuroimaging data via Bayesian spatial point processes,
Jian Kang, Timothy D Johnson, Thomas E Nichols, Tor D Wager,
Journal of the American Statistical Association 2011 March 1; 106(493) 124–134
doi:10.1198/jasa.2011.ap09735

Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models,
Yuan Shen, Stephen D Mayhew, Zoe Kourtzi, Peter Tǐno
Neuroimage 84 (2014) 657–671
doi:10.1016/j.neuroimage.2013.09.003

Voodoo correlations


Editor's introduction to Vul et al. (2009) and comments,
Ed Diener,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 272–273
doi:10.1111/j.1745-6924.2009.01124.x

Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition,
Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 274–290
doi:10.1111/j.1745-6924.2009.01125.x

Commentary on Vul et al.'s (2009) “Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition”,
Thomas E Nichols, Jean-Baptist Poline,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 291–293
doi:10.1111/j.1745-6924.2009.01126.x

Big correlations in little studies: inflated fMRI correlations reflect low statisticalpower – commentary on Vul et al. (2009),
Tal Yarkoni,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 294–298
doi:10.1111/j.1745-6924.2009.01127.x

Correlations in social neuroscience aren't voodoo: commentary on Vul et al. (2009),
Matthew D Lieberman, Elliot T Berkman, Tor D Wager,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 299–307
doi:10.1111/j.1745-6924.2009.01128.x

Discussion of “Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition”,
Nicole A Lazar,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 308–309
doi:10.1111/j.1745-6924.2009.01129.x

Correlations and multiple comparisons in functional imaging: a statistical perspective (commentary on Vul et al., 2009),
Martin Lindquist, Andrew Gelman,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 310–313
doi:10.1111/j.1745-6924.2009.01130.x

Understandin the mind by measuring the brain: lessons from measuring behavior (commentary on Vul et al., 2009),
Lisa Feldman Barrett,
Perspectives on Psychological Science, Vol 4, No 3 (2009) 314–318
doi:10.1111/j.1745-6924.2009.01131.x

Reply to comments on “Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition”,
Edward Vul, Christine Harris, Piotr Winkielman, Harold Pashler
Perspectives on Psychological Science, Vol 4, No 3 (2009) 319–324
doi:10.1111/j.1745-6924.2009.01132.x

Pipeline software and organization


The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows,
Pierre Bellec, Sébastien Lavoie-Courchesne, et al,
Frontiers in Neuroinformatics, 03 April 2012
doi:10.3389/fninf.2012.00007

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML,
Rhodri Cusack, Alejandro Vicente-Grabovetsky, et al.,
Frontiers in Neuroinformatics, 15 January 2015
doi:10.3389/fninf.2014.00090

Using Make for Reproducible and Parallel Neuroimaging Workflow and Quality-Assurance,
Mary K Askren, Trevor K McAllister-Day, Natalie Koh, et al.,
Frontiers in Neuroinformatics, 02 April 2016
doi:10.3389/fninf.2016.00002
(Also see links to local copies of supplemental materials at our Tutorials and training page)

Functional near-infrared spectroscopy (fNIRS)


A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,
Marco Ferrari and Valentina Quaresima,
Neuroimage, 63 (2012) 921–935
doi:10.1016/j.neuroimage.2012.03.049

Functional near-infrared spectroscopy (fNIRS): Principles and neuroscientific applications,
José León-Carrión and Umberto León-Domínguez,
in Neuroimaging: Methods, ed Peter Bright,
InTechOpen, 536–551
doi:10.5772/23146

Shedding light on words and sentences: Near-infrared spectroscopy in lanugage research,
Sonja Rossi, Silke Telkemeyer, et al.,
Neuroimage, 121 (2012) 152–163
doi:10.1016/j.bandl.2011.03.008


Originally collected by Cindy Lustig and expanded with contributions from the University of Michigan neuroimaging community