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

Brain templates and atlases
Alan C. Evans, et al.,
NeuroImage 62 (2012) 911-922

BrainVoyager — Past, present, future
Rainer Goebel,
NeuroImage 62 (2012) 748-756

Cortical cartography and Caret Software
David C. Van Essen
NeuroImage 62 (2012) 757-764

Bruce Fischl
NeuroImage 62 (2012) 774-781

Mark Jenkinson, et al.,
NeuroImage 62 (2012) 782-790

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)

SPM: A history
John Ashburner,
NeuroImage 62 (2012) 791-800

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

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

Functional and Effective Connectivity: A Review
Karl J. Friston,
Brain Connectivity Volume 1, Number 1, 2011

Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation,
Caterina Gratton, et al.,
Neuron, 98, 439–452

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

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

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

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

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

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

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

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

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


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


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
Supplemental figures
The author has also made some videos available online to accompany this article. They can be found at

The global signal in fMRI: Nuisance or information?,
Thomas T Liu, Alican Nalci, Maryam Falahpour
NeuroImage 154 (2017) 150–158

Towards a consensus regarding global signal regression for resting state functional connectivity MRI,
Kevin Murphy, Michael D Fox
NeuroImage 154 (2017) 169–173
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

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)

Design issues

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

A history of randomized task designs in fMRI,
Vincent P. Clark,
NeuroImage 62 (2012) 1190-1194

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

The development and use of phase-encoded functional MRI designs,
Stephen A. Engel,
NeuroImage 62 (2012) 1195-1200

The development of event-related fMRI designs,
Thomas T. Liu,
NeuroImage 62 (2012) 1157-1162

Studying the freely-behaving brain with fMRI,
Eleanor A. Maguire,
NeuroImage 62 (2012) 1170-1176

Targeting the functional properties of cortical neurons using fMR-adaptation,
Rafael Malach,
NeuroImage 62 (2012) 1163-1169

The mixed block/event-related design,
Steven E. Petersen, Joseph W. Dubis
NeuroImage 62 (2012) 1177-1184

Optimization of experimental design in fMRI: a general framework using a genetic algorithm,
Tor D. Wager and Thomas Nichols,
NeuroImage 18 (2003) 293-309

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

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

Functional interactions between intrinsic brain activity and behavior,
Sepideh Sadaghiani, Andreas Kleinschmidt,
Neuroimage 80 (2013) 379-386

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

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

Introduction to machine learning for brain imaging,
Steven Lemm, Benjamin Blankertz, et al.,
NeuroImage 56 (2011) 387–399

Machine learning classifiers and fMRI: A tutorial overview,
Francisco Pereira, Tom Mitchell, Matthew Botvinick,
Neuroimage 45 (2009) S199–S209

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

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

fMRI: More Voxels, More Problems?

fMRI: Can MVPA Really Help Crack the Neural Code?

Single-Unit Recordings Reveal Limitations of fMRI MVPA?

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

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

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
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

Multiple testing corrections, nonparametric methods, and random field theory
Thomas E Nichols
NeuroImage 62 (2012) 811–815

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

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

A power calculation guide for fMRI studies
Jeanette Mumford
SCAN (2012) 7, 738–742

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)

The secret lives of experiments: Methods reporting in the fMRI literature,
Joshua Carp,
Neuroimage 63 (2012) 289–300

Guideline for reporting an fMRI study,
Russell A Poldrack, Paul C Fletcher, Richard N Henson, et al.,
Neuroimage 40 (2008) 409–414

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

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

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

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

Imaging the developing brain with fMRI,
MC Davidson, KM Thomas, and BJ Casey
Mental Retardation and Developmental Disabilities Research Reviews 9 (2003) 161–167

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

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

Considerations for imaging the adolescent brain,
Adrian Galván, Linda Van Leijenhorst, Kristine M McGlennen
Developmental Cognitive Neuroscience 2 (2012) 293–302

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

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

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

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

Neuroimaging of the developing brain,
John Darrell Van Horn, Kevin Archer Pelphrey,
Brain Imaging and Behavior 9 (2015) 1–4

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

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)

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

A topographic latent source model for fMRI data,
Samuel Gershman, David M Blei, Francisco Pererira, Kenneth A Norman
Neuroimage 57 (2011) 89–100

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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
(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

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

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

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