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.
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