Publications

2023	Articles Telomere length and brain imaging phenotypes in UK Biobank Anya Topiwala, Thomas E. Nichols, Logan Z J Williams, Emma C Robinson, Fidel Alfaro-Almagro, Bernd Taschler, et al. PLOS ONE 18(3): e0282363. doi: 10.1371/journal.pone.0282363.
	Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers Ahmed M Salih, Esmeralda Ruiz Pujadas, Víctor M Campello, Celeste McCracken, Nicholas C Harvey, Stefan Neubauer, et al. Journal of Magnetic Resonance Imaging. doi: 10.1002/jmri.28675.
	Reply to: Multivariate BWAS can be replicable with moderate sample sizes Brenden Tervo-Clemmens, Scott Marek, Roselyne J. Chauvin, Andrew N. Van, Benjamin P. Kay, Timothy O. Laumann, et al. Nature 615(7951): E8–E12. doi: 10.1038/s41586-023-05746-w.
	Brain deficit patterns of metabolic illnesses overlap with those for major depressive disorder: A new metric of brain metabolic disease Kathryn S Hatch, Si Gao, Yizhou Ma, Alessandro Russo, Neda Jahanshad, Paul M Thompson, et al. Human Brain Mapping: 1–18. doi: 10.1002/hbm.26235.
2022	Preprints Association of Gout with Brain Reserve and Vulnerability to Neurodegenerative Disease Anya Topiwala, Bmbch Dphil, Kulveer Mankia Bmbch, Steven Bell, Alastair Webb, Bmbch Dphil, et al. Technical Report medRxiv: 1–18. doi: 10.1101/2022.11.09.22282119.
	Cluster extent inference revisited: quantification and localization of brain activity Jelle J. Goeman, Paweł Górecki, Ramin Monajemi, Xu Chen, Thomas E. Nichols, and Wouter Weeda. Technical Report : 1–52.
	Addressing selection bias in the UK Biobank neurological imaging cohort Valerie Bradley and Thomas E Tom Nichols. Technical Report medRxiv: 1–38. doi: 10.1101/2022.01.13.22269266.
	Spatial Confidence Regions for Combinations of Excursion Sets in Image Analysis Thomas Maullin-Sapey, Armin Schwartzman, and Thomas E Nichols. Technical Report : 1–33.
	Articles Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study Eugene Duff, Fernando Zelaya, Fidel Alfaro Almagro, Karla L. Miller, Naomi Martin, Thomas E. Nichols, et al. PLOS ONE 17(9): e0273704. doi: 10.1371/journal.pone.0273704.
	Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants Celeste McCracken, Zahra Raisi-Estabragh, Michele Veldsman, Betty Raman, Andrea Dennis, Masud Husain, et al. Nature Communications 13(1): 7839. doi: 10.1038/s41467-022-35321-2.
	BLMM: Parallelised computing for big linear mixed models Thomas Maullin-Sapey and Thomas E. Nichols. NeuroImage 264(March): 119729. doi: 10.1016/j.neuroimage.2022.119729.
	Alcohol consumption and MRI markers of brain structure and function: Cohort study of 25,378 UK Biobank participants Anya Topiwala, Klaus P Ebmeier, Thomas Maullin-Sapey, and Thomas E Nichols. NeuroImage: Clinical 35(May): 103066. doi: 10.1016/j.nicl.2022.103066.
	The expected behaviour of random fields in high dimensions: contradictions in the results of Bansal and Peterson Samuel Davenport and Thomas E. Nichols. Magnetic Resonance Imaging 90(January 2021): 70–75. doi: 10.1016/j.mri.2021.11.010.
	Causal inference on neuroimaging data with Mendelian randomisation Bernd Taschler, Stephen M. Smith, and Thomas E. Nichols. NeuroImage 258(March): 119385. doi: 10.1016/j.neuroimage.2022.119385.
	Reproducible brain-wide association studies require thousands of individuals Scott Marek, Brenden Tervo-Clemmens, Finnegan J Calabro, David F Montez, Benjamin P Kay, Alexander S Hatoum, et al. Nature 603(7902): 654–660. doi: 10.1038/s41586-022-04492-9.
	Isolating the sources of pipeline-variability in group-level task-fMRI results Alexander Bowring, Thomas E. Nichols, and Camille Maumet. Human Brain Mapping 43(3): 1112–1128. doi: 10.1002/hbm.25713.
2021	Preprints Penalized generalized estimating equations for relative risk regression with applications to brain lesion data Petya Kindalova, Michele Veldsman, Thomas E Nichols, and Ioannis Kosmidis. Technical Report : 1–32. doi: 10.1101/2021.11.01.466751.
	Articles Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework Petya Kindalova, Ioannis Kosmidis, and Thomas E Nichols. bioRxiv. doi: 10.1101/2021.01.11.426223.
	Fisher Scoring for crossed factor linear mixed models Thomas Maullin-Sapey and Thomas E. Nichols. Statistics and Computing 31(5): 53. doi: 10.1007/s11222-021-10026-6.
	Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis Ann-Marie Mallon, Dieter A Häring, Frank Dahlke, Piet Aarden, Soroosh Afyouni, Daniel Delbarre, et al. BMC Medical Research Methodology 21(1): 250. doi: 10.1186/s12874-021-01409-4.
	Discussion on “distributional independent component analysis for diverse neuroimaging modalities” by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo Kan Keeratimahat and Thomas E Nichols. Biometrics: 1–5. doi: 10.1111/biom.13591.
	Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project Si Gao, Brian Donohue, Kathryn S. Hatch, Shuo Chen, Tianzhou Ma, Yizhou Ma, et al. NeuroImage 245(August): 118700. doi: 10.1016/j.neuroimage.2021.118700.
	Pitfalls in brain age analyses Ellyn R. Butler, Andrew Chen, Rabie Ramadan, Trang T. Le, Kosha Ruparel, Tyler M. Moore, et al. Human Brain Mapping 42(13): 4092–4101. doi: 10.1002/hbm.25533.
	Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework Petya Kindalova, Ioannis Kosmidis, and Thomas E. Nichols. NeuroImage 236(January): 118090. doi: 10.1016/j.neuroimage.2021.118090.
	Meaningful Associations in the Adolescent Brain Cognitive Development Study Anthony Steven Dick, Daniel A. Lopez, Ashley L. Watts, Steven Heeringa, Chase Reuter, Hauke Bartsch, et al. NeuroImage: 118262. doi: 10.1016/j.neuroimage.2021.118262.
	No safe level of alcohol consumption for brain health: observational cohort study of 25,378 UK Biobank participants Anya Topiwala, Klaus P. Ebmeier, Thomas Maullin-Sapey, Thomas E Nichols, and Nichols. medRxiv. doi: 10.1101/2021.05.10.21256931.
	Shared and Anxiety-Specific Pediatric Psychopathology Dimensions Manifest Distributed Neural Correlates Julia O. Linke, Rany Abend, Katharina Kircanski, Michal Clayton, Caitlin Stavish, Brenda E. Benson, et al. Biological Psychiatry 89(6): 579–587. doi: 10.1016/j.biopsych.2020.10.018.
	Multimodal Imaging Brain Markers in Early Adolescence Are Linked with a Physically Active Lifestyle Piergiorgio Salvan, Thomas Wassenaar, Catherine Wheatley, Nicholas Beale, Michiel Cottaar, Daniel Papp, et al. The Journal of Neuroscience 41(5): 1092–1104. doi: 10.1523/JNEUROSCI.1260-20.2020.
	The effect of a one-year vigorous physical activity intervention on fitness, cognitive performance and mental health in young adolescents: the Fit to Study cluster randomised controlled trial T M Wassenaar, C M Wheatley, N Beale, T Nichols, P Salvan, A Meaney, et al. International Journal of Behavioral Nutrition and Physical Activity 18(1): 47. doi: 10.1186/s12966-021-01113-y.
	Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data Peter Kochunov, Meghann C Ryan, Qifan Yang, Kathryn S Hatch, Alyssa Zhu, Sophia I Thomopoulos, et al. NeuroImage: Clinical: 102574. doi: 10.1016/j.nicl.2021.102574.
	Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation Frank Dahlke, Douglas L Arnold, Piet Aarden, Habib Ganjgahi, Dieter A Häring, Jelena Čuklina, et al. Multiple Sclerosis Journal 27(13): 2062–2076. doi: 10.1177/1352458520988637.
	Erratum: Exploring the impact of analysis software on task \textless scp\textgreater fMRI\textless /scp\textgreater results Alexander Bowring, Camille Maumet, and Thomas E. Nichols. Human Brain Mapping 42(5): 1564–1578. doi: 10.1002/hbm.25302.
	Confidence Sets for Cohen's d effect size images Alexander Bowring, Fabian J.E. Telschow, Armin Schwartzman, and Thomas E. Nichols. NeuroImage 226(May 2020): 117477. doi: 10.1016/j.neuroimage.2020.117477.
	Confound modelling in UK Biobank brain imaging Fidel Alfaro-Almagro, Paul McCarthy, Soroosh Afyouni, Jesper L.R. Andersson, Matteo Bastiani, Karla L. Miller, et al. NeuroImage: 117002. doi: 10.1016/j.neuroimage.2020.117002.
2020	Preprints How does group differences in motion scrubbing affect false positives in functional connectivity studies ? Anders Eklund, Thomas E Nichols, Soroosh Afyouni, and Cameron Craddock. Technical Report bioRxiv. doi: 10.1101/2020.02.12.944454.
	Articles Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure Dragana M. Pavlović, Bryan R.L. Guillaume, Emma K. Towlson, Nicole M.Y. Kuek, Soroosh Afyouni, Petra E. Vértes, et al. NeuroImage 220(January): 116611. doi: 10.1016/j.neuroimage.2020.116611.
	Multi-subject stochastic blockmodels with mixed effects for adaptive analysis of individual differences in human brain network cluster structure Dragana M. Pavlović, Bryan R.L. L. Guillaume, Soroosh Afyouni, and Thomas E. Nichols. Statistica Neerlandica 74(3): 363–396. doi: 10.1111/stan.12219.
	An interactive meta-analysis of MRI biomarkers of myelin Matteo Mancini, Agah Karakuzu, Julien Cohen-Adad, Mara Cercignani, Thomas E. Nichols, and Nikola Stikov. eLife: 1–23. doi: 10.7554/eLife.61523.
	Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression Marco Palma, Shahin Tavakoli, Julia Brettschneider, and Thomas E. Nichols. NeuroImage 219(June): 116938. doi: 10.1016/j.neuroimage.2020.116938.
	Associations between fitness, physical activity and mental health in a community sample of young British adolescents: baseline data from the Fit to Study trial Catherine Wheatley, Thomas Wassenaar, Piergiorgio Salvan, Nick Beale, Thomas Nichols, Helen Dawes, et al. BMJ Open Sport & Exercise Medicine 6(1): e000819. doi: 10.1136/bmjsem-2020-000819.
	Permutation inference for canonical correlation analysis Anderson M. Winkler, Olivier Renaud, Stephen M. Smith, and Thomas E. Nichols. NeuroImage 220(April): 117065. doi: 10.1016/j.neuroimage.2020.117065.
	Developmental maturation of inhibitory control circuitry in a high-risk sample: A longitudinal fMRI study Lora M. Cope, Jillian E. Hardee, Meghan E. Martz, Robert A. Zucker, Thomas E. Nichols, and Mary M. Heitzeg. Developmental Cognitive Neuroscience: 100781. doi: 10.1016/j.dcn.2020.100781.
	Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities. Michele Veldsman, Petya Kindalova, Masud Husain, Ioannis Kosmidis, and Thomas E. Nichols. NeuroImage. Clinical 28(June): 102405. doi: 10.1016/j.nicl.2020.102405.
	The psychological correlates of distinct neural states occurring during wakeful rest. Theodoros Karapanagiotidis, Diego Vidaurre, Andrew J Quinn, Deniz Vatansever, Giulia L Poerio, Adam Turnbull, et al. Scientific reports 10(1): 21121. doi: 10.1038/s41598-020-77336-z.
	Estimating the prevalence of missing experiments in a neuroimaging meta-analysis Pantelis Samartsidis, Silvia Montagna, Angela R. Laird, Peter T. Fox, Timothy D. Johnson, and Thomas E. Nichols. Research Synthesis Methods 11(6): 866–883. doi: 10.1002/jrsm.1448.
	The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging Clara A Moreau, Martineau Jean-Louis, Ross Blair, Christopher J Markiewicz, Jessica A Turner, Vince D Calhoun, et al. GigaScience 9(10): 1–4. doi: 10.1093/gigascience/giaa104.
	Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing Michele Veldsman, Xin-You Tai, Thomas Nichols, Steve Smith, João Peixoto, Sanjay Manohar, et al. Nature Communications 11(1): 4340. doi: 10.1038/s41467-020-18201-5.
	Discovering correlates of age-related decline in a healthy late-midlife male birth cohort Kiyana Zarnani, Stephen M Smith, Fidel Alfaro-Almagro, Birgitte Fagerlund, Martin Lauritzen, Egill Rostrup, et al. Aging 12(17): 16709–16743. doi: 10.18632/aging.103345.
	Multiple testing correction over contrasts for brain imaging Bianca A.V. Alberton, Thomas E. Nichols, Humberto R. Gamba, and Anderson M. Winkler. NeuroImage 216(September 2019): 116760. doi: 10.1016/j.neuroimage.2020.116760.
	Variability in the analysis of a single neuroimaging dataset by many teams Rotem Botvinik-Nezer, Felix Holzmeister, Colin F Camerer, Anna Dreber, Juergen Huber, Magnus Johannesson, et al. Nature 582(7810): 84–88. doi: 10.1038/s41586-020-2314-9.
	Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations Stephen M Smith, Lloyd T Elliott, Fidel Alfaro-Almagro, Paul McCarthy, Thomas E Nichols, Gwenaëlle Douaud, et al. eLife: 1–28. doi: 10.7554/eLife.52677.
	Guidelines for the content and format of PET brain data in publications and archives: A consensus paper Gitte M Knudsen, Melanie Ganz, Stefan Appelhoff, Ronald Boellaard, Guy Bormans, Richard E Carson, et al. Journal of Cerebral Blood Flow & Metabolism 40(8): 1576–1585. doi: 10.1177/0271678X20905433.
	Selective peak inference: Unbiased estimation of raw and standardized effect size at local maxima Samuel Davenport and Thomas E Nichols. NeuroImage 209(November 2019): 116375. doi: 10.1016/j.neuroimage.2019.116375.
2019	Articles Genomic kinship construction to enhance genetic analyses in the human connectome project data Peter Kochunov, Brian Donohue, Braxton D. Mitchell, Habib Ganjgahi, Bhim Adhikari, Meghann Ryan, et al. Human Brain Mapping 40(5): 1677–1688. doi: 10.1002/hbm.24479.
	Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation Soroosh Afyouni, Stephen M. Smith, and Thomas E. Nichols. NeuroImage 199(May): 609–625. doi: 10.1016/j.neuroimage.2019.05.011.
	Exploring the impact of analysis software on task fMRI results Alexander Bowring, Camille Maumet, and Thomas E. Nichols. Human Brain Mapping 40(11): 3362–3384. doi: 10.1002/hbm.24603.
	Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706) Bingxin Zhao, Jingwen Zhang, Joseph G. Ibrahim, Tianyou Luo, Rebecca C. Santelli, Yun Li, et al. Molecular Psychiatry. doi: 10.1038/s41380-019-0569-z.
	Discovering markers of healthy aging: a prospective study in a Danish male birth cohort Kiyana Zarnani, Thomas E Nichols, Fidel Alfaro-Almagro, Birgitte Fagerlund, Martin Lauritzen, Egill Rostrup, et al. Aging 11(16): 5943–5974. doi: 10.18632/aging.102151.
	Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference Vaanathi Sundaresan, Ludovica Griffanti, Petya Kindalova, Fidel Alfaro-Almagro, Giovanna Zamboni, Peter M. Rothwell, et al. NeuroImage 185(September 2018): 434–445. doi: 10.1016/j.neuroimage.2018.10.042.
	Towards algorithmic analytics for large-scale datasets Danilo Bzdok, Thomas E Nichols, and Stephen M Smith. Nature Machine Intelligence 1(7): 296–306. doi: 10.1038/s42256-019-0069-5.
	Accelerated estimation and permutation inference for ACE modeling Xu Chen, Elia Formisano, Gabriëlla A. M. Blokland, Lachlan T. Strike, Katie L. McMahon, Greig I. Zubicaray, et al. Human Brain Mapping 40(12): 3488–3507. doi: 10.1002/hbm.24611.
	Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level Stephen Smith, Eugene Duff, Adrian Groves, Thomas E Nichols, Saad Jbabdi, Lars T. Westlye, et al. The Journal of Neuroscience 39(31): 6136–6149. doi: 10.1523/JNEUROSCI.2912-18.2019.
	Stable between-subject statistical inference from unstable within-subject functional connectivity estimates Diego Vidaurre, Mark W. Woolrich, Anderson M. Winkler, Theodoros Karapanagiotidis, Jonathan Smallwood, and Thomas E. Nichols. Human Brain Mapping 40(4): 1234–1243. doi: 10.1002/hbm.24442.
	Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data Peter Kochunov, Binish Patel, Habib Ganjgahi, Brian Donohue, Meghann Ryan, Elliot L. Hong, et al. Frontiers in Neuroinformatics 13(March): 1–11. doi: 10.3389/fninf.2019.00016.
	Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests Anders Eklund, Hans Knutsson, and Thomas E. Nichols. Human Brain Mapping 40(5): 1689–1691. doi: 10.1002/hbm.24465.
	Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates Anders Eklund, Hans Knutsson, and Thomas E. Nichols. Human Brain Mapping 40(7): 2017–2032. doi: 10.1002/hbm.24350.
	MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry Kerstin Bendfeldt, Bernd Taschler, Laura Gaetano, Philip Madoerin, Pascal Kuster, Nicole Mueller-Lenke, et al. Brain Imaging and Behavior 13(5): 1361–1374. doi: 10.1007/s11682-018-9942-9.
	Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power Tamás Spisák, Zsófia Spisák, Matthias Zunhammer, Ulrike Bingel, Stephen Smith, Thomas Nichols, et al. NeuroImage: 12–26. doi: 10.1016/j.neuroimage.2018.09.078.
	A group analysis using the Multiregression Dynamic Models for fMRI networked time series Lilia Costa, Jim Q. Smith, and Thomas E. Nichols. Journal of Statistical Planning and Inference: 43–61. doi: https://doi.org/10.1016/j.jspi.2018.03.004.
2018	Preprints Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates Anders Eklund, Hans Knutsson, and Thomas E. Nichols. Technical Report . doi: 10.1101/296798.
	Articles Combining multi-modality data for searching biomarkers in schizophrenia Shuixia Guo, Chu-Chung Huang, Wei Zhao, Albert C. Yang, Ching-Po Lin, Thomas Nichols, et al. PLOS ONE 13(2): e0191202. doi: 10.1371/journal.pone.0191202.
	Ten simple rules for neuroimaging meta-analysis. Veronika I. Müller, Edna C. Cieslik, Angela R. Laird, Peter T. Fox, Joaquim Radua, David Mataix-Cols, et al. Neuroscience and biobehavioral reviews: 151–161. doi: 10.1016/j.neubiorev.2017.11.012.
	Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects Michael P. Harms, Leah H. Somerville, Beau M. Ances, Jesper Andersson, Deanna M. Barch, Matteo Bastiani, et al. NeuroImage 183(September): 972–984. doi: 10.1016/j.neuroimage.2018.09.060.
	Beyond Bonferroni Revisited: Concerns over inflated false positives in conservation genetics, genetics, and neuroscience Tonya White, Jan Ende, and Thomas E. Nichols. bioRxiv 31(0): 408997. doi: 10.1101/408997.
	Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline. Bhim M Adhikari, Neda Jahanshad, Dinesh Shukla, David C Glahn, John Blangero, Richard C Reynolds, et al. Pacific Symposium on Biocomputing: 307–318.
	Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. Philip A. Kragel, Michiko Kano, Lukas Van Oudenhove, Huynh Giao Ly, Patrick Dupont, Amandine Rubio, et al. Nature neuroscience 21(2): 283–289. doi: 10.1038/s41593-017-0051-7.
	Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline Bhim M Adhikari, Neda Jahanshad, Dinesh Shukla, David C Glahn, John Blangero, Peter T Fox, et al. Human Brain Mapping 39(12): 4893–4902. doi: 10.1002/hbm.24331.
	Unravelling the GSK3β-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder Becky Inkster, Andy Simmons, James H. Cole, Erwin Schoof, Rune Linding, Tom Nichols, et al. Psychiatric Genetics 28(5): 77–84. doi: 10.1097/YPG.0000000000000203.
	The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds Leah H. Somerville, Susan Y. Bookheimer, Randy L. Buckner, Gregory C. Burgess, Sandra W. Curtiss, Mirella Dapretto, et al. NeuroImage 183(July): 456–468. doi: 10.1016/j.neuroimage.2018.08.050.
	Making replication prestigious Krzysztof J. Gorgolewski, Thomas E. Nichols, David N. Kennedy, Jean-Baptiste Poline, and Russell A. Poldrack. Behavioral and Brain Sciences: e131. doi: 10.1017/S0140525X18000663.
	Directed functional connectivity using dynamic graphical models Simon Schwab, Ruth Harbord, Valerio Zerbi, Lloyd Elliott, Soroosh Afyouni, Jim Q. Smith, et al. NeuroImage: 340 - 353. doi: https://doi.org/10.1016/j.neuroimage.2018.03.074.
	Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies Samartsidis Pantelis, Eickhoff Claudia R., Eickhoff Simon B., Wager Tor D., Barrett Lisa Feldman, Atzil Shir, et al. Journal of the Royal Statistical Society: Series C (Applied Statistics) 0(0). doi: 10.1111/rssc.12295.
	Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Brian Donohue, Peter Kochunov, et al. Nature Communications 9(1): 3254. doi: 10.1038/s41467-018-05444-6.
2017	Preprints Bayesian log-Gaussian Cox process regression with applications to fMRI meta-analysis Pantelis Samartsidis, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Simon B. Eickhoff, Thomas E. Nichols, et al. Technical Report arXiv.
	Articles A defense of using resting-state fMRI as null data for estimating false positive rates. Thomas E. Nichols, Anders Eklund, and Hans Knutsson. Cognitive neuroscience 8(3): 144–149. doi: 10.1080/17588928.2017.1287069.
	Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications. Chenyang Tao, Thomas E. Nichols, Xue Hua, Christopher R K Ching, Edmund T. Rolls, Paul M. Thompson, et al. NeuroImage 144(Pt A): 35–57. doi: 10.1016/j.neuroimage.2016.08.027.
	Scanning the horizon: towards transparent and reproducible neuroimaging research. Russell A Poldrack, Chris I Baker, Joke Durnez, Krzysztof J Gorgolewski, Paul M Matthews, Marcus R Munafò, et al. Nature reviews. Neuroscience. doi: 10.1038/nrn.2016.167.
	Best practices in data analysis and sharing in neuroimaging using MRI Thomas E. Nichols, Samir Das, Simon B. Eickhoff, Alan C. Evans, Tristan Glatard, Michael Hanke, et al. Nature Neuroscience 20(3): 299–303. doi: 10.1038/nn.4500.
2016	Preprints Best Practices in Data Analysis and Sharing in Neuroimaging using MRI Thomas E. Nichols, Samir Das, Simon B. Eickhoff, Alan C. Evans, Tristan Glatard, Michael Hanke, et al. Technical Report bioRxiv: 054262. doi: 10.1101/054262.
	Minimal Data Needed for Valid & Accurate Image-Based fMRI Meta-Analysis Camille Maumet and Thomas E Nichols. Technical Report bioRxiv: 1–9. doi: 10.1101/048249.
	Power and sample size calculations for fMRI studies based on the prevalence of active peaks. Joke Durnez, Jasper Degryse, Beatrijs Moerkerke, Ruth Seurinck, Vanessa Sochat, Russell A Poldrack, et al. Technical Report bioRxiv: 0–23. doi: 10.1101/049429.
	Articles The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects. Peter Kochunov, Paul M. Thompson, Anderson Winkler, Mary Morrissey, Mao Fu, Thomas R. Coyle, et al. NeuroImage: 189–97. doi: 10.1016/j.neuroimage.2015.10.050.
	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 of the United States of America 113(28): 7900–5. doi: 10.1073/pnas.1602413113.
	Exploring fMRI Results Space: 31 Variants of an fMRI Analysis in AFNI, FSL, and SPM Ruth Pauli, Alexander Bowring, Richard Reynolds, Gang Chen, Thomas E. Nichols, and Camille Maumet. Frontiers in Neuroinformatics 10(July): 24. doi: 10.3389/fninf.2016.00024.
	Novel genetic loci underlying human intracranial volume identified through genome-wide association Hieab H H Adams, Derrek P Hibar, Vincent Chouraki, Jason L Stein, Paul A Nyquist, Miguel E Rentería, et al. Nature Neuroscience. doi: 10.1038/nn.4398.
	The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Krzysztof J Gorgolewski, Tibor Auer, Vince D Calhoun, R Cameron Craddock, Samir Das, Eugene P Duff, et al. Scientific data: 160044. doi: 10.1038/sdata.2016.44.
	Non-parametric combination and related permutation tests for neuroimaging. Anderson M. Winkler, Matthew A. Webster, Jonathan C. Brooks, Irene Tracey, Stephen M. Smith, and Thomas E. Nichols. Human brain mapping: n/a­-n/a. doi: 10.1002/hbm.23115.
	Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept Barbara Franke, Jason L Stein, Stephan Ripke, Verneri Anttila, Derrek P Hibar, Kimm J E Hulzen, et al. Nature neuroscience. doi: 10.1038/nn.4228.
	NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. Krzysztof J. Gorgolewski, Gael Varoquaux, Gabriel Rivera, Yannick Schwartz, Vanessa V. Sochat, Satrajit S. Ghosh, et al. NeuroImage 124(Pt B): 1242–4. doi: 10.1016/j.neuroimage.2015.04.016.
	Sharing the wealth: Neuroimaging data repositories. Simon B. Eickhoff, Thomas E. Nichols, John D. Van Horn, and Jessica A. Turner. NeuroImage 124(Pt B): 1065–8. doi: 10.1016/j.neuroimage.2015.10.079.
	Seeking optimal region-of-interest (ROI) single-value summary measures for fMRI studies in imaging genetics Yunxia Tong, Qiang Chen, Thomas E. Nichols, Roberta Rasetti, Joseph H. Callicott, Karen F. Berman, et al. PLoS ONE 11(3): 1–20. doi: 10.1371/journal.pone.0151391.
	Sharing brain mapping statistical results with the neuroimaging data model. Camille Maumet, Tibor Auer, Alexander Bowring, Gang Chen, Samir Das, Guillaume Flandin, et al. Scientific data: 160102. doi: 10.1038/sdata.2016.102.
	Faster permutation inference in brain imaging Anderson M. Winkler, Gerard R. Ridgway, Gwenaëlle Douaud, Thomas E. Nichols, and Stephen M. Smith. NeuroImage: 502–516. doi: 10.1016/j.neuroimage.2016.05.068.
	Novel genetic loci associated with hippocampal volume Mohammad Arfan Ikram, Derrek Hibar, Hieab Adams, Neda Jahanshad, Ganesh Chauhan, Jason Stein, et al. Nature Communications. doi: 10.1038/ncomms13624.
	Correction for Eklund et al., 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 of the United States of America: 201612033. doi: 10.1073/pnas.1612033113.
	Books & Chapters Statistical Analysis of fMRI Data Mark W. Woolrich, Christian F. Beckmann, Thomas E. Nichols, and Stephen M. Smith. Statistical Analysis of fMRI Data: 183–239. doi: 10.1007/978-1-4939-5611-1_7.
2015	Articles Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing Kerstin Bendfeldt, Renata Smieskova, Nikolaos Koutsouleris, Stefan Klöppel, André Schmidt, Anna Walter, et al. NeuroImage: Clinical: 555–563. doi: 10.1016/j.nicl.2015.09.015.
	Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. Peter Kochunov, Neda Jahanshad, Daniel Marcus, Anderson M. Winkler, Emma Sprooten, Thomas E. Nichols, et al. NeuroImage: 300–11. doi: 10.1016/j.neuroimage.2015.02.050.
	A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Stephen M Smith, Thomas E Nichols, Diego Vidaurre, Anderson M. Winkler, Timothy E J Behrens, Matthew F Glasser, et al. Nature neuroscience 18(11): 1565–7. doi: 10.1038/nn.4125.
	Multi-level block permutation. Anderson M. Winkler, Matthew a. Webster, Diego Vidaurre, Thomas E. Nichols, and Stephen M. Smith. NeuroImage: 253–68. doi: 10.1016/j.neuroimage.2015.05.092.
	Age differences in the brain mechanisms of good taste Edmund T Rolls, Michele B Kellerhals, and Thomas E Nichols. NeuroImage: 298–309. doi: 10.1016/j.neuroimage.2015.03.065.
	A voxelation-corrected non-stationary 3D cluster-size test based on random field theory. Huanjie Li, Lisa D. Nickerson, Xuna Zhao, Thomas E. Nichols, and Jia-Hong Gao. NeuroImage. doi: 10.1016/j.neuroimage.2015.05.094.
	NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Krzysztof J. Gorgolewski, Gael Varoquaux, Gabriel Rivera, Yannick Schwarz, Satrajit S. Ghosh, Camille Maumet, et al. Frontiers in neuroinformatics 9(April): 8. doi: 10.3389/fninf.2015.00008.
	A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application. Tian Ge, Thomas E Nichols, Debashis Ghosh, Elizabeth C Mormino, Jordan W Smoller, and Mert R Sabuncu. NeuroImage: 505–514. doi: 10.1016/j.neuroimage.2015.01.029.
	Common genetic variants influence human subcortical brain structures. Derrek P. Hibar, Jason L. Stein, Miguel E. Renteria, Alejandro Arias-Vasquez, Sylvane Desrivières, Neda Jahanshad, et al. Nature 520(7546): 224–9. doi: 10.1038/nature14101.
	Searching Multiregression Dynamic Models of Resting-State fMRI Networks Using Integer Programming Lilia Costa, Jim Smith, Thomas Nichols, James Cussens, Eugene P. Duff, Tamar R. Makin, et al. Bayesian Analysis 10(2): 441–478. doi: 10.1214/14-BA913.
	Fast and powerful heritability inference for family-based neuroimaging studies. Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Peter Kochunov, and Thomas E. Nichols. NeuroImage: 256–68. doi: 10.1016/j.neuroimage.2015.03.005.
	ENIGMA and the Individual: Predicting Factors that Affect the Brain in 35 Countries Worldwide. Paul M. Thompson, Ole A. Andreassen, Alejandro Arias-Vasquez, Carrie E. Bearden, Premika S. Boedhoe, Rachel M. Brouwer, et al. NeuroImage. doi: 10.1016/j.neuroimage.2015.11.057.
	Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise. Adam G. Thomas, Andrea Dennis, Nancy B. Rawlings, Charlotte J. Stagg, Lucy Matthews, Martyn Morris, et al. NeuroImage. doi: 10.1016/j.neuroimage.2015.10.090.
	FVGWAS : Fast Voxelwise Genome Wide Association Analysis of Large-scale Imaging Genetic Data Meiyan Huang, Thomas Nichols, Chao Huang, Yu Yang, Zhaohua Lu, Qianjing Feng, et al. NeuroImage: 48. doi: 10.1016/j.neuroimage.2015.05.043.
	An Ultra-High Field Magnetic Resonance Spectroscopy Study of Post Exercise Lactate, Glutamate and Glutamine Change in the Human Brain Andrea Dennis, Adam G. Thomas, Nancy B. Rawlings, Jamie Near, Thomas E. Nichols, Stuart Clare, et al. Frontiers in Physiology 6(December). doi: 10.3389/fphys.2015.00351.
	Medial Demons Registration Localizes The Degree of Genetic Influence Over Subcortical Shape Variability: An N= 1480 Meta-Analysis. Boris A Gutman, Neda Jahanshad, Christopher R K Ching, Yalin Wang, Peter V Kochunov, Thomas E Nichols, et al. Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging: 1402–1406. doi: 10.1109/ISBI.2015.7164138.
	Massively expedited genome-wide heritability analysis (MEGHA). Tian Ge, Thomas E. Nichols, Phil H. Lee, Avram J. Holmes, Joshua L. Roffman, Randy L. Buckner, et al. Proceedings of the National Academy of Sciences of the United States of America 112(8): 2479–84. doi: 10.1073/pnas.1415603112.
	A Bayesian Model of Category-Specific Emotional Brain Responses Tor D. Wager, Jian Kang, Timothy D. Johnson, Thomas E. Nichols, Ajay B. Satpute, and Lisa Feldman Barrett. PLOS Computational Biology: e1004066. doi: 10.1371/journal.pcbi.1004066.
	In Proceedings Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis Anders Eklund, Thomas Nichols, Mats Andersson, and Hans Knutsson. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI): 1376–1380. doi: 10.1109/ISBI.2015.7164132.
2014	Preprints STATISTICAL RESEARCH AND TRAINING UNDER THE BRAIN INITIATIVE Robert Kass, Genevera Allen, Brian Caffo, John Cunningham, Uri Eden, Timothy D Johnson, et al. Technical Report ASA White Paper: 1–10.
	Articles Post-hoc power estimation for topological inference in fMRI. Joke Durnez, Beatrijs Moerkerke, and Thomas E Nichols. NeuroImage: 45–64. doi: 10.1016/j.neuroimage.2013.07.072.
	Multiple comparison procedures for neuroimaging genomewide association studies. Wen-Yu Hua, Thomas E. Nichols, and Debashis Ghosh. Biostatistics: 1–14. doi: 10.1093/biostatistics/kxu026.
	Permutation inference for the general linear model. Anderson M. Winkler, Gerard R Ridgway, Matthew A Webster, Stephen M Smith, and Thomas E Nichols. NeuroImage: 381–97. doi: 10.1016/j.neuroimage.2014.01.060.
	Joint genetic analysis of hippocampal size in mouse and human identifies a novel gene linked to neurodegenerative disease. David G Ashbrook, Robert W Williams, Lu Lu, Jason L Stein, Derrek P Hibar, Thomas E Nichols, et al. BMC genomics 15(1): 850. doi: 10.1186/1471-2164-15-850.
	Fast and accurate modelling of longitudinal and repeated measures neuroimaging data. Bryan Guillaume, Xue Hua, Paul M. Thompson, Lourens Waldorp, and Thomas E. Nichols. NeuroImage: 287–302. doi: 10.1016/j.neuroimage.2014.03.029.
	Development of impulse control circuitry in children of alcoholics. Jillian E. Hardee, Barbara J. Weiland, Thomas E. Nichols, Robert C. Welsh, Mary E. Soules, Davia B. Steinberg, et al. Biological psychiatry 76(9): 708–16. doi: 10.1016/j.biopsych.2014.03.005.
	Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease. Arash Nazeri, Habib Ganjgahi, Tina Roostaei, Thomas Nichols, and Mojtaba Zarei. NeuroImage: 657–65. doi: 10.1016/j.neuroimage.2014.08.041.
	Stochastic Blockmodeling of the Modules and Core of the Caenorhabditis elegans Connectome Dragana M. Pavlovic, Petra E. Vértes, Edward T. Bullmore, William R. Schafer, and Thomas E. Nichols. PLoS ONE 9(7): e97584. doi: 10.1371/journal.pone.0097584.
	Progression in disability and regional grey matter atrophy in relapsing-remitting multiple sclerosis. Louis Hofstetter, Yvonne Naegelin, Lukas Filli, Pascal Kuster, Stefan Traud, Renata Smieskova, et al. Multiple sclerosis 20(2): 202–13. doi: 10.1177/1352458513493034.
	Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling. Peter Kochunov, Neda Jahanshad, Emma Sprooten, Thomas E Nichols, René C Mandl, Laura Almasy, et al. NeuroImage: 136–50. doi: 10.1016/j.neuroimage.2014.03.033.
	Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines Mary Ellen Koran, Tricia a Thornton-Wells, Neda Jahanshad, David C Glahn, Paul M Thompson, John Blangero, et al. Journal of medical imaging (Bellingham, Wash.) 1(1): 14005. doi: 10.1117/1.JMI.1.1.014005.
	Corrigendum to "Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group" [NeuroImage 81 (2013) 455-469] Neda Jahanshad, Peter V. Kochunov, Emma Sprooten, René C. Mandl, Thomas E. Nichols, Laura Almasy, et al. NeuroImage: 470–471. doi: 10.1016/j.neuroimage.2013.12.053.
	Toward a Multisubject Analysis of Neural Connectivity. C J Oates, L Costa, and T E Nichols. Neural computation: 1–20. doi: 10.1162/NECO_a_00690.
	Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. C R Pernet, M Latinus, T E Nichols, and G a Rousselet. Journal of neuroscience methods: 1–9. doi: 10.1016/j.jneumeth.2014.08.003.
	Global Genetic Variations Predict Brain Response to Faces. Erin W Dickie, Amir Tahmasebi, Leon French, Natasa Kovacevic, Tobias Banaschewski, Gareth J Barker, et al. PLoS genetics 10(8): e1004523. doi: 10.1371/journal.pgen.1004523.
	On study design in neuroimaging heritability analyses Mary Ellen Koran, Bo Li, Neda Jahanshad, Tricia a. Thornton-Wells, David C. Glahn, Paul M. Thompson, et al. Proc. SPIE 9034(615): 90342P–6. doi: 10.1117/12.2043565.
	Modelling the Penumbra in Computed Tomography Audrey Kueh, Jason W Warnett, Gregory Gibbons, Julia Brettschneider, Thomas E Nichols, Mark A WIlliams, et al. CRiSM Working Paper Series 14(25): 1–19. doi: 10.3233/XST-160576.
	The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Paul M Thompson, Jason L Stein, Sarah E Medland, Derrek P Hibar, Alejandro Arias Vasquez, Miguel E Renteria, et al. Brain imaging and behavior 8(2): 153–82. doi: 10.1007/s11682-013-9269-5.
	Analysis Of Multiple Sclerosis Lesions Via Spatially Varying Coefficients Tian Ge, Nicole Müller-Lenke, Kerstin Bendfeldt, Thomas E. Nichols, and Timothy D Johnson. The annals of applied statistics 8(2): 1095–1118. doi: 10.1214/14-AOAS718.
	Books & Chapters Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging Neda Jahanshad, Peter Kochunov, David C. Glahn, John Blangero, Thomas E. Nichols, Katie L. McMahon, et al. Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging: 229–238. doi: 10.1007/978-3-319-02475-2_21.
	In Proceedings Spatial Modeling of Multiple Sclerosis for Disease Subtype Prediction Bernd Taschler, Tian Ge, Kerstin Bendfeldt, Nicole Müller-Lenke, Timothy D Johnson, and Thomas E Nichols. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 (Lecture Notes in Computer Science): 797–804. doi: 10.1007/978-3-319-10470-6_99.
2013	Articles Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group. Neda Jahanshad, Peter V Kochunov, Emma Sprooten, René C Mandl, Thomas E Nichols, Laura Almasy, et al. NeuroImage: 455–69. doi: 10.1016/j.neuroimage.2013.04.061.
	Dynamic filtering of static dipoles in magnetoencephalography Alberto Sorrentino, Adam M. Johansen, John a. D. Aston, Thomas E. Nichols, and Wilfrid S. Kendall. The Annals of Applied Statistics 7(2): 955–988. doi: 10.1214/12-AOAS611.
	A Bayesian non-parametric Potts model with application to pre-surgical FMRI data. Timothy D Johnson, Zhuqing Liu, Andreas J Bartsch, and Thomas E. Nichols. Statistical methods in medical research 22(4): 364–81. doi: 10.1177/0962280212448970.
	Transcriptomics of cortical gray matter thickness decline during normal aging. P Kochunov, J Charlesworth, Anderson M. Winkler, L E Hong, T Nichols, J E Curran, et al. NeuroImage. doi: 10.1016/j.neuroimage.2013.05.066.
	Genetics of the connectome. Paul M. Thompson, Tian Ge, David C. Glahn, Neda Jahanshad, and Thomas E. Nichols. NeuroImage: 475–88. doi: 10.1016/j.neuroimage.2013.05.013.
	Preventing Alzheimer's disease-related gray matter atrophy by B-vitamin treatment. Gwenaëlle Douaud, Helga Refsum, Celeste a. Jager, Robin Jacoby, Thomas E Nichols, Stephen M Smith, et al. Proceedings of the National Academy of Sciences of the United States of America 110(23): 9523–8. doi: 10.1073/pnas.1301816110.
	Functional connectomics from resting-state fMRI. Stephen M Smith, Diego Vidaurre, Christian F Beckmann, Matthew F Glasser, Mark Jenkinson, Karla L Miller, et al. Trends in cognitive sciences 17(12): 666–82. doi: 10.1016/j.tics.2013.09.016.
	Alternative-based thresholding with application to presurgical fMRI. Joke Durnez, Beatrijs Moerkerke, Andreas Bartsch, and Thomas E. Nichols. Cognitive, affective & behavioral neuroscience 13(4): 703–13. doi: 10.3758/s13415-013-0185-3.
	A Bayesian Hierarchical Spatial Point Process Model for Multi-Type Neuroimaging Meta-Analysis Jian Kang, Thomas E. Nichols, Tor D. Wager, and Timothy D. Johnson. The Annals of Applied Statistics 8(3): 1800–1824. doi: 10.1214/14-AOAS757.
2012	Articles A Comparison of Gray Matter Density in Restless Legs Syndrome Patients and Matched Controls Using Voxel-Based Morphometry. Robert a Comley, Simon Cervenka, Sven E Palhagen, Georgios Panagiotidis, Julian C Matthews, Robert Y.K. Lai, et al. Journal of neuroimaging : official journal of the American Society of Neuroimaging 22(1): 28–32. doi: 10.1111/j.1552-6569.2010.00536.x.
	Spatiotemporal distribution of white matter lesions in relapsing-remitting and secondary progressive multiple sclerosis. Lukas Filli, Louis Hofstetter, Pascal Kuster, Stefan Traud, Nicole Mueller-Lenke, Yvonne Naegelin, et al. Multiple sclerosis 18(11): 1577–84. doi: 10.1177/1352458512442756.
	Multiple testing corrections, nonparametric methods, and random field theory. Thomas E. Nichols. NeuroImage 62(2): 811–5. doi: 10.1016/j.neuroimage.2012.04.014.
	Multivariate pattern classification of gray matter pathology in multiple sclerosis. Kerstin Bendfeldt, Stefan Klöppel, Thomas E. Nichols, Renata Smieskova, Pascal Kuster, Stefan Traud, et al. NeuroImage 60(1): 400–8. doi: 10.1016/j.neuroimage.2011.12.070.
	Measuring and comparing brain cortical surface area and other areal quantities. Anderson M. Winkler, Mert R Sabuncu, B T Thomas Yeo, Bruce Fischl, Douglas N Greve, Peter Kochunov, et al. NeuroImage 61(4): 1428–43. doi: 10.1016/j.neuroimage.2012.03.026.
	The danger of systematic bias in group-level FMRI-lag-based causality estimation. Stephen M. Smith, Peter A. Bandettini, K L Miller, Timothy E.J. Behrens, Karl J. Friston, O David, et al. NeuroImage 59(2): 1228–9. doi: 10.1016/j.neuroimage.2011.08.015.
	Increasing power for voxel-wise genome-wide association studies: the random field theory, least square kernel machines and fast permutation procedures. Tian Ge, Jianfeng Feng, Derrek P Hibar, Paul M. Thompson, and Thomas E. Nichols. NeuroImage 63(2): 858–73. doi: 10.1016/j.neuroimage.2012.07.012.
	Identification of common variants associated with human hippocampal and intracranial volumes. Jason L Stein, Sarah E Medland, Alejandro Arias Vasquez, Derrek P Hibar, Rudy E Senstad, Anderson M. Winkler, et al. Nature genetics 44(5): 552–61. doi: 10.1038/ng.2250.
	Identification of common variants associated with human hippocampal and intracranial volumes. Jason L Stein, Sarah E Medland, Alejandro Arias Vasquez, Derrek P Hibar, Rudy E Senstad, Anderson M. Winkler, et al. Nature genetics 44(5): 552–61. doi: 10.1038/ng.2250.
2011	Articles False positives in neuroimaging genetics using voxel-based morphometry data. Matt Silver, Giovanni Montana, Thomas E. Nichols, and Alzheimer's Disease Neuroimaging Initiative. NeuroImage 54(2): 992–1000. doi: 10.1016/j.neuroimage.2010.08.049.
	Using Gaussian-Process Regression for Meta-analytic Neuroimaging Inference Based on Sparse Observations. Gholamreza Salimi-Khorshidi, Thomas E. Nichols, Stephen M. Smith, and Mark W. Woolrich. IEEE Transactions on Medical Imaging 30(7): 1401–16. doi: 10.1109/TMI.2011.2122341.
	Large-scale automated synthesis of human functional neuroimaging data Tal Yarkoni, Russell A. Poldrack, Thomas E. Nichols, David C. Van Essen, and Tor D. Wager. Nature Methods 8(8): 665–670. doi: 10.1038/nMeth.1635.
	A hybrid procedure for detecting global treatment effects in multivariate clinical trials: theory and applications to fMRI studies. Giorgos Minas, Fabio Rigat, Thomas E. Nichols, John a D Aston, and Nigel Stallard. Statistics in medicine 31(3): 253–268. doi: 10.1002/sim.4395.
	Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms. Becky Inkster, Anil W Rao, Khanum Ridler, Thomas E. Nichols, Philipp G. Saemann, Dorothee P. Auer, et al. Journal of neuroimaging : official journal of the American Society of Neuroimaging 21(4): 375–82. doi: 10.1111/j.1552-6569.2010.00515.x.
	Brain Network Analysis: Separating Cost from Topology Using Cost-Integration Cedric E. Ginestet, Thomas E. Nichols, Edward T. Bullmore, and Andrew Simmons. PLoS ONE 6(7): e21570. doi: 10.1371/journal.pone.0021570.
	Genetic analysis of cortical thickness and fractional anisotropy of water diffusion in the brain Peter Kochunov, David C. Glahn, Thomas E. Nichols, Anderson M. Winkler, Elliot L. Hong, Henry H Holcomb, et al. Frontiers in neuroscience 5(October): 1–15. doi: 10.3389/fnins.2011.00120.
	Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes Jian Kang, Timothy D Johnson, Thomas E. Nichols, and Tor D. Wager. Journal of the American Statistical Association 106(493): 124–134. doi: 10.1198/jasa.2011.ap09735.
	Orbitofrontal Connectivity with Resting-State Networks Is Associated with Midbrain Dopamine D3 Receptor Availability. David M Cole, Christian F. Beckmann, Graham E Searle, Christophe Plisson, Andri C Tziortzi, Thomas E. Nichols, et al. Cerebral cortex. doi: 10.1093/cercor/bhr354.
	Network Modelling Methods for FMRI Stephen M. Smith, Karla L. Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F. Beckmann, Thomas E. Nichols, et al. NeuroImage 54(2): 875–891. doi: 10.1016/j.neuroimage.2010.08.063.
	Books & Chapters Handbook of fMRI Data Analysis Russell A. Poldrack, Jeanette A. Mumford, and Thomas E. Nichols. Unknown venue (type=book): 450.
	Books & Chapters Validation and Pilot Application of [18F]FDG-PET in Evaluation of a Metabolic Therapy for Alzheimer's Disease Sofia Tzimopoulou, Vincent J. Cunningham, Thomas E. Nichols, Graham Searle, Nick P. Bird, Prafull Mistry, et al. Validation and Pilot Application of [18F]FDG-PET in Evaluation of a Metabolic Therapy for Alzheimer's Disease: 627–642. doi: 10.3233/978-1-60750-793-2-627.
2010	Articles Adjusting the effect of nonstationarity in cluster-based and TFCE inference. Gholamreza Salimi-Khorshidi, Stephen M. Smith, and Thomas E. Nichols. NeuroImage 54(3): 2006–2019. doi: 10.1016/j.neuroimage.2010.09.088.
	Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. Maria Vounou, Thomas E. Nichols, and Giovanni Montana. NeuroImage 53(3): 1147–59. doi: 10.1016/j.neuroimage.2010.07.002.
	A Multi-Center Randomized Proof-of-Concept Clinical Trial Applying [18F]FDG-PET for Evaluation of Metabolic Therapy with Rosiglitazone XR in Mild to Moderate Alzheimer's Disease Sofia Tzimopoulou, Vincent J. Cunningham, Thomas E. Nichols, Graham Searle, Nick P. Bird, Prafull Mistry, et al. Journal of Alzheimer's disease 22(4): 1241–56.. doi: 10.3233/JAD-2010-100939.
	Everything you never wanted to know about circular analysis, but were afraid to ask. Nikolaus Kriegeskorte, Martin A. Lindquist, Thomas E. Nichols, Russell A. Poldrack, and Edward Vul. Journal of Cerebral Blood Flow & Metabolism 30(9): 1551–7. doi: 10.1038/jcbfm.2010.86.
	Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing-remitting multiple sclerosis. Kerstin Bendfeldt, Jan Ole Blumhagen, Hanspeter Egger, Patrick Loetscher, Niklaus Denier, Pascal Kuster, et al. Human brain mapping 000(October 2009). doi: 10.1002/hbm.20951.
	Effect of immunomodulatory medication on regional gray matter loss in relapsing-remitting multiple sclerosis–a longitudinal MRI study. Kerstin Bendfeldt, Hanspeter Egger, Thomas E. Nichols, Patrick Loetscher, Niklaus Denier, Pascal Kuster, et al. Brain research: 174–82. doi: 10.1016/j.brainres.2010.02.035.
	Pathway-based approaches to imaging genetics association studies: Wnt signaling, GSK3beta substrates and major depression. Becky Inkster, Thomas E. Nichols, Philipp G. Saemann, Dorothee P. Auer, Florian Holsboer, Pierandrea Muglia, et al. NeuroImage 53(3): 908–917. doi: 10.1016/j.neuroimage.2010.02.065.
2009	Articles Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies Gholamreza Salimi-Khorshidi, Stephen M. Smith, John R Keltner, Tor D. Wager, and Thomas E. Nichols. NeuroImage 45(3): 810–823. doi: 10.1016/j.neuroimage.2008.12.039.
	Evaluating the consistency and specificity of neuroimaging data using meta-analysis. Tor D. Wager, Martin A. Lindquist, Thomas E. Nichols, Hedy Kober, and Jared X Van Snellenberg. NeuroImage 45(1 Suppl): S210–21. doi: 10.1016/j.neuroimage.2008.10.061.
	Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference Stephen M. Smith and Thomas E. Nichols. NeuroImage 44(1): 83–98. doi: 10.1016/j.neuroimage.2008.03.061.
	Adjusting the neuroimaging statistical inferences for nonstationarity Gholamreza Salimi-Khorshidi, Stephen M. Smith, and Thomas E. Nichols. Lecture Notes in Computer Science 5761 LNCS(PART 1): 992–999. doi: 10.1007/978-3-642-04268-3_122.
	Striatal [11C] dihydrotetrabenazine and [11C] methylphenidate binding in Tourette syndrome R L Albin, R A Koeppe, K Wernette, W Zhuang, Thomas E. Nichols, M R Kilbourn, et al. Neurology: 1390–1396. doi: 10.1212/WNL.0b013e3181a187dd.
	Cluster mass inference via random field theory Hui Zhang, Thomas E. Nichols, and Timothy D Johnson. Neuroimage 44(1): 51–61. doi: 10.1016/j.neuroimage.2008.08.017.
	NeuroImage Anatomically-distinct genetic associations of APOE \epsilon 4 allele load with regional cortical atrophy in Alzheimer ' s disease Nicola Filippini, Anil Rao, Sally Wetten, Rachel A Gibson, Michael Borrie, Danilo Guzman, et al. NeuroImage 44(3): 724–728. doi: 10.1016/j.neuroimage.2008.10.003.
	Simple group fMRI modeling and inference. Jeanette A Mumford and Thomas E. Nichols. NeuroImage 47(4): 1469–75. doi: 10.1016/j.neuroimage.2009.05.034.
	Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model Lei Xu, Timothy D Johnson, Thomas E. Nichols, and Derek E Nee. Biometrics 65(4): 1041–1051. doi: 10.1111/j.1541-0420.2008.01190.x.
	Association of GSK3 Polymorphisms With Brain Structural Changes in Major Depressive Disorder Becky Inkster, Thomas E. Nichols, Philipp G. Saemann, Dorothee P. Auer, Florian Holsboer, Pierandrea Muglia, et al. Archives of general psychiatry 66(7): 721–728. doi: 10.1002/bdra.20744.
	Commentary on Vul et al.'s (2009) "Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition" Thomas E. Nichols and Jean-Baptiste Poline. Perspectives on Psychological Science 4(3): 291–293. doi: 10.1111/j.1745-6924.2009.01126.x.
2008	Articles Guidelines for reporting an fMRI study. Russell A. Poldrack, Paul C Fletcher, Richard N Henson, Keith J Worsley, Matthew Brett, and Thomas E. Nichols. NeuroImage 40(2): 409–14. doi: 10.1016/j.neuroimage.2007.11.048.
	Inferring individual differences in fMRI: finding brain regions with significant within subject correlation Sumitra Purkayastha, Tor D. Wager, and Thomas E. Nichols. Statistica Sinica: 1483–1500.
	Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation. Jeanette A Mumford and Thomas E. Nichols. NeuroImage 39(1): 261–8. doi: 10.1016/j.neuroimage.2007.07.061.
	Optimizing the design and analysis of clinical functional magnetic resonance imaging research studies. Cameron S Carter, Stephan Heckers, Thomas Nichols, Daniel S Pine, and Stephen Strother. Biological psychiatry 64(10): 842–9. doi: 10.1016/j.biopsych.2008.06.014.
2007	Articles Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Stephen M. Smith, Heidi Johansen-Berg, Mark Jenkinson, Daniel Rueckert, Thomas E. Nichols, Karla L Miller, et al. Nature protocols 2(3): 499–503. doi: 10.1038/nprot.2007.45.
	Books & Chapters False Discovery Rate procedures Thomas E. Nichols. False Discovery Rate procedures: 246–252.
	Non-parametric procedures Thomas E. Nichols and A Holmes. Non-parametric procedures: 253–272.
2006	Articles Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data Stephen M. Smith, Mark Jenkinson, Heidi Johansen-Berg, Daniel Rueckert, Thomas E. Nichols, Clare E Mackay, et al. Neuroimage 31(4): 1487–1505.
	Estimation efficiency and statistical power in arterial spin labeling fMRI Jeanette A Mumford, Luis Hernandez-Garcia, Gregory R Lee, and Thomas E. Nichols. NeuroImage: 103 – 114. doi: 10.1016/j.neuroimage.2006.05.040.
	Non-white noise in fMRI: does modelling have an impact? Torben E Lund, Kristoffer H Madsen, Karam Sidaros, Wen-Lin Luo, and Thomas E. Nichols. NeuroImage 29(1): 54–66. doi: 10.1016/j.neuroimage.2005.07.005.
	Modeling and inference of multisubject fMRI data Jeanette A Mumford and Thomas E. Nichols. IEEE Engineering in Medicine and Biology Magazine 25(2): 42–51. doi: 10.1109/MEMB.2006.1607668.
	Diagnosis of Single-Subject and Group fMRI Data with SPMd Hui Zhang, Wen-lin Luo, and Thomas E. Nichols. Human Brain Mapping: 442– 451. doi: 10.1002/hbm.20253.
	Lesion probability maps of white matter hyperintensities in elderly individuals: results of the Austrian stroke prevention study. Christian Enzinger, Stephen M. Smith, Franz Fazekas, Gunther Drevin, Stefan Ropele, Thomas E. Nichols, et al. Journal of neurology 253(8): 1064–70. doi: 10.1007/s00415-006-0164-5.
	Impact of complex genetic variation in COMT on human brain function. a Meyer-Lindenberg, Thomas E. Nichols, J H Callicott, J Ding, B Kolachana, J Buckholtz, et al. Molecular psychiatry 11(9): 867–77, 797. doi: 10.1038/sj.mp.4001860.
	Books & Chapters Statistical Parametric Mapping: The Analysis of Functional Brain Images William D. Penny, Karl J. Friston, John T. Ashburner, Stefan J. Kiebel, and Thomas E. Nichols. Unknown venue (type=book): 656.
2005	Articles Valid conjunction inference with the minimum statistic Thomas E. Nichols, Matthew Brett, Jesper Andersson, Tor D. Wager, and Jean-Baptiste Poline. Neuroimage 25(3): 653–660. doi: 10.1016/j.neuroimage.2004.12.005.
	A comparison of random field theory and permutation methods for the statistical analysis of MEG data Dimitrios Pantazis, Thomas E. Nichols, Sylvain Baillet, and Richard M. Leahy. NeuroImage: 383 – 394. doi: 10.1016/j.neuroimage.2004.09.040.
	Placebo effects mediated by endogenous opioid activity on mu-opioid receptors. Jon-Kar Zubieta, Joshua A Bueller, Lisa R Jackson, David J Scott, Yanjun Xu, Robert A Koeppe, et al. The Journal of Neuroscience 25(34): 7754–62. doi: 10.1523/JNEUROSCI.0439-05.2005.
	Linkage disequilibrium: ancient history drives the new genetics. Gonçalo R Abecasis, Debashis Ghosh, and Thomas E. Nichols. Human heredity 59(2): 118–24. doi: 10.1159/000085226.
2004	Articles Nonstationary cluster-size inference with random field and permutation methods Satoru Hayasaka, K Luan Phan, Israel Liberzon, Keith J Worsley, and Thomas E. Nichols. NeuroImage 22(2): 676 – 687. doi: 10.1016/j.neuroimage.2004.01.041.
	Combining voxel intensity and cluster extent with permutation test framework Satoru Hayasaka and Thomas E. Nichols. Neuroimage 23(1): 54–63. doi: 10.1016/j.neuroimage.2004.04.035.
	In Proceedings Statistical Surface-Based Morphometry Using a Non-parametric Approach Dimitrios Pantazis, Richard M. Leahy, Thomas E. Nichols, and Martin Styner. Proceedings IEEE International Symposium on Biomedical Imaging: 1283–1286. doi: 10.1109/ISBI.2004.1398780.
2003	Articles Switching attention and resolving interference: fMRI measures of executive functions C Y Sylvester, Tor D. Wager, Steven C Lacey, Luis Hernandez-Garcia, Thomas E. Nichols, E.E. Smith, et al. Neuropsychologia 41(3): 357–370. doi: 10.1016/S0028-3932(02)00167-7.
	Validating cluster size inference: random field and permutation methods. Satoru Hayasaka and Thomas E. Nichols. NeuroImage 20(4): 2343–56. doi: 10.1016/j.neuroimage.2003.08.003.
	Spatiotemporal Localization of Significant Activation in MEG using Permutation Tests Dimitrios Pantazis, Thomas E. Nichols, Sylvain Baillet, and Richard M. Leahy. Lecture Notes in Computer Science: 512–523. doi: 10.1007/978-3-540-45087-0_43.
	Temporal Resolution Properties of Dynamic PET Reconstructions Evren Asma, Thomas E. Nichols, Richard M. Leahy, and A Log-likelihood Functions. Proc. IEEE Nuclear Science Symposium and Medical Imaging Conference: 778–781. doi: 10.1109/NSSMIC.2002.1239438.
	Controlling the familywise error rate in functional neuroimaging: a comparative review Thomas E. Nichols and Satoru Hayasaka. Statistical Methods in Medical Research 12(5): 419–446. doi: 10.1191/0962280203sm341ra.
	Optimization of experimental design in fMRI: a general framework using a genetic algorithm Tor D. Wager and Thomas E. Nichols. Neuroimage 18(2): 293–309. doi: 10.1016/S1053-8119(02)00046-0.
	Diagnosis and exploration of massively univariate neuroimaging models Wen-Lin Luo and Thomas E. Nichols. NeuroImage 19(3): 1014–1032. doi: 10.1016/S1053-8119(03)00149-6.
	Books & Chapters Modules of Working Memory John Jonides, Ching-Yune C. Sylvester, Steven C. Lacey, Tor D. Wager, Thomas E. Nichols, and Edward Awh. Modules of Working Memory: 113–134. doi: 10.1007/978-3-0348-8030-5_7.
2002	Articles Statistical analysis of multiplex brain gene expression datasets A Ossadtchi, V M Brown, A H Khan, S R Cherry, Thomas E. Nichols, Richard M. Leahy, et al. Neurochemical Research 27(10): 1113–1121. doi: 10.1023/A:1020965107124.
	Gender differences in patterns of cerebral activation during equal experience of painful laser stimulation. S W G Derbyshire, Thomas E. Nichols, Anthony Jones, D. W. Townsend, and L Firestone. Journal of Pain 3(5): 4010–4411. doi: 10.1054/jpai.2002.126788.
	http://www.fil.ion.ucl.ac.uk/spm/doc/papers/NicholsQiAsmaLeahy.pdf Spatiotemporal Reconstruction of List Mode PET Data Thomas E. Nichols, J Qi, Evren Asma, and Richard M. Leahy. IEEE Transactions on Medical Imaging 21(4): 396–404. doi: 10.1109/TMI.2002.1000263.
	Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate Christopher R Genovese, Nicole A Lazar, and Thomas E. Nichols. NeuroImage 15(4): 870–878. doi: 10.1006/nimg.2001.1037.
	Nonparametric permutation tests for functional neuroimaging: A primer with examples Thomas E. Nichols and Andrew P Holmes. Human Brain Mapping 15(1): 1–25. doi: 10.1002/hbm.1058.
2001	Articles Activation of the Medial Prefrontal Cortex and Extended Amygdala by Individual Ratings of Emotional Arousal: An f\MRI\ Study K Luan Phan, Stephan F Taylor, R C Welsh, L R Decker, D C Noll, Thomas E. Nichols, et al. Biological Psychiatry. doi: 10.1016/S0006-3223(02)01485-3.
	4D PET Image Reconstruction from List Mode Data Evren Asma, Thomas E. Nichols, and Richard M. Leahy. Proc. IEEE Nuclear Science Symposium and Medical Imaging Conference: 15/57–15/61. doi: 10.1109/NSSMIC.2000.950049.
1999	Articles Statistical limitations in functional neuroimaging I. Non-inferential methods and statistical models Karl Magnus Petersson, Thomas E. Nichols, Jean-Baptiste Poline, and Andrew P Holmes. Philosophical Transactions of the Royal Society B: Biological Sciences: 1239–1260. doi: 10.1098/rstb.1999.0477.
	Changes in forebrain function from Waking to REM sleep in depression: Preliminary Analyses of [18F]-FDG PET studies E A Nofzinger, Thomas E. Nichols, D J Kupfer, and R Y Moore. Psychiatry Research: Neuroimaging 91(2): 59–78. doi: 10.1016/S0925-4927(99)00025-6.
	Statistical limitations in functional neuroimaging II. Signal detection and statistical inference Karl Magnus Petersson, Thomas E. Nichols, Jean-Baptiste Poline, and Andrew P Holmes. Philosophical Transactions of the Royal Society B: Biological Sciences 354(354): 1261–1281. doi: 10.1098/rstb.1999.0478.
	Comparative Evaluation of \MR\-Based Partial Volume Correction Schemes for \PET\ C C Meltzer, Paul E. Kinahan, P J Greer, Thomas E. Nichols, C Comtat, M N Cantwell, et al. Journal of Nuclear Medicine 40(12): 2053–2065.
	In Proceedings Continuous Time Dynamic PET Imaging Using List Mode Data Thomas E. Nichols, J Qi, and Richard M. Leahy. Information Processing in Medical Imaging (Lecture Notes in Computer Science): 98–111. doi: 10.1007/3-540-48714-X_8.
1998	Articles Holmes and Watson on 'Sherlock' Andrew P Holmes, J D G Watson, and Thomas E. Nichols. Journal of Cerebral Blood Flow & Metabolism 18(6): 697–698. doi: 10.1097/00004647-199806000-00012.
	Test-retest variability of positron emission tomography (PET) and [18F]-altanserin for the in vivo imaging of serotonin (5-HT2A) receptors in the human brain G S Smith, J C Price, B J Lopresti, Y Huang, N Simpson, D. Holt, et al. Synapse: 380–392. doi: 10.1002/(SICI)1098-2396(199812)30:4<380::AID-SYN5>3.0.CO;2-U.
1997	Articles PET brain mapping study of auditory verbal supraspan memory versus visual fixation in schizophrenia R Ganguli, C S Carter, Mark a Mintun, J Brar, J T Becker, R Sarma, et al. Biological Psychiatry 41(1): 33–42. doi: 10.1016/S0006-3223(96)00206-5.
	Anterior Cingulate Gyrus Dysfunction And Selective Attentional Dysfunction in Schizophrenia: An [15O]-H2O PET Study During Stroop Task Performance C S Carter, Mark a Mintun, Thomas E. Nichols, and Jonathan D Cohen. American Journal of Psychiatry 154(12): 1670–1675. doi: 10.1176/ajp.154.12.1670.
1996	Articles Compensatory reallocation of brain resources supporting verbal episodic memory in Alzheimer's disease J T Becker, Mark a Mintun, K Aleva, M B Wiseman, Thomas E. Nichols, and S T DeKosky. Neurology 46(3): 692–700. doi: 10.1212/WNL.46.3.692.
	Functional connectivity in auditory-verbal short-term memory in Alzheimer's disease A N Herbster, Thomas E. Nichols, M B Wiseman, Mark a Mintun, S T DeKosky, and J T Becker. NeuroImage 4(2): 67–77. doi: 10.1006/nimg.1996.0030.
	Anterior Cingulate Gyrus Dysfunction And Attentional Pathology in Schizophrenia C S Carter, Mark a Mintun, Jonathan D Cohen, Thomas E. Nichols, and M B Wiseman. NeuroImage 3(3,˜2/2): S477. doi: 10.1176/ajp.154.12.1670.
	Positron emission tomographic imaging of serotonin activation effects on prefrontal cortex in healthy volunteers J J Mann, K M Malone, D J Diehl, J Perel, Thomas E. Nichols, and Mark a Mintun. Journal of Cerebral Blood Flow & Metabolism 16(3): 418–426. doi: 10.1097/00004647-199605000-00008.
	Alterations in Functional Neuroanatomical Connectivity in Alzheimer's Disease - Positron Emission Tomography of Auditory Verbal Short-Term Memory J T Becker, Mark a Mintun, K Aleva, M B Wiseman, Thomas E. Nichols, and S T Dekosky. Annals of the New York Academy of Sciences 777(412): 239–242. doi: 10.1111/j.1749-6632.1996.tb34425.x.
	In Proceedings Laser stimulation for pain research Stuart Clark, Mark R. Dickinson, Terence A. King, Anthony Jones, Andrew Chen, Stuart Derbyshire, et al. Proceedings of SPIE: 197–205. doi: 10.1117/12.229532.
1995	Articles Comparison of Functional Magnetic Resonance Imaging with Positron Emission Tomography and Magnetoencephalography to Identify the Motor Cortex in a Patient with an Arteriovenous Malformation S B Baumann, D C Noll, D S Kondziolka, W Schneider, Thomas E. Nichols, Mark a Mintun, et al. Journal of Image Guided Surgery 1(4): 191–197. doi: 10.1002/(SICI)1522-712X(1995)1:4<191::AID-IGS1>3.0.CO;2-5.
1994	Articles Post-ECT increases in MRI regional T2 relaxation times and their relationship to cognitive side effects: a pilot study D J Diehl, M S Keshavan, E Kanal, R D Nebes, Thomas E. Nichols, and J S Gillen. Psychiatry Research 54(2): 177–184. doi: 10.1016/0165-1781(94)90005-1.