The impact of study design on pattern estimation for single-trial multivariate pattern analysis
Abstract
A prerequisite for a pattern analysis using functional magnetic resonance imaging (fMRI) data is estimating the patterns from time series data, which then are input into the pattern analysis. Here we focus on how the combination of study design (order and spacing of trials) with pattern estimator impacts the Type I error rate of the subsequent pattern analysis. When Type I errors are inflated, the results are no longer valid, so this work serves as a guide for designing and analyzing MVPA studies with controlled false positive rates. The MVPA strategies examined are pattern classification and similarity, utilizing single trial activation patterns from the same functional run. Primarily focusing on the Least Squares Single and Least Square All pattern estimators, we show that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses. It may seem intuitive that increasing the interstimulus interval (ISI) would alleviate this issue, but remaining weak correlations between activation patterns persist and have a strong influence in pattern similarity analyses. Pattern similarity analyses using only activation patterns estimated from the same functional run of data are susceptible to inflated false positives unless trials are randomly ordered, with a different randomization for each subject. In other cases, where there is any structure to trial order, valid pattern similarity analysis results can only be obtained if similarity computations are restricted to pairs of activation patterns from independent runs. Likewise, for pattern classification, false positives are minimized when the testing and training sets in cross validation do not contain patterns estimated from the same run.
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Temporal autocorrelation was unlikely to produce spurious pattern similarity effects at the group level.
“however, because trial sequences were randomly determined for each participant, temporal autocorrelation was unlikely to produce spurious pattern similarity effects at the group level (Mumford et al., 2014).”
From Delay-dependent contributions of medial temporal lobe regions to episodic memory retrieval (2015) · cites this paper as Mumford et al., 2014
Cites
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · see citing works
Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496 · see citing works
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 6,806 · see citing works
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940 · see citing works
Accurate and robust brain image alignment using boundary-based registration
Greve, D. N., & Fischl, B. R. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.060 · Cited by 3,553 · see citing works
Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355 · see citing works
Circular analysis in systems neuroscience: the dangers of double dipping
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696 · see citing works
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271 · see citing works
Decoding mental states from brain activity in humans
Haynes, J., & Rees, G. E. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1931 · Cited by 1,804 · see citing works
Machine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624 · see citing works
Building Memories: Remembering and Forgetting of Verbal Experiences as Predicted by Brain Activity
Wagner, A. D., Schacter, D. L., Rotte, M., Koutstaal, W., Maril, A., Dale, A. M., Rosen, B. R., & Buckner, R. L. (1998) · Science · doi:10.1126/science.281.5380.1188 · Cited by 1,620 · see citing works
Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey
Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., & Bandettini, P. A. (2008) · Neuron · doi:10.1016/j.neuron.2008.10.043 · Cited by 1,334 · see citing works
Representational geometry: integrating cognition, computation, and the brain
Kriegeskorte, N., & Kievit, R. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.06.007 · Cited by 898 · see citing works
Measuring functional connectivity during distinct stages of a cognitive task
Rissman, J., Gazzaley, A. H., & D’Esposito, M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.035 · Cited by 894 · see citing works
Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses
Mumford, J. A., Turner, B. O., Ashby, F. G., & Poldrack, R. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.076 · Cited by 729 · see citing works
Cognitive emissions of 1/f noise.
Gilden, D. L. (2001) · Psychological Review · doi:10.1037/0033-295x.108.1.33 · Cited by 516 · see citing works
Patterns of Activity in the Categorical Representations of Objects
Carlson, T. A., Schrater, P., & He, S. (2003) · Journal of Cognitive Neuroscience · doi:10.1162/089892903322307429 · Cited by 273 · see citing works
Patterns of Activity in the Categorical Representations of Objects
Carlson, T. A., Schrater, P., & He, S. (2003) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2003.15.5.704 · Cited by 215 · see citing works
Pattern-information analysis: From stimulus decoding to computational-model testing
Kriegeskorte, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.061 · Cited by 191 · see citing works
Efficiency, power, and entropy in event-related FMRI with multiple trial types
Liu, T. T., & Frank, L. R. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.030 · Cited by 134 · see citing works
Measuring neural representations with fMRI: practices and pitfalls
Davis, T., & Poldrack, R. A. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12156 · Cited by 132 · see citing works
Efficiency, power, and entropy in event-related fMRI with multiple trial types
Liu, T. T. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.031 · Cited by 114 · see citing works
Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs
Turner, B. O., Mumford, J. A., Poldrack, R. A., & Ashby, F. G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.057 · Cited by 97 · see citing works
The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs
Coutanche, M. N., & Thompson‐Schill, S. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.076 · Cited by 53 · see citing works
Xiong (1999) · doi:10.1002/(sici)1097-0193(1999)8:2/3
Flagged first, then by citations
Cited by
Sorted by citations
Lonsdorf, T. B., Menz, M. M., Andreatta, M., Fullana, M. A., Golkar, A., Haaker, J., Heitland, I., Hermann, A., Kuhn, M., Kruse, O., et al. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.026 · Cited by 837 · see citing works
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives
Haynes, J.-D. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.025 · Cited by 402 · see citing works
Schlichting, M. L., Mumford, J. A., & Preston, A. R. (2015) · Nature Communications · doi:10.1038/ncomms9151 · Cited by 381 · see citing works
Tompary, A., & Davachi, L. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.005 · Cited by 222 · see citing works
Improving the accuracy of single-trial fMRI response estimates using GLMsingle
Prince, J. S., Charest, I., Kurzawski, J. W., Pyles, J. A., Tarr, M. J., & Kay, K. N. (2022) · eLife · doi:10.7554/elife.77599 · Cited by 174 · see citing works
Behavioural and neural evidence for self-reinforcing expectancy effects on pain
Jepma, M., Koban, L., van Doorn, J., Jones, M., & Wager, T. D. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0455-8 · Cited by 155 · see citing works
A Guide to Representational Similarity Analysis for Social Neuroscience
Popal, H. S., Wang, Y., & Olson, I. R. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz099 · Cited by 150 · see citing works
Delay-dependent contributions of medial temporal lobe regions to episodic memory retrieval
Ritchey, M., Montchal, M. E., Yonelinas, A. P., & Ranganath, C. (2015) · eLife · doi:10.7554/elife.05025 · Cited by 148 · see citing works
Weaverdyck, M. E., Lieberman, M. D., & Parkinson, C. M. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa057 · Cited by 136 · see citing works
Prediction errors disrupt hippocampal representations and update episodic memories
Sinclair, A. H., Manalili, G. M., Brunec, I. K., Adcock, R. A., & Barense, M. D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2117625118 · Cited by 128 · see citing works
The Relationship between Age, Neural Differentiation, and Memory Performance
Koen, J. D., Hauck, N., & Rugg, M. D. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1498-18.2018 · Cited by 117 · see citing works
Abdulrahman, H., & Henson, R. N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.009 · Cited by 116 · see citing works
Cowan, E., Liu, A., Henin, S., Kothare, S., Devinsky, O., & Davachi, L. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.1946-19.2020 · Cited by 97 · see citing works
Mnemonic prediction errors bias hippocampal states
Bein, O., Duncan, K. D., & Davachi, L. (2020) · Nature Communications · doi:10.1038/s41467-020-17287-1 · Cited by 103 · see citing works
Handjaras, G., Ricciardi, E., Leo, A., Lenci, A., Cecchetti, L., Cosottini, M., Marotta, G., & Pietrini, P. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.063 · Cited by 100 · see citing works
Fröhner, J. H., Teckentrup, V., Smolka, M. N., & Kroemer, N. B. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.053 · Cited by 92 · see citing works
Multimodal Integration and Vividness in the Angular Gyrus During Episodic Encoding and Retrieval
Tibon, R., Fuhrmann, D., Levy, D. A., Simons, J. S., & Henson, R. N. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2102-18.2018 · Cited by 91 · see citing works
Representational Similarity Analyses
Dimsdale-Zucker, H. R., & Charan Ranganath, A. (2018) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-812028-6.00027-6 · Cited by 93 · see citing works
Schemas provide a scaffold for neocortical integration of new memories over time
Audrain, S., & McAndrews, M. P. (2022) · Nature Communications · doi:10.1038/s41467-022-33517-0 · Cited by 76 · see citing works
Piva, M., Velnoskey, K. R., Jia, R., Nair, A. R., Levy, I., & Chang, S. W. C. (2019) · eLife · doi:10.7554/elife.44939 · Cited by 78 · see citing works
Valente, G., Lage‐Castellanos, A., Hausfeld, L. G., Martino, F. D., & Formisano, E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118145 · Cited by 79 · see citing works
Spaced learning enhances episodic memory by increasing neural pattern similarity across repetitions
Feng, K., Zhao, X., Liu, J., Cai, Y., Ye, Z., Chen, C., & Xue, G. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2741-18.2019 · Cited by 78 · see citing works
More Than the Sum of Its Parts: A Role for the Hippocampus in Configural Reinforcement Learning
Duncan, K. D., Doll, B. B., Daw, N. D., & Shohamy, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.042 · Cited by 77 · see citing works
A representational similarity analysis of cognitive control during color-word Stroop
Freund, M. C., Bugg, J. M., & Braver, T. S. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2956-20.2021 · Cited by 69 · see citing works
Mızrak, E., Bouffard, N. R., Libby, L. A., Boorman, E. D., & Ranganath, C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110065 · Cited by 69 · see citing works
Abrupt hippocampal remapping signals resolution of memory interference
Guo, W., Favila, S. E., Kim, G., Molitor, R. J., & Kuhl, B. A. (2021) · Nature Communications · doi:10.1038/s41467-021-25126-0 · Cited by 64 · see citing works
Understanding psychophysiological interaction and its relations to beta series correlation
Di, X., Zhang, Z., & Biswal, B. B. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00304-8 · Cited by 64 · see citing works
Lee, I., Necka, E. A., & Atlas, L. Y. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116254 · Cited by 58 · see citing works
Age Differences In Retrieval-Related Reinstatement Reflect Age-Related Dedifferentiation At Encoding
Hill, P. F., King, D. R., & Rugg, M. D. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa210 · Cited by 58 · see citing works
Long-term learning transforms prefrontal cortex representations during working memory
Miller, J. A., Tambini, A., Kiyonaga, A., & D’Esposito, M. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.019 · Cited by 56 · see citing works
Bein, O., Reggev, N., & Maril, A. (2020) · Nature Communications · doi:10.1038/s41467-020-18364-1 · Cited by 52 · see citing works
Chang, C. H. C., Lazaridi, C., Yeshurun, Y., Norman, K. A., & Hasson, U. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01707 · Cited by 52 · see citing works
Default mode network dysfunction in idiopathic generalised epilepsy
Parsons, N., Bowden, S. C., Vogrin, S. J., & D'Souza, W. J. (2019) · Epilepsy Research · doi:10.1016/j.eplepsyres.2019.106254 · Cited by 53 · see citing works
Does Hemispheric Asymmetry Reduction in Older Adults in Motor Cortex Reflect Compensation?
Knights, E., Morcom, A. M., Cam-CAN, & Henson, R. N. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1111-21.2021 · Cited by 52 · see citing works
Neural correlates of grammatical inflection in older native and second-language speakers
Prehn, K., Taud, B., Reifegerste, J., Clahsen, H., & Flöel, A. (2017) · Bilingualism Language and Cognition · doi:10.1017/s1366728916001206 · Cited by 48 · see citing works
A meta-analysis of fMRI decoding: Quantifying influences on human visual population codes
Coutanche, M. N., Solomon, S. H., & Thompson‐Schill, S. L. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.01.018 · Cited by 47 · see citing works
Koen, J. D., & Rugg, M. D. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4099-15.2016 · Cited by 47 · see citing works
Preparation for upcoming attentional states in the hippocampus and medial prefrontal cortex
Günseli, E., & Aly, M. (2020) · eLife · doi:10.7554/elife.53191 · Cited by 47 · see citing works
Visser, R. M., Bathelt, J., Scholte, H. S., & Kindt, M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0857-21.2021 · Cited by 50 · see citing works
Parallel Engagement of Regions Associated with Encoding and Later Retrieval Forms Durable Memories
Wagner, I. C., van Buuren, M., Bovy, L., & Fernández, G. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0830-16.2016 · Cited by 44 · see citing works
Trial timing and pattern-information analyses of fMRI data
Zeithamová, D., Sanchez, M.-A. D. A., & Adke, A. P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.025 · Cited by 44 · see citing works
Zafar, R., Dass, S. C., & Malik, A. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178410 · Cited by 44 · see citing works
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Zeithamová, D., Gelman, B. D., Frank, L. E., & Preston, A. R. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0719-18.2018 · Cited by 39 · see citing works
The Hippocampus Generalizes across Memories that Share Item and Context Information
Libby, L. A., Reagh, Z. M., Bouffard, N. R., Ragland, J. D., & Ranganath, C. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01345 · Cited by 42 · see citing works
Goal-oriented representations in the human hippocampus during planning and navigation
Crivelli-Decker, J. E., Clarke, A., Park, S. A., Huffman, D. J., Boorman, E. D., & Ranganath, C. (2023) · Nature Communications · doi:10.1038/s41467-023-35967-6 · Cited by 40 · see citing works
Neural reinstatement reveals divided organization of fear and extinction memories in the human brain
Hennings, A. C., McClay, M., Drew, M. R., Lewis‐Peacock, J. A., & Dunsmoor, J. E. (2021) · Current Biology · doi:10.1016/j.cub.2021.11.004 · Cited by 45 · see citing works
Visser, R. M., de Haan, M. I. C., Beemsterboer, T., Haver, P., Kindt, M., & Scholte, H. S. (2016) · Psychophysiology · doi:10.1111/psyp.12665 · Cited by 39 · see citing works
Thavabalasingam, S., O’Neil, E. B., & Lee, A. C. H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.036 · Cited by 39 · see citing works
Pudhiyidath, A., Morton, N. W., Duran, R. V., Schapiro, A. C., Momennejad, I., Hinojosa-Rowland, D. M., Molitor, R. J., & Preston, A. R. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01864 · Cited by 42 · see citing works
Multiple interactive memory representations underlie the induction of false memory
Zhu, B., Chen, C., Shao, X., Liu, W., Ye, Z., Zhuang, L., Li, Z., Loftus, E. F., & Xue, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817925116 · Cited by 55 · see citing works
Srokova, S., Hill, P. F., Koen, J. D., King, D. R., & Rugg, M. D. (2020) · eNeuro · doi:10.1523/eneuro.0142-20.2020 · Cited by 39 · see citing works
The Role of the Human Auditory Corticostriatal Network in Speech Learning
Feng, G., Yi, H. G., & Chandrasekaran, B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy289 · Cited by 38 · see citing works
Time-dependent memory transformation in hippocampus and neocortex is semantic in nature
Krenz, V., Alink, A., Sommer, T., Roozendaal, B., & Schwabe, L. (2023) · Nature Communications · doi:10.1038/s41467-023-41648-1 · Cited by 38 · see citing works
Reinstatement of Event Details during Episodic Simulation in the Hippocampus
Thakral, P. P., Madore, K. P., Addis, D. R., & Schacter, D. L. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz242 · Cited by 35 · see citing works
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Chiang, J. N., Rosenberg, M., Bufford, C. A., Stephens, D., Lysy, A., & Monti, M. M. (2018) · Brain and Language · doi:10.1016/j.bandl.2018.07.003 · Cited by 34 · see citing works
Inferring exemplar discriminability in brain representations
Nili, H., Walther, A., Alink, A., & Kriegeskorte, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232551 · Cited by 40 · see citing works
Repetition dynamically and rapidly increases cortical, but not hippocampal, offline reactivation
Yu, W., Zadbood, A., Chanales, A. J. H., & Davachi, L. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2405929121 · Cited by 39 · see citing works
A multimodal encoding model applied to imaging decision-related neural cascades in the human brain
Muraskin, J., Brown, T. R., Walz, J. M., Tu, T., Conroy, B., Goldman, R. I., & Sajda, P. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.059 · Cited by 33 · see citing works
Neural representation of newly instructed rule identities during early implementation trials
Ruge, H., Schäfer, T. A. J., Zwosta, K., Mohr, H., & Wolfensteller, U. (2019) · eLife · doi:10.7554/elife.48293 · Cited by 29 · see citing works
Asanowicz, D., Gociewicz, K., Koculak, M., Finc, K., Bonna, K., Axel Cleeremans, B., & Binder, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-60268-z · Cited by 26 · see citing works
Valente, G., Kaas, A. L., Formisano, E., & Goebel, R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.080 · Cited by 27 · see citing works
Confirmation of interpersonal expectations is intrinsically rewarding
Reggev, N., Chowdhary, A., & Mitchell, J. P. (2021) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsab081 · Cited by 26 · see citing works
Reward Enhances Memory via Age-Varying Online and Offline Neural Mechanisms across Development
Cohen, A. O., Glover, M. M., Shen, X., Phaneuf-Hadd, C. V., Avallone, K. N., Davachi, L., & Hartley, C. A. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1820-21.2022 · Cited by 25 · see citing works
Bein, O., & Davachi, L. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0627-23.2023 · Cited by 24 · see citing works
Neural encoding of perceived patch value during competitive and hazardous virtual foraging
Silston, B., Wise, T., Qi, S., Sui, X., Dayan, P., & Mobbs, D. (2021) · Nature Communications · doi:10.1038/s41467-021-25816-9 · Cited by 24 · see citing works
Keller, N. E., Hennings, A. C., Leiker, E., Lewis‐Peacock, J. A., & Dunsmoor, J. E. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0075-22.2022 · Cited by 27 · see citing works
Guo, D., & Yang, J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac475 · Cited by 24 · see citing works
Moreira, J. F. G., McLaughlin, K. A., & Silvers, J. A. (2019) · Developmental Psychology · doi:10.1037/dev0000687 · Cited by 24 · see citing works
Hennings, A. C., Cooper, S. E., Lewis‐Peacock, J. A., & Dunsmoor, J. E. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104918 · Cited by 23 · see citing works
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Hill, P. F., Seger, S. E., Yoo, H. B., King, D. R., Wang, D. X., Lega, B. C., & Rugg, M. D. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0278-21.2021 · Cited by 21 · see citing works
Guggenmos, M., Thoma, V., Haynes, J., Richardson‐Klavehn, A., Cichy, R. M., & Sterzer, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.004 · Cited by 19 · see citing works
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Zhang, L., & Du, Y. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119311 · Cited by 20 · see citing works
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Srokova, S., Aktas, A. N. Z., Koen, J. D., & Rugg, M. D. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0959-23.2023 · Cited by 20 · see citing works
Clewett, D., Huang, R., & Davachi, L. (2025) · Neuron · doi:10.1016/j.neuron.2025.05.013 · Cited by 27 · see citing works
Characterizing the Network Architecture of Emotion Regulation Neurodevelopment
Moreira, J. F. G., McLaughlin, K. A., & Silvers, J. A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab074 · Cited by 18 · see citing works
A common neural code for meaning in discourse production and comprehension
Patel, T., Morales, M., Pickering, M. J., & Hoffman, P. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120295 · Cited by 21 · see citing works
Comprehending auditory speech: previous and potential contributions of functional MRI
Evans, S., & McGettigan, C. (2017) · Language Cognition and Neuroscience · doi:10.1080/23273798.2016.1272703 · Cited by 19 · see citing works
Executive Function Assigns Value to Novel Goal-Congruent Outcomes
McDougle, S. D., Ballard, I. C., Baribault, B., Bishop, S. J., & Collins, A. G. E. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab205 · Cited by 23 · see citing works
Neural dynamics underlying the acquisition of distinct auditory category structures
Feng, G., Gan, Z., Yi, H. G., Ell, S. W., Roark, C. L., Wang, S., Wong, P. C. M., & Chandrasekaran, B. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118565 · Cited by 19 · see citing works
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Karimi-Rouzbahani, H., Woolgar, A., Henson, R. N., & Nili, H. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.755988 · Cited by 18 · see citing works
Arco, J. E., González‐García, C., Díaz-Gutiérrez, P., Ramı́rez, J., & Ruz, M. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.06.017 · Cited by 18 · see citing works
Cooper, S. E., Hennings, A. C., Bibb, S. A., Lewis‐Peacock, J. A., & Dunsmoor, J. E. (2024) · Current Biology · doi:10.1016/j.cub.2024.06.071 · Cited by 17 · see citing works
Visser, R. M., Haver, P., Zwitser, R. J., Scholte, H. S., & Kindt, M. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00222 · Cited by 17 · see citing works
Human Face-Selective Cortex Does Not Distinguish between Members of a Racial Outgroup
Reggev, N., Brodie, K., Cikara, M., & Mitchell, J. P. (2020) · eNeuro · doi:10.1523/eneuro.0431-19.2020 · Cited by 17 · see citing works
Simple but robust improvement in multivoxel pattern classification
Lee, S., & Kable, J. W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207083 · Cited by 16 · see citing works
A distributed dynamic brain network mediates linguistic tone representation and categorization
Feng, G., Gan, Z., Llanos, F., Meng, D., Wang, S., Wong, P. C. M., & Chandrasekaran, B. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117410 · Cited by 16 · see citing works
Charting a Future for fMRI in Communication Science
Turner, B. O., Huskey, R., & Weber, R. (2018) · Communication Methods and Measures · doi:10.1080/19312458.2018.1520823 · Cited by 15 · see citing works
Effects of age on goal-dependent modulation of episodic memory retrieval
Srokova, S., Hill, P. F., Elward, R. L., & Rugg, M. D. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.02.004 · Cited by 15 · see citing works
Developmental change in prefrontal cortex recruitment supports the emergence of value-guided memory
Nussenbaum, K., & Hartley, C. A. (2021) · eLife · doi:10.7554/elife.69796 · Cited by 12 · see citing works
Emerging Native-Similar Neural Representations Underlie Non-Native Speech Category Learning Success
Feng, G., Li, Y., Hsu, S., Wong, P. C. M., Chou, T., & Chandrasekaran, B. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00035 · Cited by 15 · see citing works
Katsumi, A., Iwata, S., & Tsukiura, T. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02143 · Cited by 13 · see citing works
Tompary, A., & Davachi, L. (2024) · eLife · doi:10.7554/elife.84359 · Cited by 14 · see citing works
Hokett, E., Mirjalili, S., & Duarte, A. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.04.015 · Cited by 13 · see citing works
Zafar, R., Kamel, N., Naufal, M., Malik, A. S., Dass, S. C., Ahmad, R. F., Abdullah, J. M., & Reza, F. (2017) · Journal of Integrative Neuroscience · doi:10.3233/jin-170016 · Cited by 12 · see citing works
Neural pattern similarity across concept exemplars predicts memory after a long delay
Bruett, H., Calloway, R. C., Tokowicz, N., & Coutanche, M. N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117030 · Cited by 11 · see citing works
Thakral, P. P., Bottary, R. M., & Kensinger, E. A. (2022) · Cortex · doi:10.1016/j.cortex.2022.02.014 · Cited by 13 · see citing works
Repertoire of timescales in uni – and transmodal regions mediate working memory capacity
Wolman, A., Çatal, Y., Klar, P., Steffener, J., & Northoff, G. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120602 · Cited by 12 · see citing works
Demidenko, M. I., Mumford, J. A., & Poldrack, R. A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00262 · Cited by 13 · see citing works
Ladwig, Z., Kermani, K. Z., Park, Y., Housteau, E., Dworetsky, A., Labora, N., Hernandez, J. J., Dorn, M., Smith, D. M., Nee, D. E., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.04.011 · Cited by 18 · see citing works
- Authors
- Mumford, J. A., Davis, T., & Poldrack, R. A.
- Affiliations
- University of Wisconsin–Madison (US)
- Texas Tech University (US)
- The University of Texas at Austin (US)
- Corresponding author
- Mumford, Jeanette A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 103
- Pages
- 130-138
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2015682160
- Cited by
- 237
- References
- 24
- Citations by year
- 2026: 20 · 2025: 36 · 2024: 20 · 2023: 29 · 2022: 25 · 2021: 29 · 2020: 23 · 2019: 15 · 2018: 17 · 2017: 10 · 2016: 8 · 2015: 5
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- multivariate pattern analysis, single-trial analysis, research design, interstimulus interval, type I error rate, false positive rate, pattern similarity, pattern classification, least squares estimation, temporal autocorrelation, cross-validation, functional MRI
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Research Design, Multivariate Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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