Variability in the analysis of a single neuroimaging dataset by many teams

Botvinik‐Nezer, Rotem; Holzmeister, Felix; Camerer, Colin F.; Dreber, Anna; Huber, Jürgen; Johannesson, Magnus; Kirchler, Michael; Iwanir, Roni; Mumford, Jeanette A.; Adcock, R. Alison; Avesani, Paolo; Bączkowski, Błażej M.; Bajracharya, Aahana; Bakst, Leah; Ball, Sheryl; Barilari, Marco; Bault, Nadège; Beaton, Derek; Beitner, Julia; Benoit, Roland G.; Berkers, Ruud; Bhanji, Jamil P.; Biswal, Bharat B.; Bobadilla-Suarez, Sebastian; Bortolini, Tiago; Bottenhorn, Katherine L.; Bowring, Alexander; Braem, Senne; Brooks, Hayley R.; Brudner, Emily G.; Calderon, Cristian Buc; Camilleri, Julia A.; Castrellon, Jaime J.; Cecchetti, Luca; Cieslik, Edna C.; Cole, Zachary J.; Collignon, Olivier; Cox, Robert W.; Cunningham, William A.; Czoschke, Stefan; Dadi, Kamalaker; Davis, Charles P.; Luca, Alberto De; Delgado, Mauricio R.; Demetriou, Lysia; Dennison, Jeffrey B.; Di, Xin; Dickie, Erin W.; Dobryakova, Ekaterina; Donnat, Claire; Dukart, Juergen; Duncan, Niall W.; Durnez, Joke; Eed, Amr; Eickhoff, Simon B.; Erhart, Andrew; Fontanesi, Laura; Fricke, G. Matthew; Fu, Shiguang; Galván, Adriana; Gau, Rémi; Genon, Sarah; Glatard, Tristan; Glerean, Enrico; Goeman, Jelle J.; Golowin, Sergej A.E.; González-García, Carlos; Gorgolewski, Krzysztof J.; Grady, Cheryl L.; Green, Mikella A.; Moreira, João F. Guassi; Guest, Olivia; Hakimi, Shabnam; Hamilton, J. Paul; Hancock, Roeland; Handjaras, Giacomo; Harry, Bronson; Hawco, Colin; Herholz, Peer; Herman, Gabrielle; Heunis, Stephan; Hoffstaedter, Felix; Hogeveen, Jeremy; Holmes, Susan; Hu, Chuan Peng; Huettel, Scott A.; Hughes, Matthew; Iacovella, Vittorio; Iordan, Alexandru D.; Isager, Peder Mortvedt; Isik, Ayse Ilkay; Jahn, Andrew; Johnson, Matthew; Johnstone, Tom; Joseph, Michael; Juliano, Anthony; Kable, Joseph W.; Kassinopoulos, Michalis; Koba, Cemal; Kong, Xiang; Koscik, Timothy R.; Kucukboyaci, Nuri Erkut; Kuhl, Brice A.; Kupek, Sebastian; Laird, Angela R.; Lamm, Claus; Langner, Robert; Lauharatanahirun, Nina; Lee, Hongmi; S, Lee; Leemans, Alexander; Leo, Andrea; Lesage, Elise; Li, Flora; Li, Monica Y.C.; Lim, Phui Cheng; Lintz, Evan N.; Liphardt, Schuyler; Vermeer, Annabel Losecaat; Love, Bradley C.; Mack, Michael L.; Malpica, Norberto; Marins, Theo; Maumet, Camille; McDonald, Kelsey; McGuire, Joseph T.; Melero, Helena; Leal, Adriana S. Méndez; Meyer, Benjamin; Meyer, Kristin N.; Mihai, Glad; Mitsis, Georgios D.; Moll, Jorge; Nielson, Dylan M.; Nilsonne, Gustav; Notter, Michael; Olivetti, Emanuele; Onicas, Adrian; Papale, Paolo; Patil, Kaustubh R.; Peelle, Jonathan E.; Perez-Lebel, Alexandre; Pischedda, Doris; Poline, Jean Baptiste; Prystauka, Yanina; Ray, Shruti; Reuter‐Lorenz, Patricia A.; Reynolds, Richard C.; Ricciardi, Emiliano; Rieck, Jenny; Rodriguez-Thompson, Anais; Romyn, Anthony; Salo, Taylor; Samanez-Larkin, Gregory R.; Sanz‐Morales, Emilio; Schlichting, Margaret L.; Schultz, Douglas H.; Shen, Qiang; Sheridan, Margaret A.; Silvers, Jennifer A.; Skagerlund, Kenny; Smith, Alec; Smith, David V.; Sokol‐Hessner, Peter; Steinkamp, Simon R.; Tashjian, Sarah M.; Thirion, Bertrand; Thorp, John N; Tinghög, Gustav; Tisdall, Loreen; Tompson, Steven H.; Toro-Serey, Claudio; Tresols, Juan Jesus Torre; Tozzi, Leonardo; Truong, Vuong; Turella, Luca; Veer, Anna E. van ‘t; Verguts, Tom; Vettel, Jean M.; Vijayarajah, Sagana; Vo, Khoi; Wall, Matthew B.; Weeda, Wouter D.; Weis, Susanne; White, David; Wisniewski, David; Xifra‐Porxas, Alba; Yearling, Emily A.; Yoon, Sangsuk; Yuan, Rui; Yuen, Kenneth S.L.; Zhang, Lei; Zhang, Xu; Zosky, Joshua; Nichols, Thomas E.; Poldrack, Russell A.; Schönberg, Tom

doi:10.1038/s41586-020-2314-9

Cited by 857 publications

(478 citation statements)

References 45 publications

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“…In what follows, we report three additional sets of control analyses. A first set was designed to evaluate the extent to which our analysis choices influenced our findings (Botvinik-Nezer et al, 2020). A second set was geared towards ruling out alternative hypotheses.…”

Section: Resultsmentioning

confidence: 99%

Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex

Vermeylen

Wisniewski

González-García

et al. 2019

Preprint

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AbstractInfluential theories of medial frontal cortex (MFC) function suggest that the MFC registers cognitive conflict as an aversive signal, but no study directly tested this idea. Instead, recent studies suggested that non-overlapping regions in the MFC process conflict and affect. In this pre-registered human fMRI study, we used multivariate pattern analyses to identify which regions respond similarly to conflict and aversive signals. The results reveal that, of all conflict- and value-related regions, the ventral pre-supplementary motor area (or dorsal anterior cingulate cortex) showed a shared neural pattern response to different conflict and affect tasks. These findings challenge recent conclusions that conflict and affect are processed independently, and provide support for integrative views of MFC function.

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Section: Resultsmentioning

confidence: 99%

Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex

Vermeylen

Wisniewski

González-García

et al. 2019

Preprint

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“…At a time when the reliability and replicability of neuroimaging research has come under scrutiny (Botvinik-Nezer et al, 2020;Elliott et al, 2020), tackling these open issues is crucial for the interpretation and generalizability of studies linking neural correlates with risk-taking related developmental trajectories (Moffitt et al, 2011;Braams et al, 2015;Casey et al, 2018;Rosenbaum et al, 2018) and clinical outcomes (Büchel et al, 2017;MacNiven et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Brain-outcome associations for risk taking depend on the measures used to capture individual differences

Tisdall¹,

Frey

Horn³

et al. 2018

Preprint

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Biological markers of risk taking are prominent targets for clinical, developmental, and longitudinal research. With respect to brain function, several regions are considered central for risky choice, yet insights into the neural basis of risk taking stem primarily from studies using single measures. Considering that recent studies suggested different risk-taking measures cannot be used interchangeably, it is currently unclear whether core regions of the brain involved in risk show a measure-dependent functional dissociation. Reporting results from the imaging subsample (N = 116 young adults) of the Basel–Berlin Risk Study, we examine (1) the conjunction of average neural representations of experience-based risky choice in the Balloon Analogue Risk Task and description-based risky choice in monetary gambles, (2) the preservation of individual activation differences across the two measures, and (3) the explanatory power of the neural correlates of risky choice for behavior. Our results suggest common group-level activation increases in nucleus accumbens, inconsistent individual differences in regional activation across measures, and limited explanatory power of neural indices for behavior, within and across measures. Our findings help clarify commonalities and differences between the neural representation of experienced and described risk, and thus should inform research designs targeting individual differences in risk taking.

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“…We also note that in our process of parcellating four regions within the DMN, our clusters had become relatively small, which may have limited our ability to find differences between cluster-topic associations among each DMN node. Future work may be able to address this limitation by using image-based meta analyses 90,91 , which may be possible as more researchers heed calls to share unthresholded statistical maps 82,89,92 .…”

Section: Discussionmentioning

confidence: 99%

Functional parcellation of the default mode network: a large-scale meta-analysis

Wang

Tepfer

Taren

et al. 2020

Sci Rep

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The default mode network (DMN) consists of several regions that selectively interact to support distinct domains of cognition. Of the various sites that partake in DMN function, the posterior cingulate cortex (PCC), temporal parietal junction (TPJ), and medial prefrontal cortex (MPFC) are frequently identified as key contributors. Yet, it remains unclear whether these subcomponents of the DMN make unique contributions to specific cognitive processes and health conditions. To address this issue, we applied a meta-analytic parcellation approach used in prior work. This approach used the Neurosynth database and classification methods to quantify the association between PCC, TPJ, and MPFC activation and specific topics related to cognition and health (e.g., decision making and smoking). Our analyses replicated prior observations that the PCC, TPJ, and MPFC collectively support multiple cognitive functions such as decision making, memory, and awareness. To gain insight into the functional organization of each region, we parceled each region based on its coactivation pattern with the rest of the brain. This analysis indicated that each region could be further subdivided into functionally distinct subcomponents. Taken together, we further delineate DMN function by demonstrating the relative strengths of association among subcomponents across a range of cognitive processes and health conditions. A continued attentiveness to the specialization within the DMN allows future work to consider the nuances in sub-regional contributions necessary for healthy cognition, as well as create the potential for more targeted treatment protocols in various health conditions.

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Variability in the analysis of a single neuroimaging dataset by many teams

Cited by 857 publications

References 45 publications

Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex

Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex

Brain-outcome associations for risk taking depend on the measures used to capture individual differences

Functional parcellation of the default mode network: a large-scale meta-analysis

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