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3. Predictive Data Calibration for Linear Correlation Significance Testing

Author

Patil, Kaustubh R., Eickhoff, Simon B., and Langner, Robert

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning, G.3, I.2.6, J.3
Abstract

Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well as qualify whether it is meaningful for the population. Pearson's correlation coefficient (PCC), the \textit{de-facto} measure for bivariate relationships, is known to lack in both regards. The estimated strength $r$ maybe wrong due to limited sample size, and nonnormality of data. In the context of statistical significance testing, erroneous interpretation of a $p$-value as posterior probability leads to Type I errors -- a general issue with significance testing that extends to PCC. Such errors are exacerbated when testing multiple hypotheses simultaneously. To tackle these issues, we propose a machine-learning-based predictive data calibration method which essentially conditions the data samples on the expected linear relationship. Calculating PCC using calibrated data yields a calibrated $p$-value that can be interpreted as posterior probability together with a calibrated $r$ estimate, a desired outcome not provided by other methods. Furthermore, the ensuing independent interpretation of each test might eliminate the need for multiple testing correction. We provide empirical evidence favouring the proposed method using several simulations and application to real-world data.

Published
2022

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

Author

Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., Benoit, R. G., Berkers, R. M. W. J., Bhanji, J. P., Biswal, B. B., Bobadilla-Suarez, S., Bortolini, T., Bottenhorn, K. L., Bowring, A., Braem, S., Brooks, H. R., Brudner, E. G., Calderon, C. B., Camilleri, J. A., Castrellon, J. J., Cecchetti, L., Cieslik, E. C., Cole, Z. J., Collignon, O., Cox, R. W., Cunningham, W. A., Czoschke, S., Dadi, K., Davis, C. P., Luca, A. D., Delgado, M. R., Demetriou, L., Dennison, J. B., Di, X., Dickie, E. W., Dobryakova, E., Donnat, C. L., Dukart, J., Duncan, N. W., Durnez, J., Eed, A., Eickhoff, S. B., Erhart, A., Fontanesi, L., Fricke, G. M., Fu, S., Galván, A., Gau, R., Genon, S., Glatard, T., Glerean, E., Goeman, J. J., Golowin, S. A. E., González-García, C., Gorgolewski, K. J., Grady, C. L., Green, M. A., Guassi Moreira, J. F., Guest, O., Hakimi, S., Hamilton, J. P., Hancock, R., Handjaras, G., Harry, B.B., Hawco, C., Herholz, P., Herman, G., Heunis, S., Hoffstaedter, F., Hogeveen, J., Holmes, S., Hu, C. P., Huettel, S. A., Hughes, M. E., Iacovella, V., Iordan, A. D., Isager, P. M., Isik, A. I., Jahn, Andrew, Johnson, Matthew R., Johnstone, Tom, Joseph, Michael J. E., Juliano, Anthony C., Kable, Joseph W., Kassinopoulos, Michalis, Koba, Cemal, Kong, Xiang-Zhen, Koscik, Timothy R., Kucukboyaci, Nuri Erkut, Kuhl, Brice A., Kupek, Sebastian, Laird, Angela R., Lamm, Claus, Langner, Robert, Lauharatanahirun, Nina, Lee, Hongmi, Lee, Sangil, Leemans, Alexander, Leo, Andrea, Lesage, Elise, Li, Flora, Li, Monica Y. C., Lim, Cheng Phui, Lintz, Evan N., Liphardt, Schuyler W., Losecaat Vermeer, Annabel B., Love, Bradley C., Mack, Michael L., Malpica, Norberto, Marins, Theo, Maumet, Camille, McDonald, Kelsey, McGuire, Joseph T., Méndez Leal, Adriana S., Meyer, Benjamin, Meyer, Kristin N., Mihai, Glad, Mitsis, Georgios D., Moll, Jorge, Nielson, Dylan M., Nilsonne, Gustav, Notter, Michael P., Olivetti, Emanuele, Onicas, Adrian I., Papale, Paolo, Patil, Kaustubh R., Peelle, Jonathan E., Pérez, Alexandre, Pischedda, Doris, Poline, Jean-Baptiste, Prystauka,Yanina, Ray, Shruti, Reuter-Lorenz, Patricia A., Reynolds, Richard C., Ricciardi, Emiliano, Rieck, Jenny R., Rodriguez-Thompson, Anais M., 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, Torre Tresols, Juan Jesus, Tozzi, Leonardo, Truong, Vuong, Turella, Luca, van ‘t Veer, Anna E., Verguts, Tom, Vettel, Jean M., Vijayarajah, Sagana, Vo, Khoi, Wall, Matthew B., Weeda, Wouter D., Weis, Susanne, White, David J., Wisniewski, David, Xifra-Porxas, Alba, Yearling, Emily A., Yoon, Sangsuk, Yuan, Rui, Yuen, Kenneth S. L., Lei Zhang, Zhang, Xu, Zosky, Joshua E., Thomas E. Nichols, Poldrack, Rusell A., Schonberg, Tom, Melero Carrasco, Helena, Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., Benoit, R. G., Berkers, R. M. W. J., Bhanji, J. P., Biswal, B. B., Bobadilla-Suarez, S., Bortolini, T., Bottenhorn, K. L., Bowring, A., Braem, S., Brooks, H. R., Brudner, E. G., Calderon, C. B., Camilleri, J. A., Castrellon, J. J., Cecchetti, L., Cieslik, E. C., Cole, Z. J., Collignon, O., Cox, R. W., Cunningham, W. A., Czoschke, S., Dadi, K., Davis, C. P., Luca, A. D., Delgado, M. R., Demetriou, L., Dennison, J. B., Di, X., Dickie, E. W., Dobryakova, E., Donnat, C. L., Dukart, J., Duncan, N. W., Durnez, J., Eed, A., Eickhoff, S. B., Erhart, A., Fontanesi, L., Fricke, G. M., Fu, S., Galván, A., Gau, R., Genon, S., Glatard, T., Glerean, E., Goeman, J. J., Golowin, S. A. E., González-García, C., Gorgolewski, K. J., Grady, C. L., Green, M. A., Guassi Moreira, J. F., Guest, O., Hakimi, S., Hamilton, J. P., Hancock, R., Handjaras, G., Harry, B.B., Hawco, C., Herholz, P., Herman, G., Heunis, S., Hoffstaedter, F., Hogeveen, J., Holmes, S., Hu, C. P., Huettel, S. A., Hughes, M. E., Iacovella, V., Iordan, A. D., Isager, P. M., Isik, A. I., Jahn, Andrew, Johnson, Matthew R., Johnstone, Tom, Joseph, Michael J. E., Juliano, Anthony C., Kable, Joseph W., Kassinopoulos, Michalis, Koba, Cemal, Kong, Xiang-Zhen, Koscik, Timothy R., Kucukboyaci, Nuri Erkut, Kuhl, Brice A., Kupek, Sebastian, Laird, Angela R., Lamm, Claus, Langner, Robert, Lauharatanahirun, Nina, Lee, Hongmi, Lee, Sangil, Leemans, Alexander, Leo, Andrea, Lesage, Elise, Li, Flora, Li, Monica Y. C., Lim, Cheng Phui, Lintz, Evan N., Liphardt, Schuyler W., Losecaat Vermeer, Annabel B., Love, Bradley C., Mack, Michael L., Malpica, Norberto, Marins, Theo, Maumet, Camille, McDonald, Kelsey, McGuire, Joseph T., Méndez Leal, Adriana S., Meyer, Benjamin, Meyer, Kristin N., Mihai, Glad, Mitsis, Georgios D., Moll, Jorge, Nielson, Dylan M., Nilsonne, Gustav, Notter, Michael P., Olivetti, Emanuele, Onicas, Adrian I., Papale, Paolo, Patil, Kaustubh R., Peelle, Jonathan E., Pérez, Alexandre, Pischedda, Doris, Poline, Jean-Baptiste, Prystauka,Yanina, Ray, Shruti, Reuter-Lorenz, Patricia A., Reynolds, Richard C., Ricciardi, Emiliano, Rieck, Jenny R., Rodriguez-Thompson, Anais M., 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, Torre Tresols, Juan Jesus, Tozzi, Leonardo, Truong, Vuong, Turella, Luca, van ‘t Veer, Anna E., Verguts, Tom, Vettel, Jean M., Vijayarajah, Sagana, Vo, Khoi, Wall, Matthew B., Weeda, Wouter D., Weis, Susanne, White, David J., Wisniewski, David, Xifra-Porxas, Alba, Yearling, Emily A., Yoon, Sangsuk, Yuan, Rui, Yuen, Kenneth S. L., Lei Zhang, Zhang, Xu, Zosky, Joshua E., Thomas E. Nichols, Poldrack, Rusell A., Schonberg, Tom, and Melero Carrasco, Helena

Abstract

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., Depto. de Psicobiología y Metodología en Ciencias del Comportamiento, Fac. de Psicología, TRUE, pub

Published
2024

16. Impact of data processing varieties on DCM estimates of effective connectivity from task‐fMRI.

Author

Zhang, Shufei, Jung, Kyesam, Langner, Robert, Florin, Esther, Eickhoff, Simon B., and Popovych, Oleksandr V.

Subjects
ELECTRONIC data processing, FUNCTIONAL magnetic resonance imaging, LARGE-scale brain networks
Abstract

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task‐evoked EC has hardly ever been addressed. We therefore investigated how task‐evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event‐related design of the general linear model (GLM) used for the first‐level task‐evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group‐averaged task‐evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task‐evoked EC between any two data processing conditions via between‐group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group‐averaged EC depending on the data processing choices. In particular, task‐evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event‐related GLM design appears to be more sensitive to task‐evoked modulations of EC, but provides model parameters with lower certainty than the block‐based design, while the latter is more sensitive to the type of activation contrast than is the event‐related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task‐evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Variability in the analysis of a single neuroimaging dataset by many teams

Author

Botvinik-Nezer, Rotem, Holzmeister, Felix, Camerer, Colin F., Dreber, Anna, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Iwanir, Roni, Mumford, Jeanette A., Adcock, R. Alison, Avesani, Paolo, Baczkowski, Blazej M., Bajracharya, Aahana, Bakst, Leah, Ball, Sheryl, Barilari, Marco, Bault, Nadège, Beaton, Derek, Beitner, Julia, Benoit, Roland G., Berkers, Ruud M. W. J., 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 B., 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 L., Dukart, Juergen, Duncan, Niall W., Durnez, Joke, Eed, Amr, Eickhoff, Simon B., Erhart, Andrew, Fontanesi, Laura, Fricke, G. Matthew, Fu, Shiguang, Galván, Adriana, Gau, Remi, 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., Guassi Moreira, João F., Guest, Olivia, Hakimi, Shabnam, Hamilton, J. Paul, Hancock, Roeland, Handjaras, Giacomo, Harry, Bronson B., Hawco, Colin, Herholz, Peer, Herman, Gabrielle, Heunis, Stephan, Hoffstaedter, Felix, Hogeveen, Jeremy, Holmes, Susan, Hu, Chuan-Peng, Huettel, Scott A., Hughes, Matthew E., Iacovella, Vittorio, Iordan, Alexandru D., Isager, Peder M., Isik, Ayse I., Jahn, Andrew, Johnson, Matthew R., Johnstone, Tom, Joseph, Michael J. E., Juliano, Anthony C., Kable, Joseph W., Kassinopoulos, Michalis, Koba, Cemal, Kong, Xiang-Zhen, Koscik, Timothy R., Kucukboyaci, Nuri Erkut, Kuhl, Brice A., Kupek, Sebastian, Laird, Angela R., Lamm, Claus, Langner, Robert, Lauharatanahirun, Nina, Lee, Hongmi, Lee, Sangil, Leemans, Alexander, Leo, Andrea, Lesage, Elise, Li, Flora, Li, Monica Y. C., Lim, Phui Cheng, Lintz, Evan N., Liphardt, Schuyler W., Losecaat Vermeer, Annabel B., Love, Bradley C., Mack, Michael L., Malpica, Norberto, Marins, Theo, Maumet, Camille, McDonald, Kelsey, McGuire, Joseph T., Melero, Helena, Méndez Leal, Adriana S., Meyer, Benjamin, Meyer, Kristin N., Mihai, Glad, Mitsis, Georgios D., Moll, Jorge, Nielson, Dylan M., Nilsonne, Gustav, Notter, Michael P., Olivetti, Emanuele, Onicas, Adrian I., Papale, Paolo, Patil, Kaustubh R., Peelle, Jonathan E., Pérez, Alexandre, Pischedda, Doris, Poline, Jean-Baptiste, Prystauka, Yanina, Ray, Shruti, Reuter-Lorenz, Patricia A., Reynolds, Richard C., Ricciardi, Emiliano, Rieck, Jenny R., Rodriguez-Thompson, Anais M., 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, Torre Tresols, Juan Jesus, Tozzi, Leonardo, Truong, Vuong, Turella, Luca, van ‘t Veer, Anna E., Verguts, Tom, Vettel, Jean M., Vijayarajah, Sagana, Vo, Khoi, Wall, Matthew B., Weeda, Wouter D., Weis, Susanne, White, David J., Wisniewski, David, Xifra-Porxas, Alba, Yearling, Emily A., Yoon, Sangsuk, Yuan, Rui, Yuen, Kenneth S. L., Zhang, Lei, Zhang, Xu, Zosky, Joshua E., Nichols, Thomas E., Poldrack, Russell A., and Schonberg, Tom

Published
2020
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30. Problems in medication self-management by elderly patients

Author

Luegering, Anneke, primary, Frohnhofen, Helmut, additional, Langner, Robert, additional, Wilm, Stefan, additional, Doeppner, Thorsten R., additional, Hermann, Dirk M., additional, Windolf, Joachim, additional, Süss, Jan D., additional, and Gronewold, Janine, additional

Published
2023
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37. Preliminary findings on long-term effects of fMRI neurofeedback training on functional networks involved in sustained attention

Author

Pamplona, Gustavo Santo Pedro; https://orcid.org/0000-0002-0278-203X, Heldner, Jennifer, Langner, Robert, Koush, Yury; https://orcid.org/0000-0002-3684-5641, Michels, Lars; https://orcid.org/0000-0003-3750-1100, Ionta, Silvio, Salmon, Carlos Ernesto Garrido, Scharnowski, Frank; https://orcid.org/0000-0002-9973-2521, Pamplona, Gustavo Santo Pedro; https://orcid.org/0000-0002-0278-203X, Heldner, Jennifer, Langner, Robert, Koush, Yury; https://orcid.org/0000-0002-3684-5641, Michels, Lars; https://orcid.org/0000-0003-3750-1100, Ionta, Silvio, Salmon, Carlos Ernesto Garrido, and Scharnowski, Frank; https://orcid.org/0000-0002-9973-2521

Abstract

INTRODUCTION Neurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long-term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long-term learning effects of neurofeedback training remain under-explored. METHODS Here, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs from 15 healthy individuals. RESULTS Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow-up transfer runs but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. CONCLUSION The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain-be

Published
2023

45. The role of the subgenual anterior cingulate cortex in insomnia disorder: A neuroimaging meta-analysis of 39 studies

Author

Reimann, Gerion, Kueppers, Vincent, Camilleri, Julia, Hoffstaedter, Felix, Langner, Robert, Laird, Angie, Fox, Peter, Spiegelhalder, Kai, Eickhoff, Simon, and Tahmasian, Masoud

Subjects
Sleep Medicine, Medicine and Health Sciences, Medical Specialties, Life Sciences
Abstract

Despite insomnia disorder being the most common sleep disorder and it’s high socio-economic burden, the underlying neurobiological mechanism is still poorly understood. No consistent regional abnormality has been found in our previous meta-analysis across 19 studies, published in Sleep Medicine Reviews, Dec 2018 (https://doi.org/10.1016/j.smrv.2018.07.004). The observed heterogeneity was probably attributed to moderate statistical power, experimental and analysis flexibility of individual studies, and clinical heterogeneity of insomnia disorder. In the following reply letter (https://doi.org/10.1016/j.smrv.2018.08.004), we suggested that our meta-analysis should be revisited in the future and might then reveal consistent regional alterations. Thus, with the noticeable increase of neuroimaging publications in the last few years, we revisited our former meta-analysis by including recent structural and functional neuroimaging studies to increase the statistical power of the meta-analysis and identify a potential robust pattern of abnormality in insomnia disorder.

Published
2022
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48. On the integrity of functional brain networks in schizophrenia, Parkinsonʼs disease, and advanced age: Evidence from connectivity‐based single‐subject classification

Author

Pläschke, Rachel N., Cieslik, Edna C., Müller, Veronika I., Hoffstaedter, Felix, Plachti, Anna, Varikuti, Deepthi P., Goosses, Mareike, Latz, Anne, Caspers, Svenja, Jockwitz, Christiane, Moebus, Susanne, Gruber, Oliver, Eickhoff, Claudia R., Reetz, Kathrin, Heller, Julia, Südmeyer, Martin, Mathys, Christian, Caspers, Julian, Grefkes, Christian, Kalenscher, Tobias, Langner, Robert, and Eickhoff, Simon B.

Published
2017
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