70. Bohus, M. et al. The short version of the Borderline Symptom List (BSL-23): development and initial data on psychometric properties. *Psychopathology* **42**, 32–39 (2008).

71. Sobell, L. C., Sobell, M. B. Timeline Follow Back. A technique for assessing self-reported alcohol consumption. *In: Litten R. & Allen J. eds. Measuring alcohol consumption*. 41–72 (Humana Press, 1992).

72. Saunders, J. B., Aasland, O. G., Babor, T. F., De la Fuente, J. R. & Grant, M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption - II. *Addiction* **88**, 791–804 (1993).

73. Hawks, R. L. & Chiang, C. N. *Urine testing for drugs of abuse*. (National Institute on Drug Abuse, 1986).

74. Jezzard, P. & Balaban, R. S. Correction for geometric distortion in echo planar images from B0 field variations. *Magn. Reson. Med.* **34**, 65–73 (1995).

75. Reber, P., J. Wong, E. C., Buxton, R. B. & Frank, L. R. Correction of off resonance-related distortion in echo-planar imaging using EPI-based field maps. *Magn. Reson. Med.* **39**, 328–330 (1998).

76. Chang, H. & Fitzpatrick, J. M. A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. *IEEE Trans. Med. Imaging* **11**, 319–329 (1992).

77. Andersson, J. L., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. *NeuroImage* **20**, 870–888 (2003).

78. Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T 1-mapping at high field. *NeuroImage* **49**, 1271–1281 (2010).

79. Xu, J. et al. Evaluation of slice accelerations using multiband echo planar imaging at 3T. *NeuroImage* **83**, 991–1001 (2013).

80. Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. *Magn. Reson. Med.* **63**, 1144–1153 (2010).

81. Feinberg, D. A. et al. Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging. *PloS One* **5**, e15710 (2010).

82. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. *NeuroImage* **17**, 825–841 (2002).

83. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. *NeuroImage* **62**, 782–790 (2012).

84. Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary- based registration. *NeuroImage* **48**, 63–72 (2009).

85. Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. *NeuroImage* **37**, 90–101 (2007).

86. Rokem, A., Trumpis, M. & Perez, F. Nitime: time-series analysis for neuroimaging data. *in Proceedings. of the 8th Python in Science Conference* **2**, 68–75 (Caltech, 2009).

87. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. *NeuroImage* **54**, 2033–2044 (2011).

88. Marques, J. P. & Gruetter, R. New developments and applications of the MP2RAGE sequence-focusing the contrast and high spatial resolution R1 mapping. *PloS One* **8**, e69294 (2013).

89. Waehnert, M. D. et al. A subject-specific framework for in vivo myeloarchitectonic analysis using high resolution quantitative MRI. *Neuroimage* **125**, 94–107 (2016).

90. Lorio, S. et al. Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. *Hum. Brain Mapp.* **37**, 1801–1815 (2016).

91. Bazin, P.-L. et al. A computational framework for ultra-high resolution cortical segmentation at 7Tesla. *NeuroImage* **93**, 201–209 (2014).

92. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. *NeuroImage* **9**, 179–194 (1999).

93. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system. *NeuroImage* **9**, 195–207 (1999).

94. Setsompop, K et al. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. *Magn. Reson. Med.* **67**, 1210–1224 (2012).

95. Griswold, M.A. et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). *Magn. Reson. Med.* **47**, 1202–1210 (2002).

96. Liu, C., Li, W., Tong, K.A., Yeom, K.W. & Kuzminski, S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. *J. Magn. Reson. Imaging* **42**, 23–41 (2015).

97. Haacke, E. M., Mittal, S., Wu, Z., Neelavalli, Z. & Cheng, Y.-CN.Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. *Am. J. Neuroradiol.* **30**, 19–30 (2009).

98. Mittal, S., Wu, Z., Neelavalli, J. & Haacke, E. M. Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. *Am. J. Neuroradiol* **30**, 232–252 (2009).

99. Uecker, M. et al. ESPIRIiT - An eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. *Magn. Reson. Med.* **71**, 990–1001 (2014).

100. Chatnuntawech, I. et al. Single-step quantitative susceptibility mapping with variational penalties. *NMR Biomed.* **30**, e3570 (2016).

101. Deistung, A., Schweser, F. & Reichenbach., J. R. Overview of quantitative susceptibility mapping. *NMR Biomed.* **30**, e3569 (2017).

102. Baruch, M. C. Pulse Decomposition Analysis of the digital arterial pulse during hemorrhage simulation. *Nonlinear Biomed. Phys* **5**, 1–15 (2011).

103. Oostenveld, R. & Praamstra, P. The five percent electrode system for high-resolution EEG and ERP measurements. *Clin. Neurophysiol.* **112**, 713–719 (2001).

104. Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. *Comput. Intell. Neurosci.* **2011**, 1–13 (2011).

105. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. *J. Neurosci. Methods* **134**, 9–21 (2004).

106. Makeig, S., Bell, A. J., Jung, T.-P., Sejnowski, T. J. Independent component analysis of electroencephalographic data. *In: Touretzky D., Mozer M. & Hasselmo M. Eds. Advances in Neural Information Processing Systems* **8**, 145–151 (1996).

107. Gorgolewski, K. J. et al. BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods. *PLoS Comp. Biol.* **13**, e1005209 (2017).

108. Schaworonkow, N. & Nikulin, V. V. Spatial neuronal synchronization and the waveform of oscillations: implications for EEG and MEG. *bioRxiv*, https://www.biorxiv.org/content/early/2018/08/27/401091 (2018).

109. Esteban, O. et al. MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites. *PloS One* **12**, e0184661 (2017).
70. Bohus, M. et al. The short version of the Borderline Symptom List (BSL-23): development and initial data on psychometric properties. *Psychopathology* **42**, 32–39 (2008).

71. Sobell, L. C., Sobell, M. B. Timeline Follow Back. A technique for assessing self-reported alcohol consumption. *In: Litten R. & Allen J. eds. Measuring alcohol consumption*. 41–72 (Humana Press, 1992).

72. Saunders, J. B., Aasland, O. G., Babor, T. F., De la Fuente, J. R. & Grant, M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption - II. *Addiction* **88**, 791–804 (1993).

73. Hawks, R. L. & Chiang, C. N. *Urine testing for drugs of abuse*. (National Institute on Drug Abuse, 1986).

74. Jezzard, P. & Balaban, R. S. Correction for geometric distortion in echo planar images from B0 field variations. *Magn. Reson. Med.* **34**, 65–73 (1995).

75. Reber, P., J. Wong, E. C., Buxton, R. B. & Frank, L. R. Correction of off resonance-related distortion in echo-planar imaging using EPI-based field maps. *Magn. Reson. Med.* **39**, 328–330 (1998).

76. Chang, H. & Fitzpatrick, J. M. A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. *IEEE Trans. Med. Imaging* **11**, 319–329 (1992).

77. Andersson, J. L., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. *NeuroImage* **20**, 870–888 (2003).

78. Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T 1-mapping at high field. *NeuroImage* **49**, 1271–1281 (2010).

79. Xu, J. et al. Evaluation of slice accelerations using multiband echo planar imaging at 3T. *NeuroImage* **83**, 991–1001 (2013).

80. Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. *Magn. Reson. Med.* **63**, 1144–1153 (2010).

81. Feinberg, D. A. et al. Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging. *PloS One* **5**, e15710 (2010).

82. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. *NeuroImage* **17**, 825–841 (2002).

83. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. *NeuroImage* **62**, 782–790 (2012).

84. Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary- based registration. *NeuroImage* **48**, 63–72 (2009).

85. Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. *NeuroImage* **37**, 90–101 (2007).

86. Rokem, A., Trumpis, M. & Perez, F. Nitime: time-series analysis for neuroimaging data. *in Proceedings. of the 8th Python in Science Conference* **2**, 68–75 (Caltech, 2009).

87. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. *NeuroImage* **54**, 2033–2044 (2011).

88. Marques, J. P. & Gruetter, R. New developments and applications of the MP2RAGE sequence-focusing the contrast and high spatial resolution R1 mapping. *PloS One* **8**, e69294 (2013).

89. Waehnert, M. D. et al. A subject-specific framework for in vivo myeloarchitectonic analysis using high resolution quantitative MRI. *NeuroImage* **125**, 94–107 (2016).

90. Lorio, S. et al. Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. *Hum. Brain Mapp.* **37**, 1801–1815 (2016).

91. Bazin, P.-L. et al. A computational framework for ultra-high resolution cortical segmentation at 7Tesla. *NeuroImage* **93**, 201–209 (2014).

92. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. *NeuroImage* **9**, 179–194 (1999).

93. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system. *NeuroImage* **9**, 195–207 (1999).

94. Setsompop, K et al. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. *Magn. Reson. Med.* **67**, 1210–1224 (2012).

95. Griswold, M.A. et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). *Magn. Reson. Med.* **47**, 1202–1210 (2002).

96. Liu, C., Li, W., Tong, K.A., Yeom, K.W. & Kuzminski, S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. *J. Magn. Reson. Imaging* **42**, 23–41 (2015).

97. Haacke, E. M., Mittal, S., Wu, Z., Neelavalli, Z. & Cheng, Y.-CN.Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. *Am. J. Neuroradiol.* **30**, 19–30 (2009).

98. Mittal, S., Wu, Z., Neelavalli, J. & Haacke, E. M. Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. *Am. J. Neuroradiol* **30**, 232–252 (2009).

99. Uecker, M. et al. ESPIRIiT - An eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. *Magn. Reson. Med.* **71**, 990–1001 (2014).

100. Chatnuntawech, I. et al. Single-step quantitative susceptibility mapping with variational penalties. *NMR Biomed.* **30**, e3570 (2016).

101. Deistung, A., Schweser, F. & Reichenbach., J. R. Overview of quantitative susceptibility mapping. *NMR Biomed.* **30**, e3569 (2017).

102. Baruch, M. C. Pulse Decomposition Analysis of the digital arterial pulse during hemorrhage simulation. *Nonlinear Biomed. Phys* **5**, 1–15 (2011).

103. Oostenveld, R. & Praamstra, P. The five percent electrode system for high-resolution EEG and ERP measurements. *Clin. Neurophysiol.* **112**, 713–719 (2001).

104. Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. *Comput. Intell. Neurosci.* **2011**, 1–13 (2011).

105. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. *J. Neurosci. Methods* **134**, 9–21 (2004).

106. Makeig, S., Bell, A. J., Jung, T.-P., Sejnowski, T. J. Independent component analysis of electroencephalographic data. *In: Touretzky D., Mozer M. & Hasselmo M. Eds. Advances in Neural Information Processing Systems* **8**, 145–151 (1996).

107. Gorgolewski, K. J. et al. BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods. *PLoS Comp. Biol.* **13**, e1005209 (2017).

108. Schaworonkow, N. & Nikulin, V. V. Spatial neuronal synchronization and the waveform of oscillations: implications for EEG and MEG. *bioRxiv*, https://www.biorxiv.org/content/early/2018/08/27/401091 (2018).

109. Esteban, O. et al. MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites. *PloS One* **12**, e0184661 (2017).
110. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. *NeuroImage* **59**, 2142–2154 (2012).

111. Streiner, D. L. Starting at the beginning: an introduction to coefficient alpha and internal consistency. *J. Pers. Assess.* **80**, 99–103 (2003).

112. Meule, A. & Gearhardt, A. N. Five years of the Yale Food Addiction Scale: Taking stock and moving forward. *Current Addiction Reports* **1**, 193–205 (2014).

113. Kunzmann, U., Kappes, C. & Wrosch, W. Emotional aging: a discrete emotions perspective. *Front. Psychol* **5**, 308 (2014).

114. Scott, S. B., Sliwinski, M. J. & Blanchard-Fields, F. Age differences in emotional responses to daily stress: The role of timing, severity, and global perceived stress. *Psychol. and Aging* **28**, 4 (2013).

## Data Citations

1. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative http://doi.org/10.15387/fcp_indi.mpi_lemon (2018).

2. *OpenNeuro* https://doi.org/10.18112/OPENNEURO.DS000221.V2 (2017).

## Acknowledgements

We thank all participants who volunteered to partake in our study. Moreover, we thank Elizabeth Kelly for proofreading the manuscript and Heike Schmidt-Duderstedt for editing tables and figures.

## Author Contributions

Conception, design, and preparation of the manuscript (Core LEMON group): Anahit Babayan, Miray Erbey, Andrea M.F. Reiter, Josefin Röbbig, H. Lina Schaare, Deniz Kumral, Janis D. Reinelt, Marie Uhlig, Michael Gaebler, Arno Villringer. Contribution to study design: Alfred Anwander, Pierre-Louis Bazin, Annette Horstmann, Leonie Lampe, Daniel S. Margulies, Hadas Okon-Singer, André Pampel, Christiane S. Rohr, Julia Sacher, Angelika Thöne-Otto, Sabrina Trapp, Roberto Coztal, Karina Näthe, Sven Preusser, Anne Schrimpf, Norman Forschack, Till Nierhaus, Krzysztof J. Gorgolewski. Quality control of MRI data: Miray Erbey, Andrea M. F. Reiter, Josefin Röbbig, H. Lina Schaare, Deniz Kumral, Janis D. Reinelt, Marie Uhlig, Michael Gaebler. MRI data preprocessing: Julia M. Huntenburg, Deniz Kumral, H. Lina Schaare, Ahmad S. Kaanan. EEG data quality control and preprocessing: Keyvan Mahjoory, Elena Cesnaite, Vadim V. Nikulin. Behavioral data analyses: Josefin Röbbig, Maria Blöchl. Medical Briefing: Katrin Arelin, Janis D. Reinelt, Anna Kosatschek. Recruiting participants and data acquisition (Core LEMON group, and medical or student assistants): Anahit Babayan, Miray Erbey, Andrea M. F. Reiter, Josefin Röbbig, H. Lina Schaare, Deniz Kumral, Janis D. Reinelt, Marie Uhlig, Michael Gaebler, Denise Altmann, Edith Bongartz, Patric Breig, Sufang Chen, Saskia Czerwonatis, Gabriele Dambrauskaitė, Maria Dreyer, Jesica Enders, Melina Engelhardt, Marie Michele Fischer, Johannes Golchert, Laura Golz, C. Alexandrina Guran, Susanna Hedrich, Nicole Hentschel, Daria I. Hoffmann, Rebecca Jost, Stella Kunzendorf, Hannah Lammers, Mark E. Lauckner, Natacha Mendes, Ramona Menger, Enzo Morino, Jennifer Neubauer, Handan Noyan, Sabine Oligschläger, Patricia Panczyszyn-Trzewik, Dorothee Poehlchen, Nadine Putzke, Sabrina Roski, Marie-Catherine Schaller, Anja Schieferbein, Benito Schlaak, Hanna Maria Schmidt, Robert Schmidt, Sylvia Stasch, Maria Voss, Anett Wiedemann. All authors critically reviewed and approved the final version of the manuscript.

## Additional Information

Table 6 is only available in the online version of this paper.

**Supplementary information** accompanies this paper at http://www.nature.com/sdata.

**Competing interests:** The authors declare no competing interests.

**How to cite this article:** Babayan, A. et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. *Sci. Data.* **6**:180308 https://doi.org/10.1038/sdata.2018.308 (2019).

**Publisher's note:** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_3.png" /></figure>

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

© The Author(s) 2019
110. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. *NeuroImage* **59**, 2142–2154 (2012).

111. Streiner, D. L. Starting at the beginning: an introduction to coefficient alpha and internal consistency. *J. Pers. Assess.* **80**, 99–103 (2003).

112. Meule, A. & Gearhardt, A. N. Five years of the Yale Food Addiction Scale: Taking stock and moving forward. *Current Addiction Reports* **1**, 193–205 (2014).

113. Kunzmann, U., Kappes, C. & Wrosch, W. Emotional aging: a discrete emotions perspective. *Front. Psychol* **5**, 308 (2014).

114. Scott, S. B., Sliwinski, M. J. & Blanchard-Fields, F. Age differences in emotional responses to daily stress: The role of timing, severity, and global perceived stress. *Psychol. and Aging* **28**, 4 (2013).

## Data Citations

1. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative http://doi.org/10.15387/fcp_indi.mpi_lemon (2018).

2. *OpenNeuro* https://doi.org/10.18112/OPENNEURO.DS000221.V2 (2017).

## Acknowledgements

We thank all participants who volunteered to partake in our study. Moreover, we thank Elizabeth Kelly for proofreading the manuscript and Heike Schmidt-Duderstedt for editing tables and figures.

## Author Contributions

Conception, design, and preparation of the manuscript (Core LEMON group): Anahit Babayan, Miray Erbey, Andrea M.F. Reiter, Josefin Röbbig, H. Lina Schaare, Deniz Kumral, Janis D. Reinelt, Marie Uhlig, Michael Gaebler, Arno Villringer. Contribution to study design: Alfred Anwander, Pierre-Louis Bazin, Annette Horstmann, Leonie Lampe, Daniel S. Margulies, Hadas Okon-Singer, André Pampel, Christiane S. Rohr, Julia Sacher, Angelika Thöne-Otto, Sabrina Trapp, Roberto Coztal, Karina Näthe, Sven Preusser, Anne Schrimpf, Norman Forschack, Till Nierhaus, Krzysztof J. Gorgolewski. Quality control of MRI data: Miray Erbey, Andrea M. F. Reiter, Josefin Röbbig, H. Lina Schaare, Deniz Kumral, Janis D. Reinelt, Marie Uhlig, Michael Gaebler. MRI data preprocessing: Julia M. Huntenburg, Deniz Kumral, H. Lina Schaare, Ahmad S. Kaanan. EEG data quality control and preprocessing: Keyvan Mahjoory, Elena Cesnaite, Vadim V. Nikulin. Behavioral data analyses: Josefin Röbbig, Maria Blöchl. Medical Briefing: Katrin Arelin, Janis D. Reinelt, Anna Kosatschek. Recruiting participants and data acquisition (Core LEMON group, and medical or student assistants): Anahit Babayan, Miray Erbey, Andrea M. F. Reiter, Josefin Röbbig, H. Lina Schaare, Deniz Kumral, Janis D. Reinelt, Marie Uhlig, Michael Gaebler, Denise Altmann, Edith Bongartz, Patric Breig, Sufang Chen, Saskia Czerwonatis, Gabriele Dambrauskaitė, Maria Dreyer, Jesica Enders, Melina Engelhardt, Marie Michele Fischer, Johannes Golchert, Laura Golz, C. Alexandrina Guran, Susanna Hedrich, Nicole Hentschel, Daria I. Hoffmann, Rebecca Jost, Stella Kunzendorf, Hannah Lammers, Mark E. Lauckner, Natacha Mendes, Ramona Menger, Enzo Morino, Jennifer Neubauer, Handan Noyan, Sabine Oligschläger, Patricia Panczyszyn-Trzewik, Dorothee Poehlchen, Nadine Putzke, Sabrina Roski, Marie-Catherine Schaller, Anja Schieferbein, Benito Schlaak, Hanna Maria Schmidt, Robert Schmidt, Sylvia Stasch, Maria Voss, Anett Wiedemann. All authors critically reviewed and approved the final version of the manuscript.

## Additional Information

Table 6 is only available in the online version of this paper.

**Supplementary information** accompanies this paper at http://www.nature.com/sdata.

**Competing interests:** The authors declare no competing interests.

**How to cite this article:** Babayan, A. et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. *Sci. Data.* **6**:180308 https://doi.org/10.1038/sdata.2018.308 (2019).

**Publisher's note:** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_4.png" /></figure>

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

© The Author(s) 2019