A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults

Scientific data - Tập 6 Số 1
Anahit Babayan1,2, Miray Erbey1,2,3, Deniz Kumral1,2, Janis Reinelt2, Andrea Reiter2,4, Josefin Röbbig2, H. Lina Schaare2, Marie Uhlig2, Alfred Anwander2, Pierre‐Louis Bazin2,5,6, Annette Horstmann2,7, Leonie Lampe2, Vadim V. Nikulin2, Hadas Okon‐Singer2,8, Sven Preusser2, André Pampel2, Christiane S. Rohr2, Julia Sacher2, Angelika Thöne-Otto2,9, Sabrina Trapp2, Till Nierhaus2, Denise Altmann2, Katrin Arélin2, Maria Blöchl2,7, Edith Bongartz2, Patric Breig2, Elena Čėsnaitė2, Sufang Chen2, Roberto Cozatl2, Saskia Czerwonatis2, Gabriele Dambrauskaite2, Maria Dreyer2, Jessica Enders2, Melina Engelhardt2, M. Fischer2, Norman Forschack2, Johannes Golchert2, Laura Golz2, C.-N. Alexandrina Guran2, Susanna Hedrich2, Nicole Hentschel2, D. Hoffmann2, Julia M. Huntenburg2, Rebecca Jost2, Anna Kosatschek2, Stella Kunzendorf2, H. Bruce Lammers2, Mark E. Lauckner2, Keyvan Mahjoory2, Ahmad S. Kanaan2, Natacha Mendes2, Ramona Menger2, Enzo Morino2, Karina Näthe2, Jennifer Neubauer2, Handan Noyan2, Sabine Oligschläger2, Patrycja Pańczyszyn-Trzewik2, Dorothee Poehlchen2, Nadine Putzke2, Sabrina Roski2, Marie-Catherine Schaller2, Anja Schieferbein2, Benito Schlaak2, Robert Schmidt7, Krzysztof J. Gorgolewski10, Hanna Maria Schmidt2, Anne Schrimpf2, Sylvia Stasch2, Maria Voß2, Annett Wiedemann2, Daniel S. Margulies11,2, Michael Gaebler12,2,7, Arno Villringer12,2
1Berlin School of Mind and Brain [Berlin] (Luisenstraße 56, Haus 1 10099 Berlin Germany - Germany)
2IMPNSC - Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (P.O. box 500355 04303 Leipzig - Germany)
3Max Planck Institute for Human Development (Lentzeallee 94, 14195 Berlin, Germany - Germany)
4TU Dresden - Technische Universität Dresden = Dresden University of Technology (TU Dresden 01062 Dresden - Germany)
5NIN - Netherlands Institute for Neuroscience (MeiStichting Vrienden van het Herseninstituut Meibergdreef 47 1105 BA Amsterdam - Netherlands)
6Spinoza Center for Neuroimaging [Amsterdam] (Meibergdreef 75, 1105 BK Amsterdam - Netherlands)
7Leipzig University / Universität Leipzig (Augustusplatz 10, 04109 Leipzig, Allemagne - Germany)
8University of Haifa [Haifa] (199 Aba Khoushy Ave. Mount Carmel, Haifa - Israel)
9University Hospital Leipzig = Universitätsklinikum Leipzig (​Liebigstraße 18, Haus B, 04103 Leipzig - Germany)
10Stanford University (450 Serra Mall, Stanford, CA 94305-2004 - United States)
11ICM - Institut du Cerveau = Paris Brain Institute (47-83 Boulevard de l'Hôpital 75651 Paris Cedex 13 - France)
12HU Berlin - Humboldt-Universität zu Berlin = Humboldt University of Berlin = Université Humboldt de Berlin (Humboldt-Universität zu Berlin – Unter den Linden 6 – 10099 Berlin – Bundesrepublik Deutschland - Germany)

Tóm tắt

AbstractWe present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20–35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.

Từ khóa


Tài liệu tham khảo

LeDoux, J. E. The Emotional Brain. (Touchstone Book, 1996).

Nummenmaa, L., Glerean, E., Hari, R. & Hietanen, J. K. In Bodily maps of emotions. Proceedings Natl. Acad. Sci. USA 111, 646–651 (2014).

Schachter, S. & Singer, J. Cognitive, social, and physiological determinants of emotional state. Psychol. Rev. 69, 379–399 (1962).

James, W. What is an emotion? Mind 9, 188–205 (1884).

Garfinkel, S. N. & Critchley, H. D. Threat and the body: how the heart supports fear processing. Trends Cogn. Sci. 20, 34–46 (2016).

House, A. Depression after stroke. Br. Med. J. (Clin. Res. Ed.) 294, 76–78 (1987).

Linden, W., Vodermaier, A., MacKenzie, R. & Greig, D. Anxiety and depression after cancer diagnosis: Prevalence rates by cancer type, gender, and age. J. Affect. Disord. 141, 343–351 (2012).

Lu, D. et al. Clinical diagnosis of mental disorders immediately before and after cancer diagnosis. JAMA Oncol 2, 1188–1196 (2016).

Pyter, L. M., Pineros, V., Galang, J. A., McClintock, M. K. & Prendergast, B. J. Peripheral tumors induce depressive-like behaviors and cytokine production and alter HPA regulation. PNAS 106, 9069–9074 (2009).

Chrousos, G. P. & Gold, P. W. The concepts of stress and stress system disorders: overview of physical and behavioral homeostasis. JAMA 267, 1244–1252 (1992).

Jonas, B. S. & Mussolino, M. E. Symptoms of depression as a prospective risk factor for stroke. Psychosom. Med. 62, 463–471 (2000).

Kubzansky, L. D. & Kawachi, I. Going to the heart of the matter: do negative emotions cause coronary heart disease? J. Psychosom Res. 48, 323–337 (2000).

Golden, S. H. et al. Atherosclerosis Risk in Communities study. Diabetes Care 27, 429–435 (2004).

Glover, G. H., Li, T. Q. & Ress, D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167 (2000).

Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J. & Wager, T. D. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci. Biobehav. Rev. 36, 747–756 (2012).

Pessoa, L. Précis on the cognitive-emotional brain. Behav. Brain. Sci. 38, e71 (2015).

Etkin, A., Büchel, C. & Gross, J. J. The neural bases of emotion regulation. Nat. Rev. Neurosci. 16, 693–700 (2015).

Djuric, Z. et al. Biomarkers of Psychological Stress in Health Disparities Research. Open Biomark J. 1, 7–19 (2008).

Steptoe, A., Deaton, A. & Stone, A. A. Psychological wellbeing, health and ageing. Lancet 385, 640–648 (2015).

Baumgart, M. et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimer’s & Dementia 11, 718–726 (2015).

Mendes, N. et al. A Functional connectome phenotyping dataset including cognitive state and personality measures. Sci. Data. 6:180307, https://doi.org/10.1038/sdata.2018.307 (2019).

Niemann, H., Sturm, W., Thöne-Otto, A. I. & Willmes, K. California Verbal Learning Test (CVLT). German Adaptation. Manual. (Pearson, 2008).

Zimmermann, P. & Fimm, V. Testbatterie zur Aufmerksamkeitsprüfung (TAP). Version 2.3.1, https://www.psytest.net/index.php?page=TAP-2-2&hl=en_US (Psytest, 2012).

Reitan, R. M. Trail making test A & B. (Reitan Neuropsychology Laboratory, 1992).

Schmidt, K. H. & Metzler, P. WST: Wortschatztest. (Beltz, 1992).

Kreuzpointner, L., Lukesch, H. & Horn, W. Leistungsprüfsystem 2. LPS-2. Manual. (Hogrefe, 2013).

Aschenbrenner, S., Tucha, O. & Lange, K. W. RWT: Regensburger Wortflüssigkeits-Test. (Hogrefe, 2000).

LimeSurvey Project Team / Carsten Schmitz. LimeSurvey: An Open Source survey tool. LimeSurvey version 2.0. LimeSurvey Project Hamburg http://www.limesurvey.org, (2012).

Borkenau, P. & Ostendorf, F. NEO-Fünf-Faktoren Inventar nach Costa und McCrae (NEO-FFI). Manual (2. Aufl.). (Hogrefe, 2008).

Costa, P. T. & McCrae, R. R. Revised NEO Personality Inventory (NEO PI-R) and NEO Five Factor Inventory (NEO-FFI) Professional Manual. (Psychological Assessment Resources Inc., 1992).

Schmidt, R. E., Gay, P., d’Acremont, M. & Van der Linden, M. A German adaptation of the UPPS Impulsive Behavior Scale: Psychometric properties and factor structure. Swiss. J. Psychol. 67, 107–112 (2008).

Whiteside, S. P. & Lynam, D. R. The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Pers. Indiv.Differ. 30, 669–689 (2001).

Strobel, A., Beauducel, A., Debener, S. & Brocke, B. Eine deutschsprachige Version des BIS/BAS-Fragebogens von Carver und White. Zeitschrift für Differentielle und diagnostische Psychologie 22, 216–227 (2001).

Carver, C. S. & White, T. L. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the BIS/BAS scales. J. Pers. Soc. Psychol. 67, 319–333 (1994).

Abler, B. & Kessler, H. Emotion Regulation Questionnaire - Eine deutsche Version des ERQ von Gross & John. Diagnostica 55, 144–152 (2009).

Gross, J. J. & John, O. P. Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. J. Pers. Soc. Psychol. 85, 348–362 (2003).

Loch, N., Hiller, W. & Witthöft, M. Der cognitive emotion regulation questionnaire (CERQ). Zeitschrift für Klinische Psychologie und Psychotherapie 40, 94–106 (2011).

Garnefski, N., Kraaij, V. & Spinhoven, P. Negative life events, cognitive emotion regulation and emotional problems. Pers. Individ. Dif 30, 1311–1327 (2001).

Larsen, R. J., Prizmic, Z. Affect regulation. In: Baumeister, R. F. & Vohs, K. D. (Eds) Handbook of self-regulation: Research, theory, and applications, 40–61 (Guilford Press, 2004).

Fydrich, T., Sommer, G. & Brähler, E. F-SOZU: Fragebogen zur sozialen Unterstützung. (Hogrefe, 2007).

Fydrich, T., Sommer, G., Menzel, U. & Höll, B. Social Support Questionnaire (short-form; SozU-K-22). Z. Klin. Psychol. Psychother. 16, 434–436 (1987).

Zimet, G. D, Dahlem, N. W, Zimet, S. G ., & Farley, G. K. The Multidimensional Scale of Perceived Social Support. J. Pers. Assess. 52, 30–41 (1988).

Knoll, N., Rieckmann, N. & Schwarzer, R. Coping as a mediator between personality and stress outcomes: A longitudinal study with cataract surgery patients. Eur. J. Personality 19, 229–247 (2005).

Carver, C. S. You want to measure coping but your protocol’s too long: Consider the Brief COPE. Int. J. Behav. Med. 4, 92–100 (1997).

Glaesmer, H., Hoyer, J., Klotsche, J. & Herzberg, P. Y. Die Deutsche Version des Life-Orientation-Tests (LOT-R) zum dispositionellen Optimismus und Pessimismus. Zeitschrift für Gesundheitspsychologie 16, 26–31 (2008).

Scheier, M. F., Carver, C. S. & Bridges, M. W. Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A re-evaluation of the Life Orientation Test. J. Pers. Soc. Psychol. 67, 1063–1078 (1994).

Fliege, H., Rose, M., Arck, P., Levenstein, S. & Klapp, B. F. Validierung des “Perceived Stress Questionnaire” (PSQ) an einer deutschen Stichprobe. Diagnostica 47, 142–152 (2001).

Levenstein, S. et al. Development of the Perceived Stress Questionnaire: A new tool for psychosomatic research. J. Psychosom. Res. 37, 19–32 (1993).

Schulz, P., Schlotz, W. & Becker, P. Trierer Inventar zum chronischen Stress: TICS. (Hogrefe, 2004).

Schulz, P. & Schlotz, W. Trierer Inventar zur Erfassung von chronischem Stress (TICS): Skalenkonstruktion, teststatistische Überprüfung und Validierung der Skala Arbeitsüberlastung. Diagnostica 45, 8–19 (1999).

Stunkard, A. J. & Messick, S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J. Psychosom. Res. 29, 71–83 (1985).

Pudel, D. & Westenhöfer, J. Fragebogen zum Eßverhalten (FEV). (Hogrefe, 1989).

Meule, A., Vögele, C. & Kübler, A. Deutsche Übersetzung und Validierung der Yale Food Addiction Scale-German translation and validation of the Yale Food Addiction Scale. Diagnostica 58, 115–126 (2012).

Gearhardt, A.N., Corbin, W.R. & Brownell, K.D. Preliminary validation of the Yale Food Addiction Scale. Appetite 52, 430–436 (2009).

Saß, H., Wittchen, H. U., Zaudig, M. & Houben, I. Diagnostische Kriterien. DSM-IV-TR. (Hogrefe, 2003).

Petrides, K. V. & Furnham, A. TEIQue-SF: Trait Emotional Intelligence Questionnaire-Short Form. J. Appl. Soc. Psychol. 36, 552–569 (2006).

Freudenthaler, H. H., Neubauer, A. C., Gabler, P. & Scherl, W. G. Testing the Trait Emotional Intelligence Questionnaire (TEIQue) in a German-speaking sample. Pers. Indiv. Differ. 45, 673–678 (2008).

Laux, L., Glanzmann, P., Schaffner, P. & Spielberger, C.D. Das State-Trait-Angstinventar. (Beltz Test, 1981).

Spielberger, C. D., Gorsuch, R. L. & Luschene, R. E. Manual for the State-Trait Anxiety Inventory. Manual for the State-Trait Anxiety Inventory. (Consulting Psychologists Press, 1970).

Schwenkmezger, P., Hodapp, V. & Spielberger, C. D. Das State-Trait-Ärgerausdrucks-Inventar STAX I. (Huber, 1992).

Spielberger, C. D. State-Trait Anger Expression Inventory (STAXI). Research edition. (Psychological Assessment Resources: Odessa, 1988).

Kupfer, J., Brosig, B. & Brähler, E. Toronto-Alexithymie-Skala-26. Deutsche Version (TAS-26). (Hogrefe, 2001).

Bagby, R. M., Parker, J. D. & Taylor, G. J. The twenty-item Toronto Alexithymia Scale—I. Item selection and cross-validation of the factor structure. J. Psychosom. Res. 38, 23–32 (1994).

Steyer, R., Schwenkmezger, P., Notz, P. & Eid, M. Der Mehrdimensionale Befindlichkeitsfragebogen. (Hogrefe, 1997).

Lang, F. R. & Carstensen, L. L. Time counts: future time perspective, goals, and social relationships. Psychol. Aging 17, 125–139 (2002).

Gorgolewski, K. J. et al. A correspondence between individual differences in the brain’s intrinsic functional architecture and the content and form of self-generated thoughts. PloS One 9, e97176 (2014).

Wittchen, H., Wunderlich, U. & Gruschwitz, S. SKID-I. Strukturiertes klinisches Interview für DSM-IV; Achse I: Psychische Störungen. (Hogrefe, 1997).

Saß, H., Wittchen, H. U. & Zaudig, M. Diagnostisches und statistisches Manual psychischer Störungen-DSM-IV. (Hogrefe: Göttingen, 1996).

Hamilton, M. A rating scale for depression. J. Neurol Neurosurg Psychiatry 23, 56–62 (1960).

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).

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).

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).

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

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

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).

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).

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).

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).

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

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).

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

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).

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

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

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).

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).

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

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).

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

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

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

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

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).

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).

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

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).

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).

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).

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

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

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

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

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

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).

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

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).

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).

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).

Esteban, O. et al. MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites. PloS One 12, e0184661 (2017).

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).

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

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).

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

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).

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

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