Book contents
- The Cambridge Handbook of Human Affective Neuroscience
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Human Affective Neuroscience
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Section I Theoretical Models of Emotion
- Section II Measuring Emotional Processes
- Chapter 3 Neuropsychology and Lesion Models
- Chapter 4 Peripheral Physiological Measures of Emotion
- Chapter 5 Functional MRI
- Chapter 6 Mapping the Human Emotion Circuits with Positron Emission Tomography
- Chapter 7 Electro- and Magnetoencephalography
- Chapter 8 The Study of Emotion in Other Animals
- Section III Emotion Perception and Elicitation
- Section IV Emotional Learning and Memory
- Section V Cognition–Emotion Interactions
- Section VI Social Emotions
- Section VII Individual Differences
- Index
- References
Chapter 5 - Functional MRI
Principles and Applications in Affective Neuroscience
from Section II - Measuring Emotional Processes
Published online by Cambridge University Press: 16 September 2025
Book contents
- The Cambridge Handbook of Human Affective Neuroscience
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Human Affective Neuroscience
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Section I Theoretical Models of Emotion
- Section II Measuring Emotional Processes
- Chapter 3 Neuropsychology and Lesion Models
- Chapter 4 Peripheral Physiological Measures of Emotion
- Chapter 5 Functional MRI
- Chapter 6 Mapping the Human Emotion Circuits with Positron Emission Tomography
- Chapter 7 Electro- and Magnetoencephalography
- Chapter 8 The Study of Emotion in Other Animals
- Section III Emotion Perception and Elicitation
- Section IV Emotional Learning and Memory
- Section V Cognition–Emotion Interactions
- Section VI Social Emotions
- Section VII Individual Differences
- Index
- References
Summary
Functional magnetic resonance imaging (fMRI) is a noninvasive technique widely used in research to identify and characterize the neural correlates of human cognitive and affective processes. Here we provide a brief introduction to the physical and physiological bases of fMRI, as well as a description of some of the main analysis approaches. These include traditional approaches, such as those based on univariate general linear models, as well as more recent ones, including multivariate methods and connectivity measures. We discuss how these different techniques can be used to answer different, complementary scientific questions, providing some examples to illustrate this. We end with a discussion of some of the key issues, both in terms of experimental design and data acquisition, analysis, and interpretation, that should be considered when planning an fMRI study and that can be of particular interest to those new to the technique.
Information
- Type
- Chapter
- Information
- The Cambridge Handbook of Human Affective Neuroscience , pp. 101 - 117Publisher: Cambridge University PressPrint publication year: 2025
References
Agcaoglu, O., Wilson, T. W., Wang, Y. P., Stephen, J., & Calhoun, V. D. (2019). Resting state connectivity differences in eyes open versus eyes closed conditions. Human Brain Mapping, 40, 2488–2498.CrossRefGoogle ScholarPubMed
Baez-Lugo, S., Deza-Araujo, Y. I., Maradan, C., Collette, F., Lutz, A., Marchant, N. L., … Medit-Ageing Research Group (2023). Exposure to negative socio-emotional events induces sustained alteration of resting-state brain networks in older adults. Nature Aging, 3, 105–120.CrossRefGoogle ScholarPubMed
Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., & Hyde, J. S. (1992). Time course EPI of human brain function during task activation. Magnetic Resonance in Medicine, 25, 390–397.CrossRefGoogle ScholarPubMed
Barrett, L. F., & Satpute, A. B. (2013). Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain. Current Opinion in Neurobiology, 23, 361–372.CrossRefGoogle ScholarPubMed
Bestelmeyer, P. E. G., Maurage, P., Rouger, J., Latinus, M., & Belin, P. (2014). Adaptation to vocal expressions reveals multistep perception of auditory emotion. Journal of Neuroscience, 34, 8098–8105.CrossRefGoogle ScholarPubMed
Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., … Prabhakaran, V. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage, 83, 550–558.CrossRefGoogle ScholarPubMed
Bordi, F., & LeDoux, J. (1992). Sensory tuning beyond the sensory system: An initial analysis of auditory response properties of neurons in the lateral amygdaloid nucleus and overlying areas of the striatum. Journal of Neuroscience, 12, 2493–2503.CrossRefGoogle ScholarPubMed
Carlson, H., Leitao, J., Delplanque, S., Cayeux, I., Sander, D., & Vuilleumier, P. (2020). Sustained effects of pleasant and unpleasant smells on resting state brain activity. Cortex, 132, 386–403.CrossRefGoogle ScholarPubMed
Chen, G., Taylor, P. A., Reynolds, R. C., Leibenluft, E., Pine, D. S., Brotman, M. A., … Haller, S. P. (2023). BOLD response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles. NeuroImage, 277, 120224.CrossRefGoogle ScholarPubMed
Chen, J. E., Polimeni, J. R., Bollmann, S., & Glover, G. H. (2019). On the analysis of rapidly sampled fMRI data. NeuroImage, 188, 807–820.CrossRefGoogle ScholarPubMed
Chen, N. K., Dickey, C. C., Yoo, S. S., Guttmann, C. R., & Panych, L. P. (2003). Selection of voxel size and slice orientation for fMRI in the presence of susceptibility field gradients: Application to imaging of the amygdala. NeuroImage, 19, 817–825.CrossRefGoogle ScholarPubMed
Corradi-Dell’Acqua, C., Hofstetter, C., Sharvit, G., Hugli, O., & Vuilleumier, P. (2023). Healthcare experience affects pain-specific responses to others’ suffering in the anterior insula. Human Brain Mapping, 44, 5655–5671.Google ScholarPubMed
de Zwart, J. A., van Gelderen, P., Jansma, J. M., Fukunaga, M., Bianciardi, M., & Duyn, J. H. (2009). Hemodynamic nonlinearities affect BOLD fMRI response timing and amplitude. NeuroImage, 47, 1649–1658.CrossRefGoogle ScholarPubMed
Donders, F. C. (1969). Over de snelheid van psychische processen [On the speed of psychological processes]. In Koster, W. G. & Instituut voor Perceptie Onderzoek (Eindhoven Netherlands) (Eds.), Attention and performance II: Proceedings of the Donders Centenary Symposium on Reaction time, held in Eindhoven, July 29–August 2, 1968 (pp. 412–431). North-Holland Publishing.Google Scholar
Eryilmaz, H., Van De Ville, D., Schwartz, S., & Vuilleumier, P. (2011). Impact of transient emotions on functional connectivity during subsequent resting state: A wavelet correlation approach. NeuroImage, 54, 2481–2491.CrossRefGoogle Scholar
Faraday, M. (1846). Experimental researches in electricity. Nineteenth series. On the magnetization of light and the illumination of magnetic lines of force. Philosophical Transactions of the Royal Society of London, 136, 1–20.Google Scholar
Fernandez, B., Leuchs, L., Sämann, P. G., Czisch, M., & Spoormaker, V. I. (2017). Multi-echo EPI of human fear conditioning reveals improved BOLD detection in ventromedial prefrontal cortex. NeuroImage, 156, 65–77.CrossRefGoogle ScholarPubMed
Finn, E. S. (2021). Is it time to put rest to rest? Trends in Cognitive Sciences, 25, 1021–1032.CrossRefGoogle ScholarPubMed
Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8, 700–711.CrossRefGoogle ScholarPubMed
Frassle, S., Lomakina, E. I., Razi, A., Friston, K. J., Buhmann, J. M., & Stephan, K. E. (2017). Regression DCM for fMRI. NeuroImage, 155, 406–421.CrossRefGoogle ScholarPubMed
Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connect, 1, 13–36.CrossRefGoogle ScholarPubMed
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). Psychophysiological and modulatory interactions in neuroimaging. NeuroImage, 6, 218–229.CrossRefGoogle ScholarPubMed
Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15, 870–878.CrossRefGoogle ScholarPubMed
Gerchen, M. F., Bernal-Casas, D., & Kirsch, P. (2014). Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: A comparison to conventional analysis. Human Brain Mapping, 35, 5071–5082.CrossRefGoogle ScholarPubMed
Gonzalez-Castillo, J., Kam, J. W. Y., Hoy, C. W., & Bandettini, P. A. (2021). How to interpret resting-state fMRI: Ask your participants. Journal of Neuroscience, 41, 1130–1141.CrossRefGoogle ScholarPubMed
Haacke, E. M., Brown, R. W., Thompson, M. R., & Venkatesan, R. (1999). Magnetic resonance imaging: Physical principles and sequence design. Wiley-Liss.Google Scholar
Handwerker, D. A., Gonzalez-Castillo, J., D’Esposito, M., & Bandettini, P. A. (2012). The continuing challenge of understanding and modeling hemodynamic variation in fMRI. NeuroImage, 62, 1017–1023.CrossRefGoogle ScholarPubMed
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303, 1634–1640.CrossRefGoogle ScholarPubMed
Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37, 435–456.CrossRefGoogle ScholarPubMed
Huettel, S. A., Song, A. W., & McCarthy, G. (2014). Functional magnetic resonance imaging, 3rd ed. Sinauer Associates.Google Scholar
Jezzard, P., Matthews, P. M., & Smith, S. M. (2001). Functional MRI: An introduction to methods. Oxford University Press.Google Scholar
Kragel, P. A., Koban, L., Barrett, L. F., & Wager, T. D. (2018). Representation, pattern information, and brain signatures: From neurons to neuroimaging. Neuron, 99, 257–273.CrossRefGoogle ScholarPubMed
Kragel, P. A., & LaBar, K. S. (2015). Multivariate neural biomarkers of emotional states are categorically distinct. Social Cognitive and Affective Neuroscience, 10, 1437–1448.CrossRefGoogle ScholarPubMed
Kragel, P. A., Reddan, M. C., LaBar, K. S., & Wager, T. D. (2019). Emotion schemas are embedded in the human visual system. Science Advances, 5, eaaw4358.CrossRefGoogle ScholarPubMed
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., … Turner, R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences of the United States of America, 89, 5675–5679.Google ScholarPubMed
Larsson, J., Solomon, S. G., & Kohn, A. (2016). fMRI adaptation revisited. Cortex, 80, 154–160.CrossRefGoogle ScholarPubMed
Leitão, J., Burckhardt, M., & Vuilleumier, P. (2022). Amygdala in action: Functional connectivity during approach and avoidance behaviors. Journal of Cognitive Neuroscience, 34, 729–747.CrossRefGoogle ScholarPubMed
Logan, B. R., & Rowe, D. B. (2004). An evaluation of thresholding techniques in fMRI analysis. NeuroImage, 22, 95–108.CrossRefGoogle ScholarPubMed
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150–157.CrossRefGoogle ScholarPubMed
Ma, N., Baetens, K., Vandekerckhove, M., Van Der Cruyssen, L., & Van Overwalle, F. (2014). Dissociation of a trait and a valence representation in the mPFC. Social Cognitive and Affective Neuroscience, 9, 1506–1514.CrossRefGoogle Scholar
McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. NeuroImage, 61, 1277–1286.CrossRefGoogle ScholarPubMed
Merboldt, K. D., Fransson, P., Bruhn, H., & Frahm, J. (2001). Functional MRI of the human amygdala? NeuroImage, 14, 253–257.CrossRefGoogle ScholarPubMed
Mohammadi, G., Van De Ville, D., & Vuilleumier, P. (2023). Brain networks subserving functional core processes of emotions identified with componential modeling. Cerebral Cortex, 33, 7993–8010.CrossRefGoogle ScholarPubMed
Morawetz, C., Holz, P., Lange, C., Baudewig, J., Weniger, G., Irle, E., & Dechent, P. (2008). Improved functional mapping of the human amygdala using a standard functional magnetic resonance imaging sequence with simple modifications. Magnetic Resonance Imaging, 26, 45–53.CrossRefGoogle ScholarPubMed
Morgenroth, E., Vilaclara, L., Muszynski, M., Gaviria, J., Vuilleumier, P., & Van De Ville, D. (2023). Probing neurodynamics of experienced emotions: A Hitchhiker’s guide to film fMRI. Social Cognitive and Affective Neuroscience, 18, nsad063.CrossRefGoogle ScholarPubMed
Noble, S., Spann, M. N., Tokoglu, F., Shen, X., Constable, R. T., & Scheinost, D. (2017). Influences on the test–retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cerebral Cortex, 27, 5415–5429.CrossRefGoogle ScholarPubMed
Nummenmaa, L., & Saarimäki, H. (2019). Emotions as discrete patterns of systemic activity. Neuroscience Letters, 693, 3–8.CrossRefGoogle ScholarPubMed
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87, 9868–9872.Google ScholarPubMed
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of America, 89, 5951–5955.Google ScholarPubMed
Pauling, L., & Coryell, C. D. (1936). The magnetic properties and structure of hemoglobin, oxyhemoglobin and carbonmonoxyhemoglobin. Proceedings of the National Academy of Sciences of the United States of America, 22, 210–216.Google ScholarPubMed
Peelen, M. V., Atkinson, A. P., & Vuilleumier, P. (2010). Supramodal representations of perceived emotions in the human brain. Journal of Neuroscience, 30, 10127–10134.CrossRefGoogle ScholarPubMed
Peelen, M. V., & Downing, P. E. (2023). Testing cognitive theories with multivariate pattern analysis of neuroimaging data. Nature Human Behaviour, 7, 1430–1441.CrossRefGoogle ScholarPubMed
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., & Nichols, T. E. (2007). Statistical parametric mapping: The analysis of functional brain images. Elsevier.Google Scholar
Preston, A. R., Thomason, M. E., Ochsner, K. N., Cooper, J. C., & Glover, G. H. (2004). Comparison of spiral-in/out and spiral-out BOLD fMRI at 1.5 and 3 T. NeuroImage, 21, 291–301.CrossRefGoogle ScholarPubMed
Preti, M. G., Bolton, T. A., & Van De Ville, D. (2017). The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage, 160, 41–54.CrossRefGoogle Scholar
Raichle, M. E. (2015). The brain’s default mode network. Annual Review of Neuroscience, 38, 433–447.CrossRefGoogle ScholarPubMed
Reinders, A. A., Glascher, J., de Jong, J. R., Willemsen, A. T., den Boer, J. A., & Buchel, C. (2006). Detecting fearful and neutral faces: BOLD latency differences in amygdala-hippocampal junction. NeuroImage, 33, 805–814.CrossRefGoogle ScholarPubMed
Risk, B. B., Murden, R. J., Wu, J., Nebel, M. B., Venkataraman, A., Zhang, Z., & Qiu, D. (2021). Which multiband factor should you choose for your resting-state fMRI study? NeuroImage, 234, 117965.CrossRefGoogle ScholarPubMed
Roy, C. S., & Sherrington, C. S. (1890). On the regulation of the blood-supply of the brain. Journal of Physiology, 11, 85–158.CrossRefGoogle ScholarPubMed
Saarimäki, H. (2021). Naturalistic stimuli in affective neuroimaging: A review. Frontiers in Human Neuroscience, 15, 675068.CrossRefGoogle ScholarPubMed
Sander, D., Grandjean, D., & Scherer, K. R. (2018). Brain networks, emotion components, and appraised relevance. Emotion Review, 10, 238–241.CrossRefGoogle Scholar
Scherer, K. R., & Moors, A. (2019). The emotion process: Event appraisal and component differentiation. Annual Review of Psychology, 70, 719–745.CrossRefGoogle ScholarPubMed
Sergerie, K., Chochol, C., & Armony, J. L. (2008). The role of the amygdala in emotional processing: A quantitative meta-analysis of functional neuroimaging studies. Neuroscience & Biobehavioral Reviews, 32, 811–830.CrossRefGoogle ScholarPubMed
Setsompop, K., Gagoski, B. A., Polimeni, J. R., Witzel, T., Wedeen, V. J., & Wald, L. L. (2012). Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g -factor penalty: Blipped-CAIPI for simultaneous multislice EPI. Magnetic Resonance in Medicine, 67, 1210–1224.CrossRefGoogle Scholar
Sharvit, G., Lin, E., Vuilleumier, P., & Corradi-Dell’Acqua, C. (2020). Does inappropriate behavior hurt or stink? The interplay between neural representations of somatic experiences and moral decisions. Science Advances, 6, eaat4390.CrossRefGoogle ScholarPubMed
Sherman, B. E., Harris, B. B., Turk-Browne, N. B., Sinha, R., & Goldfarb, E. V. (2023). Hippocampal mechanisms support cortisol-induced memory enhancements. The Journal of Neuroscience, 43, 7198–7212.CrossRefGoogle ScholarPubMed
Shmuel, A., & Grinvald, A. (1996). Functional organization for direction of motion and its relationship to orientation maps in cat area 18. Journal of Neuroscience, 16, 6945–6964.CrossRefGoogle ScholarPubMed
Shmuel, A., & Maier, A. (2022). Locally measured neuronal correlates of functional MRI signals. In Mulert, C. & Lemieux, L. (Eds.), EEG – fMRI: Physiological basis, technique, and applications (pp. 79–104). Springer.Google Scholar
Skerry, A. E., & Saxe, R. (2015). Neural representations of emotion are organized around abstract event features. Current Biology, 25, 1945–1954.CrossRefGoogle ScholarPubMed
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., … Woolrich, M. W. (2011). Network modelling methods for FMRI. NeuroImage, 54, 875–891.CrossRefGoogle ScholarPubMed
Sporns, O. (2018). Graph theory methods: Applications in brain networks. Dialogues Clinical Neuroscience, 20, 111–121.CrossRefGoogle ScholarPubMed
Takano, T., Tian, G. F., Peng, W., Lou, N., Libionka, W., Han, X., & Nedergaard, M. (2006). Astrocyte-mediated control of cerebral blood flow. Nature Neuroscience, 9, 260–267.CrossRefGoogle ScholarPubMed
Takata, N., Sugiura, Y., Yoshida, K., Koizumi, M., Hiroshi, N., Honda, K., … Tanaka, K. F. (2018). Optogenetic astrocyte activation evokes BOLD fMRI response with oxygen consumption without neuronal activity modulation. Glia, 66, 2013–2023.CrossRefGoogle ScholarPubMed
Truong, T. K., & Song, A. W. (2008). Single-shot dual-z-shimmed sensitivity-encoded spiral-in/out imaging for functional MRI with reduced susceptibility artifacts. Magnetic Resonance in Medicine, 59, 221–227.CrossRefGoogle ScholarPubMed
Whitehead, J. C., Spiousas, I., & Armony, J. L. (2024). Individual differences in the evaluation of ambiguous visual and auditory threat‐related expressions. European Journal of Neuroscience, 59, 370–393.CrossRefGoogle ScholarPubMed
Wilson, F. A., & Rolls, E. T. (1993). The effects of stimulus novelty and familiarity on neuronal activity in the amygdala of monkeys performing recognition memory tasks. Experimental Brain Research, 93, 367–382.CrossRefGoogle ScholarPubMed
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4, 58–73.3.0.CO;2-O>CrossRefGoogle ScholarPubMed
Accessibility standard: WCAG 2.0 A
The PDF of this book
conforms to version 2.0 of the
Web Content Accessibility Guidelines
(WCAG), ensuring core accessibility principles are addressed and meets the basic
(A)
level of WCAG compliance, addressing essential accessibility barriers.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visual Accessibility
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
Structural and Technical Features
ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.