Bridging the big (data) gap: levels of control in small- and large-scale cognitive neuroscience research
Authors: Roni Tibon, Linda Geerligs, Karen Campbell
Journal: Trends in Neurosciences (2022)

Studies in the field of cognitive neuroscience vary along two different axes: the first refers to their 'levels of control' (e.g., controlled task designs vs. resting-state paradigms) and the second refers to their sample size.Although theoretically these two axes can be orthogonal, in practice they often correlate: controlled designs generally dominate the field for small-scale studies, whereas uncontrolled designs are prevalent among large-scale studies.This results in qualitative differences between small- and large-scale studies, which are not due to their size per se, and poses challenges to the interpretation of the results. Recently, cognitive neuroscience has experienced unprecedented growth in the availability of large-scale datasets. These developments hold great methodological and theoretical promise: they allow increased statistical power, the use of nonparametric and generative models, the examination of individual differences, and more. Nevertheless, unlike most 'traditional' cognitive neuroscience research, which uses controlled experimental designs, large-scale projects often collect neuroimaging data not directly related to a particular task (e.g., resting state). This creates a gap between small- and large-scale studies that is not solely due to differences in sample size. Measures obtained with large-scale studies might tap into different neurocognitive mechanisms and thus show little overlap with the mechanisms probed by small-scale studies. In this opinion article, we aim to address this gap and its potential implications for the interpretation of research findings in cognitive neuroscience. Recently, cognitive neuroscience has experienced unprecedented growth in the availability of large-scale datasets. These developments hold great methodological and theoretical promise: they allow increased statistical power, the use of nonparametric and generative models, the examination of individual differences, and more. Nevertheless, unlike most 'traditional' cognitive neuroscience research, which uses controlled experimental designs, large-scale projects often collect neuroimaging data not directly related to a particular task (e.g., resting state). This creates a gap between small- and large-scale studies that is not solely due to differences in sample size. Measures obtained with large-scale studies might tap into different neurocognitive mechanisms and thus show little overlap with the mechanisms probed by small-scale studies. In this opinion article, we aim to address this gap and its potential implications for the interpretation of research findings in cognitive neuroscience. Studies in the field of cognitive neuroscience vary along two different axes. The first axis, termed here 'levels of control', refers to the nature of the designs that are employed: in some studies, the cognitive processes are driven by external input provided by the researcher (i.e., particular tasks or stimuli). Tight control over the experimental input allows the researcher to track the specific cognitive processes elicited by the task. At the other end of this continuum are studies in which cognitive processes are generated internally (i.e., without any external stimulus or task). With no external input, and without the ability to infer what participants are thinking, such studies entail low levels of control. The second axis refers to the size of the study: traditionally, studies in the field of cognitive neuroscience have relied on data collected from relatively few participants (often fewer than 20 per group, although see the later discussion of current trends). However, in recent years, large-scale studies, for which data are collected from hundreds or even thousands of participants, are becoming increasingly popular. Although theoretically these two axes can be orthogonal, in practice they often correlate. In this opinion article, we investigate these two axes in the context of recent advances in the field of cognitive neuroscience and demonstrate how these qualitative differences between small- and large-scale studies might pose considerable challenges to the interpretation of research results. Studies in the field of cognitive neuroscience employ different levels of control. The traditional approach in the field is that tightly controlled experimental manipulations can be applied to discover interpretable relationships between the brain and behavior. This notion relies on the assumption that carefully designed experimental tasks can be used to individuate cognitive processes, which can then be linked to specific neural mechanisms. For example, a central debate in the cognitive neuroscience of episodic memory revolved around the role of the hippocampus in episodic retrieval: whether it selectively supports recollection (i.e., the retrieval of qualitative information about a specific study episode), or otherwise supports the retrieval of 'strong' memories, regardless of whether their retrieval is achieved via recollection or familiarity-based judgments of prior occurrence [1.Moscovitch M. et al.Episodic memory and beyond: the hippocampus and neocortex in transformation.Annu. Rev. Psychol. 2016; 67: 105-134Crossref PubMed Scopus (462) Google Scholar]. By combining neuroimaging measures with a range of tightly controlled memory tasks (including source memory, context memory, and continuous recognition tasks), studies lent support to the notion that the hippocampus tracks the amount of episodic information that is retrieved, thereby selectively supporting recollection, rather than the strength of an undifferentiated memory signal [2.Rugg M.D. et al.Item memory, context memory and the hippocampus: fMRI evidence.Neuropsychologia. 2012; 50: 3070-3079Crossref PubMed Scopus (97) Google Scholar]. Other examples can be found in the fields of face perception (the face specificity vs. individuation/expertise debate [3.Kanwisher N. Domain specificity in face perception.Nat. Neurosci. 2000; 3: 759-763Crossref PubMed Scopus (670) Google Scholar, 4.Kanwisher N. et al.The fusiform face area: a module in human extrastriate cortex specialized for face perception.J. Neurosci. 1997; 17: 4302-4311Crossref PubMed Scopus (133) Google Scholar, 5.Grill-Spector K. et al.The fusiform face area subserves face perception, not generic within-category identification.Nat. Neurosci. 2004; 7: 555-562Crossref PubMed Scopus (721) Google Scholar, 6.Tarr M.J. Gauthier I. FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise.Nat. Neurosci. 2000; 3: 764-769Crossref PubMed Scopus (475) Google Scholar, 7.Gauthier I. et al.Activation of the middle fusiform "face area" increases with expertise in recognizing novel objects.Nat. Neurosci. 1999; 2: 568-573Crossref PubMed Scopus (924) Google Scholar]), visual working memory (representations maintained in frontoparietal vs. occipital regions [8.Teng C. Postle B.R. Understanding occipital and parietal contributions to visual working memory: commentary on Xu (2020).Vis. Cogn. 2021; 29: 401-408Crossref PubMed Scopus (3) Google Scholar, 9.Scimeca J.M. et al.Reaffirming the sensory recruitment account of working memory.Trends Cogn. Sci. 2018; 22: 190-192Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar, 10.Gayet S. et al.Visual working memory storage recruits sensory processing areas.Trends Cogn. Sci. 2018; 22: 189-190Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar]), and syntactic processing (a specific role in syntactic processing vs. a general role in cognition for the left inferior frontal gyrus [11.Tyler L.K. et al.Left inferior frontal cortex and syntax: function, structure and behaviour in patients with left hemisphere damage.Brain. 2011; 134: 415-431Crossref PubMed Scopus (164) Google Scholar]). Over the years, this combination of neuroimaging measures and tightly controlled experimental designs allowed cognitive neuroscientists to make immense progress in characterizing and understanding the relations between cognitive and neural mechanisms via multiple imaging modalities (Box 1) and across multiple cognitive domains.Box 1Functional neuroimaging modalities in cognitive neuroscienceA variety of neuroimaging modalities, including fMRI, electroencephalography (EEG), MEG, positron emission tomography (PET)/computed tomography (CT), and functional near-IR spectroscopy (fNIRS), are used in the field of cognitive neuroscience, often providing different perspectives or complementary evidence that relies on the unique strengths offered by each technique. Nevertheless, in large-scale projects, there is a clear dominance of fMRI. In a recent review of open-access neuroimaging datasets [26.Madan C.R. Scan once, analyse many: using large open-access neuroimaging datasets to understand the brain.Neuroinformatics. 2021; (Published online May 11, 2021)https://doi.org/10.1007/s12021-021-09519-6Crossref PubMed Scopus (7) Google Scholar], of the 61 large-scale projects (n > 100) involving human participants, 37 included fMRI data but only nine included EEG and/or MEG data (we are aware of only two additional large-scale projects involving M/EEG data, which were not included in that review [48.Harati A. et al.The TUH EEG CORPUS: a big data resource for automated EEG interpretation.in: 2014 Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2014: 1-5Crossref Scopus (51) Google Scholar,49.Vaghari D. et al.A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: the BioFIND dataset.medRxiv. 2021; (Published online May 19, 2021)https://doi.org/10.1101/2021.05.19.21257330Google Scholar]).Why some modalities are being relatively overlooked in large-scale projects is not entirely clear. Possible reasons for the popularity of fMRI in large-scale projects include the straightforward combination with structural brain metrics and the greater spatial resolution of fMRI. Nevertheless, given that EEG is widely available and frequently used in small-scale studies, while also entailing relatively low costs, one might expect EEG to be at least as popular as fMRI for large-scale projects. Thus, another reason for the popularity of fMRI in large-scale projects may stem from the fact that resting-state networks were first established with fMRI [12.Damoiseaux J.S. et al.Consistent resting-state networks across healthy subjects.Proc. Natl. Acad. Sci. U. S. A. 2006; 103: 13848-13853Crossref PubMed Scopus (3183) Google Scholar, 13.Raichle M.E. et al.A default mode of brain function.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 676-682Crossref PubMed Scopus (8774) Google Scholar, 14.Greicius M.D. et al.Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 253-258Crossref PubMed Scopus (4752) Google Scholar], and resting-state data are relatively easy to collect on a grand scale (see main text). Although resting-state networks have also been observed using MEG/EEG [50.Khanna A. et al.Microstates in resting-state EEG: current status and future directions.Neurosci. Biobehav. Rev. 2015; 49: 105-113Crossref PubMed Scopus (299) Google Scholar, 51.O'Neill G.C. et al.Dynamics of large-scale electrophysiological networks: a technical review.NeuroImage. 2018; 180: 559-576Crossref PubMed Scopus (100) Google Scholar, 52.Newson J.J. Thiagarajan T.C. EEG frequency bands in psychiatric disorders: a review of resting state studies.Front. Hum. Neurosci. 2019; 12: 521Crossref PubMed Scopus (201) Google Scholar, 53.Baker A.P. et al.Fast transient networks in spontaneous human brain activity.eLife. 2014; 3e01867Crossref Scopus (271) Google Scholar, 54.Tibon R. et al.Transient neural network dynamics in cognitive ageing.Neurobiol. Aging. 2021; 105: 217-228Crossref PubMed Scopus (8) Google Scholar], the typical way of analyzing such data remains task focused, and there is less consensus regarding resting-state connectivity metrics [55.Colclough G.L. et al.How reliable are MEG resting-state connectivity metrics?.NeuroImage. 2016; 138: 284-293Crossref PubMed Scopus (206) Google Scholar]. Nevertheless, the investigation of the temporal dynamics of resting-state networks might greatly benefit from the superior temporal resolution of these modalities.Despite this dominance of fMRI in large-scale projects, it might not be the optimal modality for every purpose. It is important to consider, we would argue, a range of methods, and rather than seeing fMRI as a 'default' modality, to carefully select the methods that seem best suited to the specific research question(s) at hand. A variety of neuroimaging modalities, including fMRI, electroencephalography (EEG), MEG, positron emission tomography (PET)/computed tomography (CT), and functional near-IR spectroscopy (fNIRS), are used in the field of cognitive neuroscience, often providing different perspectives or complementary evidence that relies on the unique strengths offered by each technique. Nevertheless, in large-scale projects, there is a clear dominance of fMRI. In a recent review of open-access neuroimaging datasets [26.Madan C.R. Scan once, analyse many: using large open-access neuroimaging datasets to understand the brain.Neuroinformatics. 2021; (Published online May 11, 2021)https://doi.org/10.1007/s12021-021-09519-6Crossref PubMed Scopus (7) Google Scholar], of the 61 large-scale projects (n > 100) involving human participants, 37 included fMRI data but only nine included EEG and/or MEG data (we are aware of only two additional large-scale projects involving M/EEG data, which were not included in that review [48.Harati A. et al.The TUH EEG CORPUS: a big data resource for automated EEG interpretation.in: 2014 Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2014: 1-5Crossref Scopus (51) Google Scholar,49.Vaghari D. et al.A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: the BioFIND dataset.medRxiv. 2021; (Published online May 19, 2021)https://doi.org/10.1101/2021.05.19.21257330Google Scholar]). Why some modalities are being relatively overlooked in large-scale projects is not entirely clear. Possible reasons for the popularity of fMRI in large-scale projects include the straightforward combination with structural brain metrics and the greater spatial resolution of fMRI. Nevertheless, given that EEG is widely available and frequently used in small-scale studies, while also entailing relatively low costs, one might expect EEG to be at least as popular as fMRI for large-scale projects. Thus, another reason for the popularity of fMRI in large-scale projects may stem from the fact that resting-state networks were first established with fMRI [12.Damoiseaux J.S. et al.Consistent resting-state networks across healthy subjects.Proc. Natl. Acad. Sci. U. S. A. 2006; 103: 13848-13853Crossref PubMed Scopus (3183) Google Scholar, 13.Raichle M.E. et al.A default mode of brain function.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 676-682Crossref PubMed Scopus (8774) Google Scholar, 14.Greicius M.D. et al.Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 253-258Crossref PubMed Scopus (4752) Google Scholar], and resting-state data are relatively easy to collect on a grand scale (see main text). Although resting-state networks have also been observed using MEG/EEG [50.Khanna A. et al.Microstates in resting-state EEG: current status and future directions.Neurosci. Biobehav. Rev. 2015; 49: 105-113Crossref PubMed Scopus (299) Google Scholar, 51.O'Neill G.C. et al.Dynamics of large-scale electrophysiological networks: a technical review.NeuroImage. 2018; 180: 559-576Crossref PubMed Scopus (100) Google Scholar, 52.Newson J.J. Thiagarajan T.C. EEG frequency bands in psychiatric disorders: a review of resting state studies.Front. Hum. Neurosci. 2019; 12: 521Crossref PubMed Scopus (201) Google Scholar, 53.Baker A.P. et al.Fast transient networks in spontaneous human brain activity.eLife. 2014; 3e01867Crossref Scopus (271) Google Scholar, 54.Tibon R. et al.Transient neural network dynamics in cognitive ageing.Neurobiol. Aging. 2021; 105: 217-228Crossref PubMed Scopus (8) Google Scholar], the typical way of analyzing such data remains task focused, and there is less consensus regarding resting-state connectivity metrics [55.Colclough G.L. et al.How reliable are MEG resting-state connectivity metrics?.NeuroImage. 2016; 138: 284-293Crossref PubMed Scopus (206) Google Scholar]. Nevertheless, the investigation of the temporal dynamics of resting-state networks might greatly benefit from the superior temporal resolution of these modalities. Despite this dominance of fMRI in large-scale projects, it might not be the optimal modality for every purpose. It is important to consider, we would argue, a range of methods, and rather than seeing fMRI as a 'default' modality, to carefully select the methods that seem best suited to the specific research question(s) at hand. At the other end of the levels-of-control continuum, resting-state studies emerged as one form of uncontrolled design, often used as an alternative approach to map the relationship between trait-like brain networks and cognitive functions. These studies build on the important discovery that consistent functional resting-state networks are observed in the absence of any stimulus or task [12.Damoiseaux J.S. et al.Consistent resting-state networks across healthy subjects.Proc. Natl. Acad. Sci. U. S. A. 2006; 103: 13848-13853Crossref PubMed Scopus (3183) Google Scholar, 13.Raichle M.E. et al.A default mode of brain function.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 676-682Crossref PubMed Scopus (8774) Google Scholar, 14.Greicius M.D. et al.Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 253-258Crossref PubMed Scopus (4752) Google Scholar]. In a typical resting-state study, neural data are acquired while participants are not exposed to any controlled external input (i.e., specific task or stimuli). In some cases, additional data are collected (e.g., data from behavioral tasks performed outside the scanner) and linked to neural patterns that are observed at rest. This approach has been widely applied to the study of various populations and clinical applications [15.Arbabshirani M. et al.Classification of schizophrenia patients based on resting-state functional network connectivity.Front. Neurosci. 2013; 7: 133Crossref PubMed Scopus (122) Google Scholar]. In addition, commonalities between resting-state networks and task activation networks have been observed [16.Smith S.M. et al.Correspondence of the brain's functional architecture during activation and rest.Proc. Natl. Acad. Sci. U. S. A. 2009; 106: 13040-13045Crossref PubMed Scopus (3487) Google Scholar,17.Tavor I. et al.Task-free MRI predicts individual differences in brain activity during task performance.Science. 2016; 352: 216-220Crossref PubMed Scopus (403) Google Scholar], suggesting that both are largely shaped by a shared and stable intrinsic network architecture [18.Cole M.W. et al.Intrinsic and task-evoked network architectures of the human brain.Neuron. 2014; 83: 238-251Abstract Full Text Full Text PDF PubMed Scopus (864) Google Scholar]. It is important to note, however, that despite the prevalence of resting-state designs, especially in large-scale studies, resting-state data are not unique in how they can be analyzed, and methods that measure functional connectivity can be similarly applied to task data [19.Campbell K.L. Schacter D.L. Ageing and the resting state: is cognition obsolete?.Lang. Cogn. Neurosci. 2017; 32: 661-668Crossref PubMed Scopus (35) Google Scholar,20.Finn E.S. Is it time to put rest to rest?.Trends Cogn. Sci. 2021; 25: 1021-1032Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar]. In addition to tightly controlled experimental designs and resting-state designs, other designs are becoming increasingly popular (Box 2), providing additional opportunities for the investigation of human neurocognition.Box 2Naturalistic designs: beyond controlled tasks and resting-state dataIn the field of cognitive neuroscience, the recent decade has been characterized by increasing methodological versatility. This has provided opportunities for innovative study designs that are different from the more traditional ones. In particular, designs that incorporate naturalistic stimuli or behaviors are gaining popularity. For example, in movie-viewing paradigms, participants watch a movie without performing any particular task (other than following a general instruction to 'watch the movie') while neural measures are collected [35.Finn E.S. et al.Idiosynchrony: from shared responses to individual differences during naturalistic neuroimaging.NeuroImage. 2020; 215116828Crossref PubMed Scopus (43) Google Scholar,56.Hasson U. et al.Reliability of cortical activity during natural stimulation.Trends Cogn. Sci. 2010; 14: 40-48Abstract Full Text Full Text PDF PubMed Scopus (362) Google Scholar, 57.Ben-Yakov A. Henson R.N. The hippocampal film editor: sensitivity and specificity to event boundaries in continuous experience.J. Neurosci. 2018; 38: 10057-10068Crossref PubMed Scopus (62) Google Scholar, 58.Geerligs L. Campbell K.L. Age-related differences in information processing during movie watching.Neurobiol. Aging. 2018; 72: 106-120Crossref PubMed Scopus (15) Google Scholar, 59.Kauttonen J. et al.Brain mechanisms underlying cue-based memorizing during free viewing of movie Memento.NeuroImage. 2018; 172: 313-325Crossref PubMed Scopus (17) Google Scholar]. Similarly, in ongoing narrative-processing paradigms, participants might listen to a story, a conversation, or a lecture, again provided with some general or minimal instructions [59.Kauttonen J. et al.Brain mechanisms underlying cue-based memorizing during free viewing of movie Memento.NeuroImage. 2018; 172: 313-325Crossref PubMed Scopus (17) Google Scholar,60.Meshulam M. et al.Neural alignment predicts learning outcomes in students taking an introduction to computer science course.Nat. Commun. 2021; 12: 1922Crossref PubMed Scopus (10) Google Scholar]. Such tasks comprise a continuous input stream, providing a more ecological model for how information is obtained and processed in 'real-world' situations. Arguably, however, the naturalistic characteristics of such tasks are constrained by the fact that, unlike our real-world experiences, in which we act and interact with the stimuli that surround us, the range of behaviors afforded by these tasks remains limited. To account for these limitations, recent research has incorporated interactive behaviors, such as continuous speech or navigation in a virtual-reality environment, to better approach naturalistic cognition [61.Chen J. et al.Shared memories reveal shared structure in neural activity across individuals.Nat. Neurosci. 2017; 20: 115-125Crossref PubMed Scopus (235) Google Scholar, 62.Lenormand D. Piolino P. In search of a naturalistic neuroimaging approach: exploration of general feasibility through the case of VR-fMRI and application in the domain of episodic memory.Neurosci. Biobehav. Rev. 2022; 133104499Crossref PubMed Scopus (1) Google Scholar, 63.Zadbood A. et al.How we transmit memories to other brains: constructing shared neural representations via communication.Cereb. Cortex. 2017; 27: 4988-5000Crossref PubMed Scopus (69) Google Scholar]. Although the production of naturalistic behaviors within the confines of an fMRI or MEG scanner is undoubtedly challenging, such research benefits from recent methodological developments that allow better mobility of neuroimaging techniques, such as mobile EEG devices and wearable MEG systems [64.Roberts G. et al.Towards OPM-MEG in a virtual reality environment.NeuroImage. 2019; 199: 408-417Crossref PubMed Scopus (40) Google Scholar, 65.Seymour R.A. et al.Interference suppression techniques for OPM-based MEG: opportunities and challenges.NeuroImage. 2022; 247118834Crossref PubMed Scopus (5) Google Scholar, 66.Liebherr M. et al.EEG and behavioral correlates of attentional processing while walking and navigating naturalistic environments.Sci. Rep. 2021; 11: 22325Crossref PubMed Scopus (3) Google Scholar, 67.Krugliak A. Clarke A. Towards real-world neuroscience using mobile EEG and augmented reality.Sci. Rep. 2022; 12: 2291Crossref PubMed Scopus (2) Google Scholar].One of the initial motivations to use such 'naturalistic' designs was rooted in the long-standing critique that because of the isolated and artificial nature of tightly controlled tasks, the conclusions they generate often fail to generalize outside the laboratory into real-life contexts [68.Hasson U. Honey C.J. Future trends in neuroimaging: neural processes as expressed within real-life contexts.NeuroImage. 2012; 62: 1272-1278Crossref PubMed Scopus (101) Google Scholar, 69.Nastase S.A. et al.Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.NeuroImage. 2020; 222117254Crossref PubMed Scopus (46) Google Scholar, 70.Maguire E.A. Studying the freely-behaving brain with fMRI.NeuroImage. 2012; 62: 1170-1176Crossref PubMed Scopus (34) Google Scholar, 71.Hamilton L.S. Huth A.G. The revolution will not be controlled: natural stimuli in speech neuroscience.Lang. Cogn. Neurosci. 2020; 35: 573-582Crossref PubMed Scopus (58) Google Scholar, 72.Matusz P.J. et al.Are we ready for real-world neuroscience?.J. Cogn. Neurosci. 2019; 31: 327-338Crossref PubMed Scopus (73) Google Scholar, 73.Sonkusare S. et al.Naturalistic stimuli in neuroscience: critically acclaimed.Trends Cogn. Sci. 2019; 23: 699-714Abstract Full Text Full Text PDF PubMed Scopus (125) Google Scholar]. Moreover, although naturalistic paradigms do not have the experimental efficiency of targeted manipulations [71.Hamilton L.S. Huth A.G. The revolution will not be controlled: natural stimuli in speech neuroscience.Lang. Cogn. Neurosci. 2020; 35: 573-582Crossref PubMed Scopus (58) Google Scholar], they offer better understanding of the relative contributions of different domains to real-world phenomena [74.Haxby J.V. et al.Naturalistic stimuli reveal a dominant role for agentic action in visual representation.NeuroImage. 2020; 216116561Crossref Scopus (18) Google Scholar]. Importantly, these designs also overcome some of the limitations of uncontrolled resting-state designs [19.Campbell K.L. Schacter D.L. Ageing and the resting state: is cognition obsolete?.Lang. Cogn. Neurosci. 2017; 32: 661-668Crossref PubMed Scopus (35) Google Scholar], for example, by maintaining the complex, multidimensional, and ongoing nature of sensory input that the brain has evolved to capitalize on to guide behavior [69.Nastase S.A. et al.Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.NeuroImage. 2020; 222117254Crossref PubMed Scopus (46) Google Scholar]. Naturalistic stimuli have also been shown to enhance meaningful individual differences in functional connectivity [75.Finn E.S. Bandettini P.A. Movie-watching outperforms rest for functional connectivity-based prediction of behavior.NeuroImage. 2021; 235117963Crossref PubMed Scopus (28) Google Scholar] and provide more direct links between neural and cognitive mechanisms than resting-state studies (e.g., by using annotated stimulus features and narrative structure [20.Finn E.S. Is it time to put rest to rest?.Trends Cogn. Sci. 2021; 25: 1021-1032Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar,57.Ben-Yakov A. Henson R.N. The hippocampal film editor: sensitivity and specificity to event boundaries in continuous experience.J. Neurosci. 2018; 38: 10057-10068Crossref PubMed Scopus (62) Google Scholar,76.Kurby C.A. Zacks J.M. Preserved neural event segmentation in healthy older adults.Psychol. Aging. 2018; 33: 232-245Crossref PubMed Scopus (14) Google Scholar, 77.Michelmann S. et al.Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions.Nat. Commun. 2021; 12: 5394Crossref PubMed Scopus (6) Google Scholar, 78.Chang C.H.C. et al.Relating the past with the present: information integration and segregation during ongoing narrative processing.J. Cogn. Neurosci. 2021; 33: 1106-1128Crossref PubMed Scopus (12) Google Scholar]). In the context of large-scale studies, the great advantage of naturalistic paradigms is the richness of the data they produce. This richness allows these data to be used to answer a wider range of research questions than typical task-controlled or resting-state studies. For example, a spoken narrative can be used to investigate questions ranging from the domain of word meaning or syntactic processing to the perception of emotions and social cognition [69.Nastase S.A. et al.Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.NeuroImage. 2020; 222117254Crossref PubMed Scopus (46) Google Scholar,71.Hamilton L.S. Huth A.G. The revolution will not be controlled: natural stimuli in speech neuroscience.Lang. Cogn. Neurosci. 2020; 35: 573-582Crossref PubMed Scopus (58) Google Scholar,79.Willems R.M. et al.Narratives for neuroscience.Trends Neurosci. 2020; 43: 271-273Abstract Full Text Full Text PDF PubMed Scopus (19) Google Scholar]. At the practical level (i.e., 'ease of use'), these designs are comparable with resting-state designs and therefore are relatively easy to implement in large-scale studies, and have been shown to reduce head motion in the scanner relative to rest [80.Greene D.J. et al.Behavioral interventions for reducing head motion during MRI scans in children.NeuroImage. 2018; 171: 234-245Crossref PubMed Scopus (80) Google Scholar]. When these are complemented by controlled tasks, datasets will also allow mor