{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:59:28Z","timestamp":1780347568265,"version":"3.54.1"},"reference-count":76,"publisher":"MIT Press","issue":"7","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The frontoparietal \u201cmultiple-demand\u201d (MD) control network plays a key role in goal-directed behavior. Recent developments of multivoxel pattern analysis (MVPA) for fMRI data allow for more fine-grained investigations into the functionality and properties of brain systems. In particular, MVPA in the MD network was used to gain better understanding of control processes such as attentional effects, adaptive coding, and representation of multiple task-relevant features, but overall low decoding levels have limited its use for this network. A common practice of applying MVPA is by investigating pattern discriminability within a ROI using a template mask, thus ensuring that the same brain areas are studied in all participants. This approach offers high sensitivity but does not take into account differences between individuals in the spatial organization of brain regions. An alternative approach uses independent localizer data for each subject to select the most responsive voxels and define individual ROIs within the boundaries of a group template. Such an approach allows for a refined and targeted localization based on the unique pattern of activity of individual subjects while ensuring that functionally similar brain regions are studied for all subjects. In the current study, we tested whether using individual ROIs leads to changes in decodability of task-related neural representations as well as univariate activity across the MD network compared with when using a group template. We used three localizer tasks to separately define subject-specific ROIs: spatial working memory, verbal working memory, and a Stroop task. We then systematically assessed univariate and multivariate results in a separate rule-based criterion task. All the localizer tasks robustly recruited the MD network and evoked highly reliable activity patterns in individual subjects. Consistent with previous studies, we found a clear benefit of the subject-specific ROIs for univariate results from the criterion task, with increased activity in the individual ROIs based on the localizers' data, compared with the activity observed when using the group template. In contrast, there was no benefit of the subject-specific ROIs for the multivariate results in the form of increased discriminability, as well as no cost of reduced discriminability. Both univariate and multivariate results were similar in the subject-specific ROIs defined by each of the three localizers. Our results provide important empirical evidence for researchers in the field of cognitive control for the use of individual ROIs in the frontoparietal network for both univariate and multivariate analysis of fMRI data and serve as another step toward standardization and increased comparability across studies.<\/jats:p>","DOI":"10.1162\/jocn_a_01554","type":"journal-article","created":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T10:19:51Z","timestamp":1582885191000},"page":"1348-1368","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":45,"title":["Individual-subject Functional Localization Increases Univariate Activation but Not Multivariate Pattern Discriminability in the \u201cMultiple-demand\u201d Frontoparietal Network"],"prefix":"10.1162","volume":"32","author":[{"given":"Sneha","family":"Shashidhara","sequence":"first","affiliation":[{"name":"MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Floortje S.","family":"Spronkers","sequence":"additional","affiliation":[{"name":"MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaara","family":"Erez","sequence":"additional","affiliation":[{"name":"MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"key":"2022042815223314000_bib1","doi-asserted-by":"crossref","unstructured":"Ahlheim,  C., & Love,  B. C. (2018). Estimating the functional dimensionality of neural representations. Neuroimage, 179, 51\u201362.","DOI":"10.1016\/j.neuroimage.2018.06.015"},{"key":"2022042815223314000_bib2","doi-asserted-by":"crossref","unstructured":"Allefeld,  C., G\u00f6rgen,  K., & Haynes,  J.-D. (2016). Valid population inference for information-based imaging: From the second-level t test to prevalence inference. Neuroimage, 141, 378\u2013392.","DOI":"10.1016\/j.neuroimage.2016.07.040"},{"key":"2022042815223314000_bib3","doi-asserted-by":"crossref","unstructured":"Assem,  M., Glasser,  M. F., Van Essen,  D. C., & Duncan,  J. (2020). A domain-general cognitive core defined in multimodally parcellated human cortex. BioRxiv, 517599.","DOI":"10.1101\/517599"},{"key":"2022042815223314000_bib4","doi-asserted-by":"crossref","unstructured":"Berman,  M. G., Park,  J., Gonzalez,  R., Polk,  T. A., Gehrke,  A., Knaffla,  S., et al (2010). Evaluating functional localizers: The case of the FFA. Neuroimage, 50, 56\u201371.","DOI":"10.1016\/j.neuroimage.2009.12.024"},{"key":"2022042815223314000_bib5","doi-asserted-by":"crossref","unstructured":"Bhandari,  A., Gagne,  C., & Badre,  D. (2018). Just above chance: Is it harder to decode information from prefrontal cortex hemodynamic activity patterns?Journal of Cognitive Neuroscience, 30, 1473\u20131498.","DOI":"10.1162\/jocn_a_01291"},{"key":"2022042815223314000_bib6","doi-asserted-by":"crossref","unstructured":"Blank,  I. A., & Fedorenko,  E. (2017). Domain-general brain regions do not track linguistic input as closely as language-selective regions. Journal of Neuroscience, 37, 9999\u201310011.","DOI":"10.1523\/JNEUROSCI.3642-16.2017"},{"key":"2022042815223314000_bib7","doi-asserted-by":"crossref","unstructured":"Blank,  I. A., Kanwisher,  N., & Fedorenko,  E. (2014). A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations. Journal of Neurophysiology, 112, 1105\u20131118.","DOI":"10.1152\/jn.00884.2013"},{"key":"2022042815223314000_bib8","doi-asserted-by":"crossref","unstructured":"Brainard,  D. H.\n           (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433\u2013436.","DOI":"10.1163\/156856897X00357"},{"key":"2022042815223314000_bib9","unstructured":"Brett,  M., Anton,  J.-L., Valabregue,  R., & Poline,  J.-B. (2002). Region of interest analysis using an SPM toolbox [Abstract]. Neuroimage, 16, Abstract 497. Retrieved from matthew.dynevor.org\/research\/abstracts\/marsbar\/marsbar_abstract.pdf"},{"key":"2022042815223314000_bib10","doi-asserted-by":"crossref","unstructured":"Brett,  M., Johnsrude,  I. S., & Owen,  A. M. (2002). The problem of functional localization in the human brain. Nature Reviews Neuroscience, 3, 243\u2013249.","DOI":"10.1038\/nrn756"},{"key":"2022042815223314000_bib11","doi-asserted-by":"crossref","unstructured":"Carlin,  J. D., & Kriegeskorte,  N. (2017). Adjudicating between face-coding models with individual-face fMRI responses. PLoS Computational Biology, 13, e1005604.","DOI":"10.1371\/journal.pcbi.1005604"},{"key":"2022042815223314000_bib12","doi-asserted-by":"crossref","unstructured":"Cole,  M. W., Ito,  T., & Braver,  T. S. (2016). The behavioral relevance of task information in human prefrontal cortex. Cerebral Cortex, 26, 2497\u20132505.","DOI":"10.1093\/cercor\/bhv072"},{"key":"2022042815223314000_bib13","doi-asserted-by":"crossref","unstructured":"Cole,  M. W., Reynolds,  J. R., Power,  J. D., Repovs,  G., Anticevic,  A., & Braver,  T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16, 1348\u20131355.","DOI":"10.1038\/nn.3470"},{"key":"2022042815223314000_bib14","doi-asserted-by":"crossref","unstructured":"Cole,  M. W., & Schneider,  W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. Neuroimage, 37, 343\u2013360.","DOI":"10.1016\/j.neuroimage.2007.03.071"},{"key":"2022042815223314000_bib15","doi-asserted-by":"crossref","unstructured":"Crittenden,  B. M., Mitchell,  D. J., & Duncan,  J. (2015). Recruitment of the default mode network during a demanding act of executive control. eLife, 4, e06481.","DOI":"10.7554\/eLife.06481"},{"key":"2022042815223314000_bib16","doi-asserted-by":"crossref","unstructured":"Crittenden,  B. M., Mitchell,  D. J., & Duncan,  J. (2016). Task encoding across the multiple demand cortex is consistent with a frontoparietal and cingulo-opercular dual networks distinction. Journal of Neuroscience, 36, 6147\u20136155.","DOI":"10.1523\/JNEUROSCI.4590-15.2016"},{"key":"2022042815223314000_bib17","doi-asserted-by":"crossref","unstructured":"Curtis,  C. E., Cole,  M. W., Rao,  V. Y., & D'Esposito,  M. (2005). Canceling planned action: An fMRI study of countermanding saccades. Cerebral Cortex, 15, 1281\u20131289.","DOI":"10.1093\/cercor\/bhi011"},{"key":"2022042815223314000_bib18","doi-asserted-by":"crossref","unstructured":"Cusack,  R., Vicente-Grabovetsky,  A., Mitchell,  D. J., Wild,  C. J., Auer,  T., Linke,  A. C., et al (2015). Automatic analysis (aa): Efficient neuroimaging workflows and parallel processing using Matlab and XML. Frontiers in Neuroinformatics, 8, 90.","DOI":"10.3389\/fninf.2014.00090"},{"key":"2022042815223314000_bib19","doi-asserted-by":"crossref","unstructured":"Desimone,  R., & Duncan,  J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193\u2013222.","DOI":"10.1146\/annurev.ne.18.030195.001205"},{"key":"2022042815223314000_bib20","doi-asserted-by":"crossref","unstructured":"Dosenbach,  N. U. F., Fair,  D. A., Miezin,  F. M., Cohen,  A. L., Wenger,  K. K., Dosenbach,  R. A. T., et al (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, U.S.A., 104, 11073\u201311078.","DOI":"10.1073\/pnas.0704320104"},{"key":"2022042815223314000_bib21","doi-asserted-by":"crossref","unstructured":"Dove,  A., Pollmann,  S., Schubert,  T., Wiggins,  C. J., & von Cramon,  D. Y. (2000). Prefrontal cortex activation in task switching: An event-related fMRI study. Cognitive Brain Research, 9, 103\u2013109.","DOI":"10.1016\/S0926-6410(99)00029-4"},{"key":"2022042815223314000_bib22","doi-asserted-by":"crossref","unstructured":"Dubois,  J., Otto de Berker,  A., & Tsao,  D. Y. (2015). Single-unit recordings in the macaque face patch system reveal limitations of fMRI MVPA. Journal of Neuroscience, 35, 2791\u20132802.","DOI":"10.1523\/JNEUROSCI.4037-14.2015"},{"key":"2022042815223314000_bib23","doi-asserted-by":"crossref","unstructured":"Duncan,  J.\n           (2006). EPS mid-career award 2004: Brain mechanisms of attention. Quarterly Journal of Experimental Psychology, 59, 2\u201327.","DOI":"10.1080\/17470210500260674"},{"key":"2022042815223314000_bib24","doi-asserted-by":"crossref","unstructured":"Duncan,  J.\n           (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14, 172\u2013179.","DOI":"10.1016\/j.tics.2010.01.004"},{"key":"2022042815223314000_bib25","doi-asserted-by":"crossref","unstructured":"Duncan,  J.\n           (2013). The structure of cognition: Attentional episodes in mind and brain. Neuron, 80, 35\u201350.","DOI":"10.1016\/j.neuron.2013.09.015"},{"key":"2022042815223314000_bib26","doi-asserted-by":"crossref","unstructured":"Duncan,  J., & Owen,  A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23, 475\u2013483.","DOI":"10.1016\/S0166-2236(00)01633-7"},{"key":"2022042815223314000_bib27","doi-asserted-by":"crossref","unstructured":"Eger,  E., Ashburner,  J., Haynes,  J.-D., Dolan,  R. J., & Rees,  G. (2008). fMRI activity patterns in human LOC carry information about object exemplars within category. Journal of Cognitive Neuroscience, 20, 356\u2013370.","DOI":"10.1162\/jocn.2008.20019"},{"key":"2022042815223314000_bib28","doi-asserted-by":"crossref","unstructured":"Erez,  Y., & Duncan,  J. (2015). Discrimination of visual categories based on behavioral relevance in widespread regions of frontoparietal cortex. Journal of Neuroscience, 35, 12383\u201312393.","DOI":"10.1523\/JNEUROSCI.1134-15.2015"},{"key":"2022042815223314000_bib29","doi-asserted-by":"crossref","unstructured":"Erez,  Y., & Yovel,  G. (2014). Clutter modulates the representation of target objects in the human occipitotemporal cortex. Journal of Cognitive Neuroscience, 26, 490\u2013500.","DOI":"10.1162\/jocn_a_00505"},{"key":"2022042815223314000_bib30","doi-asserted-by":"crossref","unstructured":"Ester,  E. F., Sprague,  T. C., & Serences,  J. T. (2015). Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron, 87, 893\u2013905.","DOI":"10.1016\/j.neuron.2015.07.013"},{"key":"2022042815223314000_bib31","doi-asserted-by":"crossref","unstructured":"Etzel,  J. A., Cole,  M. W., Zacks,  J. M., Kay,  K. N., & Braver,  T. S. (2016). Reward motivation enhances task coding in frontoparietal cortex. Cerebral Cortex, 26, 1647\u20131659.","DOI":"10.1093\/cercor\/bhu327"},{"key":"2022042815223314000_bib32","doi-asserted-by":"crossref","unstructured":"Fedorenko,  E., Duncan,  J., & Kanwisher,  N. (2013). Broad domain generality in focal regions of frontal and parietal cortex. Proceedings of the National Academy of Sciences, U.S.A., 110, 16616\u201316621.","DOI":"10.1073\/pnas.1315235110"},{"key":"2022042815223314000_bib33","doi-asserted-by":"crossref","unstructured":"Fedorenko,  E., Hsieh,  P.-J., Nieto-Casta\u00f1\u00f3n,  A., Whitfield-Gabrieli,  S., & Kanwisher,  N. (2010). New method for fMRI investigations of language: Defining ROIs functionally in individual subjects. Journal of Neurophysiology, 104, 1177\u20131194.","DOI":"10.1152\/jn.00032.2010"},{"key":"2022042815223314000_bib34","doi-asserted-by":"crossref","unstructured":"Fedorenko,  E., Nieto-Casta\u00f1\u00f3n,  A., & Kanwisher,  N. (2012). Lexical and syntactic representations in the brain: An fMRI investigation with multi-voxel pattern analyses. Neuropsychologia, 50, 499\u2013513.","DOI":"10.1016\/j.neuropsychologia.2011.09.014"},{"key":"2022042815223314000_bib35","doi-asserted-by":"crossref","unstructured":"Feinberg,  D. A., Moeller,  S., Smith,  S. M., Auerbach,  E., Ramanna,  S., Glasser,  M. F., et al (2010). Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging. PLoS One, 5, e15710.","DOI":"10.1371\/journal.pone.0015710"},{"key":"2022042815223314000_bib36","doi-asserted-by":"crossref","unstructured":"Fox,  M. D., Snyder,  A. Z., Barch,  D. M., Gusnard,  D. A., & Raichle,  M. E. (2005). Transient BOLD responses at block transitions. Neuroimage, 28, 956\u2013966.","DOI":"10.1016\/j.neuroimage.2005.06.025"},{"key":"2022042815223314000_bib37","doi-asserted-by":"crossref","unstructured":"Fox,  M. D., Snyder,  A. Z., Vincent,  J. L., Corbetta,  M., Van Essen,  D. C., & Raichle,  M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, U.S.A., 102, 9673\u20139678.","DOI":"10.1073\/pnas.0504136102"},{"key":"2022042815223314000_bib38","doi-asserted-by":"crossref","unstructured":"Garrison,  K. A., Rogalsky,  C., Sheng,  T., Liu,  B., Damasio,  H., Winstein,  C. J., et al (2015). Functional MRI preprocessing in lesioned brains: Manual versus automated region of interest analysis. Frontiers in Neurology, 6, 196.","DOI":"10.3389\/fneur.2015.00196"},{"key":"2022042815223314000_bib39","doi-asserted-by":"crossref","unstructured":"Glasser,  M. F., Coalson,  T. S., Robinson,  E. C., Hacker,  C. D., Harwell,  J., Yacoub,  E., et al (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536, 171\u2013178.","DOI":"10.1038\/nature18933"},{"key":"2022042815223314000_bib40","doi-asserted-by":"crossref","unstructured":"Hampshire,  A., Highfield,  R. R., Parkin,  B. L., & Owen,  A. M. (2012). Fractionating human intelligence. Neuron, 76, 1225\u20131237.","DOI":"10.1016\/j.neuron.2012.06.022"},{"key":"2022042815223314000_bib41","doi-asserted-by":"crossref","unstructured":"Haxby,  J. V., Gobbini,  M. I., Furey,  M. L., Ishai,  A., Schouten,  J. L., & Pietrini,  P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425\u20132430.","DOI":"10.1126\/science.1063736"},{"key":"2022042815223314000_bib42","doi-asserted-by":"crossref","unstructured":"Haynes,  J.-D., & Rees,  G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7, 523\u2013534.","DOI":"10.1038\/nrn1931"},{"key":"2022042815223314000_bib43","doi-asserted-by":"crossref","unstructured":"Hebart,  M. N., G\u00f6rgen,  K., & Haynes,  J.-D. (2015). The Decoding Toolbox (TDT): A versatile software package for multivariate analyses of functional imaging data. Frontiers in Neuroinformatics, 8, 88.","DOI":"10.3389\/fninf.2014.00088"},{"key":"2022042815223314000_bib44","doi-asserted-by":"crossref","unstructured":"Julian,  J. B., Fedorenko,  E., Webster,  J., & Kanwisher,  N. (2012). An algorithmic method for functionally defining regions of interest in the ventral visual pathway. Neuroimage, 60, 2357\u20132364.","DOI":"10.1016\/j.neuroimage.2012.02.055"},{"key":"2022042815223314000_bib45","doi-asserted-by":"crossref","unstructured":"Kanwisher,  N., McDermott,  J., & Chun,  M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302\u20134311.","DOI":"10.1523\/JNEUROSCI.17-11-04302.1997"},{"key":"2022042815223314000_bib46","doi-asserted-by":"crossref","unstructured":"Kriegeskorte,  N., Goebel,  R., & Bandettini,  P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences, U.S.A., 103, 3863\u20133868.","DOI":"10.1073\/pnas.0600244103"},{"key":"2022042815223314000_bib47","doi-asserted-by":"crossref","unstructured":"Kriegeskorte,  N., Mur,  M., & Bandettini,  P. (2008). Representational similarity analysis\u2014connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.","DOI":"10.3389\/neuro.06.004.2008"},{"key":"2022042815223314000_bib48","doi-asserted-by":"crossref","unstructured":"Kriegeskorte,  N., Simmons,  W. K., Bellgowan,  P. S. F., & Baker,  C. I. (2009). Circular analysis in systems neuroscience: The dangers of double dipping. Nature Neuroscience, 12, 535\u2013540.","DOI":"10.1038\/nn.2303"},{"key":"2022042815223314000_bib49","doi-asserted-by":"crossref","unstructured":"Krishnan,  S., Slavin,  M. J., Tran,  T.-T. T., Doraiswamy,  P. M., & Petrella,  J. R. (2006). Accuracy of spatial normalization of the hippocampus: Implications for fMRI research in memory disorders. Neuroimage, 31, 560\u2013571.","DOI":"10.1016\/j.neuroimage.2005.12.061"},{"key":"2022042815223314000_bib50","doi-asserted-by":"crossref","unstructured":"Lafer-Sousa,  R., Conway,  B. R., & Kanwisher,  N. (2016). Color-biased regions of the ventral visual pathway lie between face- and place-selective regions in humans, as in macaques. Journal of Neuroscience, 36, 1682\u20131697.","DOI":"10.1523\/JNEUROSCI.3164-15.2016"},{"key":"2022042815223314000_bib51","doi-asserted-by":"crossref","unstructured":"Malach,  R., Reppas,  J. B., Benson,  R. R., Kwong,  K. K., Jiang,  H., Kennedy,  W. A., et al (1995). Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. Proceedings of the National Academy of Sciences, U.S.A., 92, 8135\u20138139.","DOI":"10.1073\/pnas.92.18.8135"},{"key":"2022042815223314000_bib52","doi-asserted-by":"crossref","unstructured":"Mineroff,  Z., Blank,  I. A., Mahowald,  K., & Fedorenko,  E. (2018). A robust dissociation among the language, multiple demand, and default mode networks: Evidence from inter-region correlations in effect size. Neuropsychologia, 119, 501\u2013511.","DOI":"10.1016\/j.neuropsychologia.2018.09.011"},{"key":"2022042815223314000_bib53","doi-asserted-by":"crossref","unstructured":"Muhle-Karbe,  P. S., Duncan,  J., De Baene,  W., Mitchell,  D. J., & Brass,  M. (2017). Neural coding for instruction-based task sets in human frontoparietal and visual cortex. Cerebral Cortex, 27, 1891\u20131905.","DOI":"10.1093\/cercor\/bhw032"},{"key":"2022042815223314000_bib54","doi-asserted-by":"crossref","unstructured":"Nee,  D. E., & Brown,  J. W. (2012). Rostral\u2013caudal gradients of abstraction revealed by multi-variate pattern analysis of working memory. Neuroimage, 63, 1285\u20131294.","DOI":"10.1016\/j.neuroimage.2012.08.034"},{"key":"2022042815223314000_bib55","doi-asserted-by":"crossref","unstructured":"Nelissen,  N., Stokes,  M., Nobre,  A. C., & Rushworth,  M. F. S. (2013). Frontal and parietal cortical interactions with distributed visual representations during selective attention and action selection. Journal of Neuroscience, 33, 16443\u201316458.","DOI":"10.1523\/JNEUROSCI.2625-13.2013"},{"key":"2022042815223314000_bib56","doi-asserted-by":"crossref","unstructured":"Nieto-Casta\u00f1\u00f3n,  A., & Fedorenko,  E. (2012). Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses. Neuroimage, 63, 1646\u20131669.","DOI":"10.1016\/j.neuroimage.2012.06.065"},{"key":"2022042815223314000_bib57","doi-asserted-by":"crossref","unstructured":"Nili,  H., Wingfield,  C., Walther,  A., Su,  L., Marslen-Wilson,  W., & Kriegeskorte,  N. (2014). A toolbox for representational similarity analysis. PLoS Computational Biology, 10, e1003553.","DOI":"10.1371\/journal.pcbi.1003553"},{"key":"2022042815223314000_bib58","doi-asserted-by":"crossref","unstructured":"Nomura,  E. M., Gratton,  C., Visser,  R. M., Kayser,  A., Perez,  F., & D'Esposito,  M. (2010). Double dissociation of two cognitive control networks in patients with focal brain lesions. Proceedings of the National Academy of Sciences, U.S.A., 107, 12017\u201312022.","DOI":"10.1073\/pnas.1002431107"},{"key":"2022042815223314000_bib59","doi-asserted-by":"crossref","unstructured":"Paunov,  A. M., Blank,  I. A., & Fedorenko,  E. (2019). Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension. Journal of Neurophysiology, 121, 1244\u20131265.","DOI":"10.1152\/jn.00619.2018"},{"key":"2022042815223314000_bib60","doi-asserted-by":"crossref","unstructured":"Reddy,  L., & Kanwisher,  N. (2007). Category selectivity in the ventral visual pathway confers robustness to clutter and diverted attention. Current Biology, 17, 2067\u20132072.","DOI":"10.1016\/j.cub.2007.10.043"},{"key":"2022042815223314000_bib61","doi-asserted-by":"crossref","unstructured":"Rouder,  J. N., Speckman,  P. L., Sun,  D., Morey,  R. D., & Iverson,  G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225\u2013237.","DOI":"10.3758\/PBR.16.2.225"},{"key":"2022042815223314000_bib62","doi-asserted-by":"crossref","unstructured":"Said,  C. P., Moore,  C. D., Engell,  A. D., Todorov,  A., & Haxby,  J. V. (2010). Distributed representations of dynamic facial expressions in the superior temporal sulcus. Journal of Vision, 10, 11.","DOI":"10.1167\/10.5.11"},{"key":"2022042815223314000_bib63","doi-asserted-by":"crossref","unstructured":"Saxe,  R., Brett,  M., & Kanwisher,  N. (2006). Divide and conquer: A defense of functional localizers. Neuroimage, 30, 1088\u20131096.","DOI":"10.1016\/j.neuroimage.2005.12.062"},{"key":"2022042815223314000_bib64","doi-asserted-by":"crossref","unstructured":"Schaefer,  A., Kong,  R., Gordon,  E. M., Laumann,  T. O., Zuo,  X.-N., Holmes,  A. J., et al (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28, 3095\u20133114.","DOI":"10.1093\/cercor\/bhx179"},{"key":"2022042815223314000_bib65","doi-asserted-by":"crossref","unstructured":"Seeley,  W. W., Menon,  V., Schatzberg,  A. F., Keller,  J., Glover,  G. H., Kenna,  H., et al (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27, 2349\u20132356.","DOI":"10.1523\/JNEUROSCI.5587-06.2007"},{"key":"2022042815223314000_bib66","unstructured":"Shashidhara,  S., & Erez,  Y. (2019). Reward motivation does not modulate coding of behaviorally relevant category distinctions across the frontoparietal cortex. BioRxiv, 609537."},{"key":"2022042815223314000_bib67","doi-asserted-by":"crossref","unstructured":"Shashidhara,  S., Mitchell,  D. J., Erez,  Y., & Duncan,  J. (2019). Progressive recruitment of the frontoparietal multiple-demand system with increased task complexity, time pressure, and reward. Journal of Cognitive Neuroscience, 31, 1617\u20131630.","DOI":"10.1162\/jocn_a_01440"},{"key":"2022042815223314000_bib68","doi-asserted-by":"crossref","unstructured":"Smith,  V., Mitchell,  D. J., & Duncan,  J. (2018). Role of the default mode network in cognitive transitions. Cerebral Cortex, 28, 3685\u20133696.","DOI":"10.1093\/cercor\/bhy167"},{"key":"2022042815223314000_bib69","doi-asserted-by":"crossref","unstructured":"Stiers,  P., Mennes,  M., & Sunaert,  S. (2010). Distributed task coding throughout the multiple demand network of the human frontal\u2013insular cortex. Neuroimage, 52, 252\u2013262.","DOI":"10.1016\/j.neuroimage.2010.03.078"},{"key":"2022042815223314000_bib70","doi-asserted-by":"crossref","unstructured":"van der Kouwe,  A. J. W., Benner,  T., Salat,  D. H., & Fischl,  B. (2008). Brain morphometry with multiecho MPRAGE. Neuroimage, 40, 559\u2013569.","DOI":"10.1016\/j.neuroimage.2007.12.025"},{"key":"2022042815223314000_bib71","doi-asserted-by":"crossref","unstructured":"Walther,  D. B., Caddigan,  E., Fei-Fei,  L., & Beck,  D. M. (2009). Natural scene categories revealed in distributed patterns of activity in the human brain. Journal of Neuroscience, 29, 10573\u201310581.","DOI":"10.1523\/JNEUROSCI.0559-09.2009"},{"key":"2022042815223314000_bib72","doi-asserted-by":"crossref","unstructured":"Weiner,  K. S., Barnett,  M. A., Witthoft,  N., Golarai,  G., Stigliani,  A., Kay,  K. N., et al (2018). Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation. Neuroimage, 170, 373\u2013384.","DOI":"10.1016\/j.neuroimage.2017.04.040"},{"key":"2022042815223314000_bib73","doi-asserted-by":"crossref","unstructured":"Wisniewski,  D., Goschke,  T., & Haynes,  J.-D. (2016). Similar coding of freely chosen and externally cued intentions in a fronto-parietal network. Neuroimage, 134, 450\u2013458.","DOI":"10.1016\/j.neuroimage.2016.04.044"},{"key":"2022042815223314000_bib74","doi-asserted-by":"crossref","unstructured":"Woolgar,  A., Hampshire,  A., Thompson,  R., & Duncan,  J. (2011). Adaptive coding of task-relevant information in human frontoparietal cortex. Journal of Neuroscience, 31, 14592\u201314599.","DOI":"10.1523\/JNEUROSCI.2616-11.2011"},{"key":"2022042815223314000_bib75","doi-asserted-by":"crossref","unstructured":"Woolgar,  A., Thompson,  R., Bor,  D., & Duncan,  J. (2011). Multi-voxel coding of stimuli, rules, and responses in human frontoparietal cortex. Neuroimage, 56, 744\u2013752.","DOI":"10.1016\/j.neuroimage.2010.04.035"},{"key":"2022042815223314000_bib76","doi-asserted-by":"crossref","unstructured":"Yeo,  B. T. T., Krienen,  F. M., Sepulcre,  J., Sabuncu,  M. R., Lashkari,  D., Hollinshead,  M., et al (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125\u20131165.","DOI":"10.1152\/jn.00338.2011"}],"container-title":["Journal of Cognitive Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/jocn\/article-pdf\/32\/7\/1348\/2013598\/jocn_a_01554.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/jocn\/article-pdf\/32\/7\/1348\/2013598\/jocn_a_01554.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T20:44:25Z","timestamp":1651178665000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/jocn\/article\/32\/7\/1348\/95439\/Individual-subject-Functional-Localization"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,1]]},"references-count":76,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7,1]]},"published-print":{"date-parts":[[2020,7,1]]}},"URL":"https:\/\/doi.org\/10.1162\/jocn_a_01554","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/661934","asserted-by":"object"}]},"ISSN":["0898-929X","1530-8898"],"issn-type":[{"value":"0898-929X","type":"print"},{"value":"1530-8898","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7]]},"published":{"date-parts":[[2020,7,1]]}}}