Resting-state functional connectivity analyses in an independent dataset were used to establish patterns of intrinsic connectivity for the clusters we derived from the task contrasts. First, we looked to see if the identified LIFG clusters linked to task knowledge (derived from the contrast of Known > Unknown Goal), effects of word2vec, and classification of specific goal information would show connectivity with the broader semantic control network, described by Noonan et al. (2013). Secondly, we assessed patterns of intrinsic connectivity for sites sensitive to the type of semantic relationship, to establish whether these brain regions showed relatively strong connectivity with each other and to other default mode network regions. Lastly, we characterised the intrinsic connectivity of a site implicated in the application of semantic control (a site in visual cortex which showed an interaction between task knowledge and word position). This resting-state, from more than 200 healthy students at the University of York, has been used in multiple previous publications (e.g., Sormaz et al., 2018; Vatansever et al., 2017; Vatansever, Karapanagiotidis, Margulies, Jefferies, & Smallwood, 2019; Wang et al., 2018).

Resting-state functional connectivity analyses were conducted using CONN-fMRI functional connectivity toolbox, version 18a (http://www.nitrc.org/projects/conn) (Whitfield-Gabrieli & Nieto-Castanon, 2012), based on Statistical Parametric Mapping 12 (http://www.fil.ion.ucl.ac.uk/spm/). First, we performed spatial pre-processing of functional and structural images. This included slice-timing correction (bottom-up, interleaved), motion realignment, skull-stripped, co-registration to the high-resolution structural image, functional indirect segmentation, spatially normalisation to Montreal Neurological Institute (MNI) space and smoothing with a 8 mm FWHM-Gaussian filter. In addition, a temporal filter in the range 0.009–0.08 Hz was applied to constrain analyses to low-frequency fluctuations. A strict nuisance regression method included motion artifacts, scrubbing, a linear detrending term, and CompCor components attributable to the signal from white matter and cerebrospinal fluid (Behzadi, Restom, Liau, & Liu, 2007), eliminating the need for global signal normalisation (Chai, Castañón, Öngür, & Whitfield-Gabrieli, 2012; Murphy, Birn, Handwerker, Jones, & Bandettini, 2009). ROIs were taken from the task-based fMRI results (see Results section), and binarised using fslmaths. In the first-level analysis, we extracted the time series from these seeds and used these data as explanatory variables in whole-brain connectivity analyses at the single-subject level. For group-level analysis, a whole-brain analysis was conducted to identify the functional connectivity of each ROI with a cluster-forming threshold of z = 3.1 correcting for multiple comparisons (p < .05, FDR corrected). Connectivity maps were uploaded to Neurovault (Gorgolewski et al., 2015, https://neurovault.org/collections/3509/).

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