Introduction: The major networks implicated in language processing can also be discerned using resting-state MRI and several studies have used data-driven approaches to study whole-brain resting state functional connectivity (rsFC) in deaf signers. In contrast, hypothesis-driven analyses of rsFC based on findings from task-based studies of sign language processing are currently still missing. Here, we investigated rsFC of the so-called “core” language network, consisting of left posterior inferior frontal gyrus (pIFG) and posterior middle temporal gyrus and superior temporal sulcus (pMTG/STS), to five sets of regions relevant for (sign) language processing in bilaterial frontal, parietal, temporal, and occipital cortex. Methods: This analysis includes resting state magnetic resonance imaging and structural data from 24 deaf signers (11 male; mean age = 32.25, SD = 8.69) who acquired a sign language already early in life (mean age of acquisition = 1.29, SD = 2.22) and primarily from their deaf parents (N = 19), as well as 24 hearing non-signers (11 male; mean age = 32.75, SD = 8.25). All data was pre-processed using the standardised workflow implemented in fMRIPrep (Esteban et al., 2019) which integrates advanced quality control measures and enhances reproducibility. Further processing steps were carried out using the “nilearn” package (version 0.11.1; Nilearn Contributors, 2024): We first applied a bandpass filter, denoised the data using the 24-paramters approach by Satterthwaite et al. (2013), and applied a smoothing kernel of 4mm FWHM. We then computed rsFC between left pIFG and pMTG/STS as a sanity check. Next, whole-brain seed to voxel correlation maps were computed using either left pIFG and left pMTG/STS as seed region. From the resulting whole-brian maps, we then extracted five sets of pre-registered (https://doi.org/10.17605/OSF.IO/8DS93) ROIs from the Brainnetome atlas (Fan et al., 2016): Bilateral (1) parietal cortex, (2) rolandic cortex, (3) premotor cortex including supplementary motor area, (4), fusiform gyrus and posterior inferior temporal gyrus, and (5) occipital cortex. Lastly, we fit Bayesian hierarchical regression models for each seed region and set of target ROIs using Stan via the “brms” package (version 2.22.0; Bürkner et al., 2021) in R (version 4.4.3; R Core Team). Preliminary Results: Both seed regions of the core language network show high rsFC to each other as is predicted by the direct anatomical connectivity via the arcuate fasciculus. At the time of submission, computing of Bayesian models was still ongoing. Preliminary results obtained during determining the appropriate model structure indicate several differences in rsFC to the sets of target regions between both groups. Outlook: Our analyses will shed light on whether the modality-independent core language network in the brain’s left hemisphere exhibits differential resting-state connectivity in the groups of deaf signers and hearing non-signers in regions that have previously been associated with different aspects of sign and spoken language processing in task-based studies.