eropsycholegin 166 (2022)10813 A. =0.002.1 visio right ess.and normal or abow nommal non-ve non-MBn preg og。5 a (0 Nine of the vorks that are functionally nnected.independer had their end ye aining si erTminecdu5ingaCCfaiCAaotnorf 0 npone ent sample of sion ing acqu ach a cted and matched to the six netw singNone of the par er cluded in CONN.The venty-four. hed the larg 019)and wer oard in Linkopi d by the uded in arge 01 2.2.Image acquisition 2.4.Data analysis Structural and functional MRI data collected with a Si es in the nine inde b) the spatial mapso each component were analyzed u 10 D).Re were voxe 1atp<0.001 incorrecte preparedrapidgad t echo (MPRAGE)seg th rrors.For in ent com 090×086×086mTR2300m 236 analyses Grou ou tas-E rus (ndin (beta values)of each significant cluster were extracted mm,TR- 69,number of slices =48.440 vol. 3.Results 3.1.Independent component analysis 2.3.Data processing The nine inde et al (201 ched the six ne nn,RRID: unning under Matlab R2018 orks. the visual netw repr irst sc represented by d a d pn of f e disp nt (o etal)by a nd arightcom and )The atten ization into standard MNI s ral seg inte sular). and the default network (me dial fro ( atio.The tound in th nt-based on (a the ventral soma the left co physi Oualit wed that the e no o dif e found in the sup n had s or gan nd in the pole,right brain volu 0.05;HN SD 0.04:37) 4,p 0.023 why es thus include (deaf ear anng nt rer network had peak voxels in right sup Neuropsychologia 166 (2022) 108139 3 = 3.4, p = 0.002. Inclusion criteria were normal or corrected-to-normal vision, right-handedness, and normal or above normal non-verbal cognitive ability. Exclusion criteria included claustrophobia, preg￾nancy, and having non-MR compatible implants. Nine of the partici￾pants had their deafness discovered at birth, while the remaining six were between 6 month and 3 years when their deafness was discovered. All deaf signers were considered early signers, using Swedish Sign Language (Svenskt Teckenspråk; STS) as their primary language, per￾forming on par with an independent sample of deaf native signers on the STS sentence repetition test (Schonstr ¨ om ¨ and Hauser, 2021). Five par￾ticipants were signed with from birth and nine reported starting acqui￾sition of STS before the age of three. For one participant, age of acquisition was missing. None of the participants relied on hearing aids for verbal communication, although two participants used hearing aids for sound awareness. The hearing participants were native Swedish speakers without any knowledge of STS. The study was approved by the regional ethical review board in Linkoping ¨ (Dnr, 2016/344–31) and was conducted in accordance with the Declaration of Helsinki. Participants gave their written informed consent and were compensated for their participation. 2.2. Image acquisition Structural and functional MRI data were collected with a Siemens Magnetom Prisma 3T scanner (Siemens Healthcare, GmbH) at the Center for Medical Image Science and Visualization (Linko¨ping University, Swe￾den) using a 64-channel head coil. The scanning started with acquisition of structural images using a T1-weighted three-dimensional magnetization￾prepared rapid gradient echo (MPRAGE) sequence with the following parameters: FOV = 288 × 288, acquisition matrix = 208 × 288 × 288, voxel size = 0.90 × 0.86 × 0.86 mm, TR = 2300 ms, TE = 2.36 ms, TI = 900 ms, FA = 8◦. Resting-state data was acquired at the end of the scan￾ning after the participants had performed four task-EPI runs (Andin et al., 2021), using a BOLD multi-plex EPI sequence during a 10-min scan with the following parameters: FOV = 192 × 192 mm, voxel size = 3 × 3 × 3 mm, TR = 1340 ms, TE = 30 ms, FA = 69◦, number of slices = 48, 440 vol, interleaved/simultaneous acquisition. 2.3. Data processing Preprocessing was performed using the default pipeline in CONN functional connectivity toolbox (Version 20.b; www.nitric.org/proj ects/conn, RRID: SCR_009550) running under Matlab R2018a (The MathWorks Inc., Natick, MA). The preprocessing steps included func￾tional realignment, unwarping and co-registration to the first scan, slice￾timing correction to adjust for temporal misalignment between slices, outlier detection by computation of framewise displacement (outliers defined as displacement >0.9 mm or BOLD signal change >5 SD.), normalization into standard MNI space, structural segmentation into grey matter, white matter and CSF tissue classes, and smoothing using a Gaussian kernel of 8 mm full width half maximum to increase signal-to￾noise ratio. The realignment parameters and the noise components from the outlier detection were used as first-level covariates. Linear regres￾sion using the anatomical component-based noise correction (aComp￾Cor) algorithm was implemented to remove effect from subject specific physiological noise such as white matter and cerebrospinal areas, mo￾tion parameters, outlier scans (scrubbing) and session-related slow trends. Quality assurance checks showed that there were no group dif￾ferences in number of scrubbed slices, max motion, or global signal change. However, the deaf group had significantly higher mean motion, i.e., the absolute displacement of each brain volume compared to the previous estimated from the x, y and z translation parameters (DS: M = 0.15, SD = 0.05; HN: M = 0.12, SD = 0.04; t(37) = 2.4, p = 0.023), why this parameter was included as a covariate in all group analyses. Second￾level covariates thus included group (deaf early signers/hearing non￾signers), age (mean-centered), and the mean motion parameter from the realignment step. Denoising included linear regression of potential confounding effects and temporal processing using bandpass filtering (0.008, 0.09 Hz). To identify networks that are functionally connected, independent component analysis (ICA) was performed by estimating spatially inde￾pendent patterns in the fMRI data. Independent components across both groups were determined using a G1 FastICA algorithm for component definition at the group-level and GICA 3 subject-level back projection. Dimensionality reduction was set to 64. ICA was performed with the number of components set to eight, sixteen, twenty-four, and thirty-two. Each analysis was visually inspected and matched to the six networks described by Uddin et al. (2019), and automatically to the network templates included in CONN. The twenty-four-component setting rendered the best overall solution. Nine of the twenty-four components matched the large scale brain networks by Uddin et al. (2019) and were included in further analyses (Fig. 1). Generally, a lower number of components (around 20) are used when the aim is to identify functional large-scale networks, as in the present study, while larger number of components (above 100) are used for brain parcellation (Ray et al., 2013). 2.4. Data analysis To identify group-differences in the nine independent components, the spatial maps of each component were analyzed using between￾subjects contrasts with age and mean motion as covariate (1, − 1, 0, 0). Results were voxel thresholded at p < 0.001 uncorrected, and cluster thresholded at p < 0.05 using False Discovery Rate to control for type 1 errors. For independent components with significant differences be￾tween groups, the significant clusters were exported and used as seeds in seed-to-voxel analyses. Group differences in functional connectivity were investigated using the same contrasts and the same thresholding as for the ICA. Further, functional connectivity measures and effect size (beta values) of each significant cluster were extracted. 3. Results 3.1. Independent component analysis The nine independent components that best matched the six net￾works proposed by Uddin et al. (2019) are presented in Fig. 1. For three networks, two separate components were chosen since they represented typical sub-networks. Thus, the visual network (occipital) was repre￾sented by a medial and a lateral component (Fig. 1a and b), the soma￾tomotor network (pericentral) was represented by a ventral and a dorsal component (Fig. 1c and d), and the control network (lateral frontopar￾ietal) by a left and a right component (Fig. 1e and f). The attention network (dorsal frontoparietal), salience network (the midcingulo-insular), and the default network (medial frontoparietal) were best captured by one single component each (Fig. 1g–i). Group differences, with stronger connectivity for deaf signers compared to hearing non-signers, were found in the default network component. Stronger connectivity for hearing non-signers compared to deaf signers were found in the ventral somatomotor network, the left control network, and the attention network (Fig. 2). The four independent components in which group differences were identified match different large-scale networks. However, all significant clusters in the between-group analysis except one were found in the superior and middle temporal regions. For the default network component, peak voxels were found in the left temporal pole, right superior and left middle temporal gyri. For the component representing the left control network, peak voxels were found in bilateral superior temporal gyrus and right pallidum. The ventral somatomotor network component showed peak voxels in left superior temporal gyrus, while the compo￾nent representing the attention network had peak voxels in right supe￾rior temporal gyrus (Table 2). J. Andin and E. Holmer