DOI: 10.5281/zenodo.15516222
title: Seeing speech: neural mechanisms of cued speech perception in prelingually deaf and hearing users

description: Group-level results for the article: Sarré, A., & Cohen, L. (2024). Seeing speech: Neural mechanisms of cued speech perception in prelingually deaf and hearing users. bioRxiv, 2024.12.06.626971. https://doi.org/10.1101/2024.12.06.626971

Contains spmT and effect size map files for the main experiment and for the localizer. Code can be found at: https://github.com/AnnahitaSarre/CUSPEX_seeing_speech.

Groups: deaf (CS users), hearing (CS users), (hearing CS naive) controls
description: Image acquisition and preprocessing

MRI data were acquired on a Siemens 3T Prisma system at the CENIR imaging center (Paris Brain Institute), using 20 (main experiment) and 64 (functional localizer) channels head coils. To record fMRI data, we used the multi-echo multi-band approach to have high SNR and coverage of the areas sensitive to signal dropout, particularly the lower part of the temporal lobes, while keeping good spatiotemporal resolution. Sequence parameters were TR/TEs/FA = 1660ms/14.2ms, 35.39ms, 56.58ms/74°, isotropic voxel size of 2.5mm, 60 slices, acceleration factors were multi-band=3 and iPat(GRAPPA)=2. In most participants, pulse oximeter and respiration belt signals were recorded and used for denoising of the data. The anatomical image was a 3DT1 with 1mm isotropic voxels, using an MPRAGE sequence.

The anatomical image was segmented and normalized to the MNI space using CAT12 (Gaser, 2020). Minimal preprocessing was then conducted with AFNI library (Cox, 1996; Cox and Hyde, 1997) using afni_proc.py wrapper to perform temporal despiking (despike), slice timing correction (tshift), and movement correction (volreg). The volume registration was computed on the first (shortest) echo, and applied to all echos, where the target for the registration is the MIN_OUTLIER volume, corresponding the volume with minimal movement. The 3 echos were optimally combined with TEDANA library (The tedana Community et al., 2021). First, a T2* map was computed using all echos, then the echos were combined with a weighted sum where the weights were a combination of TE and T2*. All subsequent steps used SPM12. Optimally combined volumes were coregistered to the anatomical scan, and normalized to MNI space using the deformation field computed for the anatomical scan. Finally, data were smoothed using a Gaussian kernel of 4 mm FWHM.

A set of noise regressors were derived from the preprocessing using TAPAS/PhysIO (Kasper et al., 2017), derived from cardiac and respiratory recordings (RETROICOR), cardiac recordings (HRV), respiratory recordings (RVT), white-matter and CSF time series, and PCA to reduce their dimensionality (Noise ROI), realignment parameters, their derivatives, and squared parameters and derivatives, and stick regressors to scrub data from volumes with FD>0.5mm.

Statistical analyses

For single-subject analyses, General Linear Models (GLMs) were created and estimated for each experiment and participant using SPM12, with a regressor for each experimental condition, plus regressors for targets and motor responses for the functional localizer, as well as movement and physiological regressors. For group-level analyses, individual contrast images were entered in t-test models for the different comparisons of interest. Unless stated otherwise, the statistical threshold was set to p<0.001 voxel-wise, and q<0.05 cluster-wise FDR-corrected for multiple comparisons across the whole brain. Effect sizes reported in the Table were computed using the Measures of Effect Size (MES) toolbox (Gerchen et al., 2021; Hentschke and Stüttgen, 2011) for SPM12. Cerebellar activations are only reported in the tables.