Functional connectivity of cognition-related brain networks in adults with fetal alcohol syndrome

Background Fetal alcohol syndrome (FAS) can result in cognitive dysfunction. Cognitive functions affected are subserved by few functional brain networks. Functional connectivity (FC) in these networks can be assessed with resting-state functional MRI (rs-fMRI). Alterations of FC have been reported in children and adolescents prenatally exposed to alcohol. Previous reports varied substantially regarding the exact nature of findings. The purpose of this study was to assess FC of cognition-related networks in young adults with FAS. Methods Cross-sectional rs-fMRI study in participants with FAS (n = 39, age: 20.9 ± 3.4 years) and healthy participants without prenatal alcohol exposure (n = 44, age: 22.2 ± 3.4 years). FC was calculated as correlation between cortical regions in ten cognition-related sub-networks. Subsequent modelling of overall FC was based on linear models comparing FC between FAS and controls. Results were subjected to a hierarchical statistical testing approach, first determining whether there is any alteration of FC in FAS in the full cognitive connectome, subsequently resolving these findings to the level of either FC within each network or between networks based on the Higher Criticism (HC) approach for detecting rare and weak effects in high-dimensional data. Finally, group differences in single connections were assessed using conventional multiple-comparison correction. In an additional exploratory analysis, dynamic FC states were assessed. Results Comparing FAS participants with controls, we observed altered FC of cognition-related brain regions globally, within 7 out of 10 networks, and between networks employing the HC statistic. This was most obvious in attention-related network components. Findings also spanned across subcomponents of the fronto-parietal control and default mode networks. None of the single FC alterations within these networks yielded statistical significance in the conventional high-resolution analysis. The exploratory time-resolved FC analysis did not show significant group differences of dynamic FC states. Conclusions FC in cognition-related networks was altered in adults with FAS. Effects were widely distributed across networks, potentially reflecting the diversity of cognitive deficits in FAS. However, no altered single connections could be determined in the most detailed analysis level. Findings were pronounced in networks in line with attentional deficits previously reported. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-023-03208-8.


Rationale of the Majewski criteria for FAS
The Majewski diagnostic criteria for FAS [37] were widely used in German speaking countries at the time of diagnosis [38] of the parrticipantts with FAS in this study.In summary, they are based on the following clinical features [37]: • intrauterine and/or postnatal growth retardation

MRI data preprocessing
The following fMRIPrep "boilerplate" (indented text) describes the preprocessing steps in detail.The text has intentionally been left completely unchanged according to the fMRIPrep recommendations for optimal reproducibility.Please note that fMRIPrep generated multiple preprocessing outputs which could be used in different denoising and analysis strategies.Not all of these parallel outputs have been used for further processing in this study.Details about which outputs were used for actual denoising and further functional connectivity modelling are presented in the main text.

Functional data preprocessing
For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed.First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep.Susceptibility distortion correction (SDC) was omitted.The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration (Greve and Fischl 2009).Coregistration was configured with six degrees of freedom.Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt (FSL 5.0.9,Jenkinson et al. 2002).The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion.These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD.The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space.First, a reference volume and its skullstripped version were generated using a custom methodology of fMRIPrep.Several confounding time-series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals.FD and DVARS are calculated for each functional run, both using their implementations in Nipype (following the definitions by Power et al. 2014).The three global signals are extracted within the CSF, the WM, and the whole-brain masks.Additionally, a set of physiological regressors were extracted to allow for component-based noise correction (CompCor, Behzadi et al. 2007).Principal components are estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor).tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions.This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions.For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation).Components are also calculated separately within the WM and CSF masks.For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components' time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal).The remaining components are dropped from consideration.The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file.The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each (Satterthwaite et al. 2013).Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers.All resamplings can be performed with a single interpolation step by composing all the pertinent transformations (i.e.head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces).Gridded (volumetric) resamplings were performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels (Lanczos 1964).Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer).
Many internal operations of fMRIPrep use Nilearn 0.6.2(Abraham et al. 2014, RRID:SCR_001362), mostly within the functional processing workflow.For more details of the pipeline, see the section corresponding to workflows in fMRIPrep's documentation.

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