A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

Abstract

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings for individuals in healthcare settings. Nevertheless, it remains unclear if it is possible to build robust dimensional predictive models of attention in neurodiverse populations. Here, we use five datasets to identify and validate functional connectome-based markers of attention. In dataset one, we use connectome-based predictive modelling and observe successful prediction of performance on an in-scan sustained attention task in a neurodiverse sample of youth. The predictions are not driven by confounds, such as head motion. In dataset two, we find the attention network model defined in dataset one generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets three to five, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attention in neurodiverse youth and support the further development of predictive dimensional models of other clinically-relevant phenotypes.

Differences in fMRI-based functional connectivity during abstinence or interventions between heroin-dependent individuals and healthy controls

“…There is some literature showing FC differences between HC males and females (Weis et al, 2020), and there are some sex-based differences in the functional correlates of addiction (Nicolas et al, 2022;Smith et al, 2023). Similar effects of neurodiversity and socioeconomic status on FC have been reported (Horien et al, 2023;Sripada et al, 2021;Yaple & Yu, 2020). Therefore, future work should endeavour to include representation of male and female participants, as excluding the consideration of clinically relevant factors from research negatively impacts the clinical relevance of findings (Johnson et al, 2014).…”

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

“…Edges from connectivity matrices for each subject per scan were correlated to the three aforementioned memory performance measures, totaling to seven connectivity matrices and three memory scores per subject (21 total correlated matrices). Motion and age covariates were also included in the CPM analyses to account for in-scanner head motion, age, and their interaction in our predictions, as previously done (Scheinost et al ., 2021; Dufford et al ., 2022; Horien et al ., 2022).…”

“…To visualize sex differences at the network level, we first split the aforementioned consensus matrix into two binarized matrices (a “positive” matrix containing edge with significant positive correlations to memory and the other “negative” matrix of edges with significant negative correlations to memory) for each predictive model. Categorization of nodes by functional network was determined using the 10-network parcellation of the Shen 268-node atlas (Horien et al ., 2022). In this network grouping, the medial frontal (MF) network also includes some temporal and frontal nodes which often cluster with the DMN.…”

“…Inter-network edges were defined as the number of significant edges between each pair of networks normalized by the total number of edges between the same network pair. As done in previous work, we defined edges as “significant” if they appear in at least 2 out of 5 folds in 40% of 1000 iterations of CPM to minimize noise while retaining meaningful connections (Rosenberg et al ., 2016; Yip et al ., 2019; Horien et al ., 2022). In addition to using heatmaps to visualize the inter-network edges of both female and male groups separately, we subtracted male-group positive edges from female-group positive edges (and the same with the negative edges) across corresponding matrix cells to evaluate the inter-network sex differences.…”