EEG functional connectivity as a prognostic biomarker of adaptive function in autistic people

Abstract Many autistic people have challenges with adaptive function, impacting education, employment and independent-living goals. Adaptive function outcomes of autistic people vary considerably, which makes planning for future needs challenging. Here, using a developmentally sensitive approach, we investigated if cortico-cortical functional connectivity – a core neurobiological feature that differs in autism – could predict longitudinal changes in adaptive function in autistic people. Using electroencephalography in 150 autistic and 159 non-autistic participants aged 6–31 years, we investigated if mean degree and network organisation (small-world index) predict longitudinal changes in adaptive function over 19-months. We found that small-world index significantly predicted changes in adaptive function in autistic people across the entire age-range. Predictive performance was best for autistic youth (15–24-year-olds), where mean degree and small-world index explained 21 and 30% of additional variance in outcomes, respectively, outperforming measures of intelligence and autistic features. In categorising binary (improved versus not-improved) outcomes, the model containing mean degree had an AUC of 0.84 [95% CI: 0.71–0.97] in 15–24-year-olds, while that containing small-world index had an AUC of 0.76 [95% CI: 0.63–0.89] across the 6–31-year age-range. Both metrics demonstrated properties desired in prognostic biomarkers: high test-retest reliability and convergence with underlying biology (significant associations with genetic variation in brain volume-related genes). Thus, we demonstrate the first evidence that electroencephalography-derived functional connectivity metrics show promise as prognostic biomarkers of adaptive function in autistic people. Potential precision-medicine applications include stratifying participants in clinical trials and identifying those at risk of declining function in clinical settings.