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

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. We are grateful to the AIMS-2-TRIALS LEAP-group for data collection and quality control procedures. We are grateful to Dr Piotr J.

Franaszczuk for his expert advice on signal analysis and filtering. We are grateful to the reviewers for their help in improving our study. This work was supported by EU-AIMS (European Autism Interventions), which received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115300, the resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (grant FP7/2007-2013), from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and from Autism Speaks.

The results leading to this publication have also received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, and SFARI. This study was also delivered through the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC).