Abstract
Autism is a neurodevelopmental condition characterised by social-communication behaviours and cognitive styles that are different to that of non-autistic people. Autistic features pose both strengths and challenges. Many autistic people have difficulties with education, employment and independent living attainments. Key research priorities of autistic people include improving these outcomes. Better outcomes in these domains are predicted by greater adaptive function abilities (the ability to navigate one’s day-to-day environment using the skills they possess) in autistic people, including better application of a person’s language, communication, social and self-help skills. Currently, there are few effective methods to predict whether adaptive function abilities will improve, stay the same, or worsen over time in autistic people, making planning for future independence or support needs challenging. Altered connectivity between brain regions is a key neurobiological feature of autism, associated with autistic behaviours and cognition. In this thesis, I investigated if quantifying cortical functional connectivity can predict longitudinal changes in adaptive function in autistic people.I used scalp electroencephalography (EEG) to measure cortical functional connectivity, as EEG signals capture the oscillatory activity of neurons, mechanistically implicated in perception and behaviour. However, an unresolved debate in the EEG functional connectivity literature has limited the interpretability of its findings. A disadvantage of EEG-derived functional connectivity is the presence of artefactual connections between brain regions due to signal leakage (the spatial blurring that occurs when attempting to localise the brain regions from which EEG signals arise) and volume conduction (the spread of electrical fields through non-neural tissue). The oscillatory activity that is misattributed to different brain regions because of signal leakage and volume conduction occurs with apparent perfect synchronicity, or in other words, with zero-phase delay. To limit these artefactual connections, scalp EEG functional connectivity analyses frequently exclude zero-phase delay interactions. However, a strong body of evidence highlights the presence of ‘true’ connectivity that also occurs with zero- and near-zero phase delay. Therefore, excluding zero-phase delay connectivity discards both artefactual connectivity and ‘true’ connectivity, in proportions that are currently unknown. Including versus excluding zero-phase delay connections may also lead to contrasting functional connectivity findings from otherwise identical signals, making it difficult to draw conclusions about brain functional connectivity in autism.
Therefore, in Study 1, I first investigated the effects of including versus excluding zero-and near-zero phase delay connections on functional connectivity metrics in neurotypical people. Using a novel approach, I showed that zero- and near-zero phase delay connections comprise the most frequent and strong functional connections between cortical regions, including where such connectivity is unlikely to be artefactual. Including zero-phase delay connections increased the test-retest reliability, concurrent validity with structural connections, ability to predict participant age, and predictive validity for longitudinal changes in working memory, compared to excluding zero-phase delay connections. Further, I found that some methods of excluding zero-phase connectivity especially penalised functional connections between the strongest structurally connected regions. Thus, this study provided five converging lines of evidence to challenge the generally accepted assumption that zero-phase exclusive methods are superior to zero-phase inclusive methods. Based on these basic neuroscience findings, I used a zero-phase delay inclusive method to quantify functional connectivity in autistic people.
In Study 2, to investigate if EEG-derived functional connectivity could predict longitudinal changes in adaptive function in autistic people, I took a developmentally sensitive approach. I found that both the number of functional connections (mean degree) and the extent to which functional connections are optimally organised in a network (small-world index) changed more slowly from childhood to adulthood in autistic compared to non-autistic people. In autistic people, compared to non-autistic people, mean degree was lower in 15-21 and 22-31-year-olds, but no different in 6-14-year-olds. Small-world index significantly predicted longitudinal changes in adaptive function in autistic people across the entire age-range. Predictive performance was best in 15-21-year-olds, where small-world index and mean degree explained 30% and 33% of the variance in adaptive function outcomes, respectively. In this age-group, functional connectivity measures outperformed measures of intelligence and autistic features in predictive performance. This study is the first, to my knowledge, to show that EEG functional connectivity can predict longitudinal changes in adaptive function in autistic people.
In Study 3, I investigated if the functional connectivity findings in autistic people from Study 2 converged with synaptic density changes, in a separate sample. Altered synaptic number, structure and function are thought to underpin many autistic features, as suggested by genetic and postmortem research. However, postmortem methods quantifying synaptic density have several confounds. Until now, to my knowledge, in vivo synaptic density in autistic people had not been quantified. I used the 11C-UCB-J positron emission tomography tracer, which binds to presynaptic boutons, to investigate synaptic density differences between autistic and non-autistic adults. I found that estimated synaptic density was significantly lower in autistic compared to non-autistic adults in the prefrontal cortex and nucleus accumbens (regions implicated in autistic behaviours and cognitive styles), with large effect sizes. Estimated synaptic density negatively correlated with the extent of autistic traits. Therefore, reduced synaptic density may be implicated in autistic behaviours and could contribute to the reduced number of functional connections (mean degree) between cortical regions seen in autistic adults.
The finding that EEG-derived functional connectivity metrics can predict longitudinal changes in adaptive function in autistic people was made more robust through 1) the use of a zero-phase-inclusive functional connectivity method shown to be reliable and have concordance with the underlying structural connectivity, and 2) convergence with synaptic density changes in autistic adults. If these findings are replicated, EEG-derived mean degree and small-world organisation, used in a developmentally sensitive manner, could be developed into prognostic biomarkers for adaptive function in autistic people. This could help autistic people to plan for either more independence or more support.
| Date of Award | 1 Apr 2025 |
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| Original language | English |
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| Supervisor | Jonathan O'Muircheartaigh (Supervisor), Mark Richardson (Supervisor) & Michael Absoud (Supervisor) |