Imaging and analysis methods used

EEG and MEG are complementary methods that are commonly used to investigate neural oscillations with advantages and disadvantages to each approach. EEG systems are less costly than MEG systems and, unlike MEG, they are unaffected by magnetic fields in the environment and therefore don’t need to be used in a shielded environment. Many EEG systems are portable so experiments can be conducted in participants’ homes. However, EEG preparation time is much longer and involves participants wearing a tightly-fitting cap and having gel applied to the scalp. This raises potential issues for children with sensory difficulties. EEG measures signals that arise directly from the electrical potential generated from neuronal currents. These signals have contributions from tangential and radial currents whereas in MEG, only the tangential component contributes to the signal. However, unlike EEG, MEG is not affected by conductance differences and therefore source reconstruction is easier to perform, enabling cortical sources of generated signals to be identified, a factor that was important in the investigations presented in this thesis.

While there are many metrics that could be used to estimate MEG resting-state connectivity, amplitude-envelope coupling is one of the most robust and repeatable and was therefore selected for the investigations presented in Chapter 5. It would be interesting however, to compare alternative approaches and explore cross-frequency coupling. A five-minute eyes-open resting-state paradigm was used in Chapter 4. Longer resting-state recordings have been found to improve the reproducibility of MEG connectivity estimates, however these potential benefits have to be balanced with the ability of participants to remain still for longer periods of time (Liuzzi et al., 2017). Eyes-closed resting-state paradigms are associated with strong alpha power increases but can induce

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drowsiness and result in participants falling asleep during recordings (Tagliazucchi and Laufs, 2015). Keeping one’s eyes closed for five minutes in an unfamiliar environment would also be daunting for many children. For these reasons, an eyes-open paradigm was used. One of the major concerns in the interpretation of resting-state MEG/EEG data is the potential for spurious correlations or noise to affect the observed results. Several approaches were used to reduce the likelihood of spurious correlations or noise being interpreted as signal, including orthogonalisation of the source-level signals to reduce the effects of signal leakage between adjacent regions and Gaussian mixture modelling to classify connections as signal or noise. These approaches are conservative and may have led to false negatives but importantly they reduced the likelihood of false positives.

To investigate gamma band responses between probands and controls, a simple visual stimulus which has previously been shown to produce strong evoked and induced responses in the occipital cortex was used (Muthukumaraswamy et al., 2010). Despite selecting a task which was not cognitively demanding, many participants found it difficult to remain still for the ~ eight minutes required to complete the MEG recording and a large number of trials were contaminated by artefact, leading to exclusion of participants from the final analyses. Furthermore several children failed to generate peak gamma responses, further reducing the sample size for the between-group analyses. Significant reductions in the sum of gamma power and a non-significant trend towards a reduction in peak gamma amplitude and an increase in peak gamma frequency were found in the proband group. The lack of statistically significant group differences in peak gamma responses may reflect low statistical power in the limited sample who remained in the analyses after quality control. Future work in larger samples will help to clarify whether peak gamma variables are altered in 22q11.2DS.

The lack of observed GABA concentration differences between children with 22q11.2DS and controls may also be due to type II error. This dataset was relatively small (13 participants in each group) and, as GABA concentrations show

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high inter-individual variability (Muthukumaraswamy et al., 2010), this study may have lacked statistical power to detect significant differences between groups in the presence of small effect sizes. As with much of the existing literature on GABA MRS in clinical samples, the data for this thesis were collected at 3T which has lower sensitivity to detect GABA signals than higher field strengths (e.g. 7T), thus low signal to noise ratio at 3T may also have limited the ability to detect any group differences, if present. A MEGA-PRESS sequence was used to identify GABA peaks. While this approach was considered to be gold-standard at the time the study commenced, subsequent work has shown that a substantial component of the signal detected using MEGA-PRESS sequences at 3T is due to macromolecules (Mikkelsen et al., 2016). It is possible that differences in macromolecule concentrations between groups could mask differences in GABA+ concentrations. Future research using macromolecule suppression may help to elucidate this.

A potential problem in interpreting between-group differences in neuroimaging studies is the possibility that data quality varies between study groups (e.g. due to higher levels of motion or other artefacts in patients than controls). In the investigations presented in this thesis, rigorous data cleaning and quality control procedures were used, after which there were no significant between-group differences in either the number of good trials or markers of head motion in the MEG experiments, or in MRS data quality (e.g. fit error), suggesting that overall the significant findings are not due to lower data quality in the proband group.