Once established, the cognitive impairments characteristic of Alzheimer’s disease (AD) are irreversible (Herrup, 2015). To effectively identify measures to predict and prevent AD, it is vital to identify biomarkers at an early age, before the presence of any symptoms, and one increasingly popular route for this is neuroimaging of genetic risk groups. The greatest genetic risk factor for developing late-onset AD is the apolipoprotein E4 (APOE-ɛ4) allele (Corder et al., 1993; van der Flier et al., 2011).

APOE consists of 299 amino acids divided into receptor-binding and lipid-binding regions and has three isoforms (ɛ2, ɛ3 and ɛ4) that only differ at two of the amino acids (112 and 158), both of which are on the receptor-binding part of the protein. APOE has a crucial role in transporting cholesterol and other lipids, both in plasma and in the brain, but it is its role in regulating amyloid-β that has received much attention, due to the latter’s apparently crucial role in AD (Reinvang et al., 2013; Zhao et al., 2018). The pathological accumulation of amyloid-β could occur via multiple mechanisms of over-production and/or impaired clearance (Zhao et al., 2018) and it appears that the presence of the APOE-ɛ4 isoform may differentially lead to one or more of these occurring. For example, compared to APOE-ɛ3, APOE-ɛ4 stimulates increased recycling of the amyloid precursor protein (APP), hence leading to an increase in amyloid-β production (Ye et al., 2005). This mostly appears to be via modified interactions between APOE-ɛ4 and the LRP1 receptor. Similarly, it has been shown that APOE can bind to amyloid-β to form a complex that then promotes clearance via cell internalization of this complex (Zhao et al., 2018). As the APOE-ɛ4-amyloid-β complex is less stable than the other isoforms, this may make it less effective in amyloid clearance. Finally, APOE-ɛ4 may interfere with protease-mediated clearance of amyloid-β via downregulation of, for example, insulin-degrading-enzyme (IDE) (Du et al., 2009).

Whatever the precise mechanism, APOE-ɛ4 carriers have a much increased risk of developing sporadic AD (Corder et al., 1993), of progression to AD if diagnosed with mild cognitive impairment (MCI) (Petersen, 1995), and to develop AD at an earlier age (Corder et al., 1993; van der Flier et al., 2011) compared to non-carriers. Studying early effects of the APOE-ɛ4 allele on the brain may therefore reveal insights into the neural pathways leading to AD.

Within groups of AD patients and elderly controls, APOE-ɛ4 carriers show alterations in brain structure and function that follow similar patterns to those that characterise AD, but are stronger than in non-carriers. Imaging studies show that APOE-ɛ4 carriers present with more severely atrophied temporal areas (Filippini et al., 2009a; Filippini et al., 2009b; Juottonen et al., 1998), and have elevated levels in temporo-parietal areas of the two main harmful brain deposits seen in AD: the tau protein (Ossenkoppele et al., 2016), causing neurofibrillary tangles, and amyloid-β (Drzezga et al., 2009), the peptide accumulating in the pathognomonic intercellular plaques.

Functional neuroimaging during a ‘resting state’ can show intrinsic fluctuations in levels of background brain activity. Fast fluctuations of rhythmic neural activity occur spontaneously and are thought to underlie communication between brain areas (Schnitzler and Gross, 2005). These neural oscillations, particularly in frequency bands such as the alpha band, have been linked to patterns of white matter connectivity (Teipel et al., 2009). APOE plays a role in lipid transport, important for myelination of white matter (Herrup, 2015). Studying neural oscillations in conjunction with variation in this gene may therefore reveal important changes in neural connectivity. Neural oscillations can be non-invasively measured in vivo in humans using electro- or magnetoencephalography (E/MEG), which measure fluctuations in electric potentials or magnetic fields and hence directly reflect neural activity at the scalp with high temporal resolution (Baillet, 2017).

To date, M/EEG studies in elderly APOE-ɛ4 carriers have shown changes in the oscillatory alpha band (Babiloni et al., 2006; Canuet et al., 2012; de Waal et al., 2013; Jelic et al., 1997; Kramer et al., 2008; Ponomareva et al., 2008) and in the much lower-frequency delta band (Cuesta et al., 2015; de Waal et al., 2013) in regions similar to those reduced in AD and MCI. These findings suggest that the presence of an APOE-ɛ4 allele is associated with differences in oscillatory brain function at a stage preceding AD symptomology, but it is not clear how early in the life span this is established. One very small-sample study has measured MEG oscillations in healthy young genotyped individuals, and found that low-frequency (theta) oscillations were increased in the medial frontal cortex of APOE-ɛ4 carriers, but did not investigate the alpha band, nor connectivity between brain regions (Filbey et al., 2006).

Here, we used MEG to study resting-state neural activity non-invasively in a large sample of healthy young individuals who were genotyped for APOE alleles (51 ɛ4 carriers and 108 non-carriers). We assessed whether and how background oscillatory neural activity and connectivity across the whole brain in a range of frequencies is affected in young APOE-ɛ4 carriers, decades before the effects of ageing or potential onset of AD symptomology. To relate the significance of these findings to brain dysfunction in AD, we further compared the young APOE-ɛ4-related findings with resting-state MEG data from patients with established AD and healthy age-matched controls. Finally, using machine learning approaches, we assessed the diagnostic information content of these MEG-derived static connectivity matrices by quantifying their accuracy in solving three problems: 1) Whether young APOE-ε4 carriers could be distinguished from young non-carriers, 2) Whether AD patients could be distinguished from elderly controls and 3) Whether a classifier trained on identifying young E4 carriers could identify AD patients, purely from their MEG connectivity matrices.

In a large sample of young healthy APOE-ɛ4 carriers, and using a conservative approach in which we restrict our analysis to highly reproducible (top 20%) connections, we found consistent increases in resting-state oscillatory activity and connectivity in a right-dominant network of parieto-occipital, parieto-temporal and parieto-frontal connections. The largest and most consistent cluster of effects arose in the beta-band, representing rhythmic synchronized activity thought to reflect global input at the synapse (Schnitzler and Gross, 2005) and in our analysis of Combined connectivity maps. These findings occurred in absence of cognitive or structural brain differences. We also showed that those connections showing the largest average increases in connectivity tended to overlap with those connections that showed decreased parieto-temporal connections in AD patients, and was most profound in connections to/from right-hemisphere parietal areas. Our results therefore suggest that the right parietal cortex is a particularly important area in the risk for, and development of, AD. Although our study can only show an implied association between early hyperconnectivity and later hypoconnectivity, we could speculate that an initial hyperconnectivity/hyperactivity centred around (right) parietal cortex has a cascade of effects that may lead to eventual disconnection in AD. Although initially a right-dominant effect, this may spread over time to more widespread effects across the cortex and hemispheres. A larger study, using the methods we present here, over a wider age-range, may shed further light on this.

Interestingly, when analysing activity differences in oscillatory envelopes, rather than connectivity, the only significant differences found were APOE-ɛ4 related hyperactivity in the gamma bands, both 40–60 Hz and 60–140 Hz. These gamma activity increases overlapped with those right-hemisphere brain regions showing hyperconnectivity in the lower bands (alpha/beta). This result is consistent with theories suggesting that gamma band oscillations mostly reflecting local, rather than global processing, whilst alpha/beta underpin longer-range interactions (Donner and Siegel, 2011). Our results are also consistent with a recent computational model in which it was shown that a realistically-coupled set of local gamma oscillators, at multiple simulated brain regions, led to long-range, slowly varying, coupled amplitude-envelopes, but in the alpha/beta range (Cabral et al., 2014). Finally, it has been suggested that higher-gamma band activity, such as our 60–140 Hz band, may be correlated with neuronal spiking rates (Ray and Maunsell, 2011). If true then the higher gamma-band activity we see in APOE-ɛ4 carriers may be a reflection of the neuronal hyperactivity, associated with increased amyloid-β, seen in rodent AD models (Stargardt et al., 2015).

The highly specific spatial patterns of increased resting-state oscillatory connectivity in young APOE-ɛ4 carriers, identifying the right parietal cortex as the most affected area, including inter-hemispheric connections and connections to the precuneus, complement and extend current neurophysiology findings. MEG studies using Graph Theory network measures have also identified a loss of connectivity in AD, with the parietal cortex as the most affected hub (de Haan et al., 2012b; Tijms et al., 2013). Effects were found specifically in the beta band – thought to play an important role in long-range functional connectivity (Schnitzler and Gross, 2005). Furthermore, previous EEG studies on APOE-ɛ4 have identified differences in power or connectivity in the alpha band, but these have only been investigated in older participants (Babiloni et al., 2006; Canuet et al., 2012; de Waal et al., 2013; Jelic et al., 1997; Kramer et al., 2008; Ponomareva et al., 2008). Using the higher spatial resolution of MEG, Cuesta et al. (Cuesta et al., 2015) studied source-localised connectivity in elderly participants with and without MCI, and found that APOE-ɛ4 carriers showed differences only in a much lower frequency band than alpha (delta, 1–4 Hz), focused around a frontal brain hub. Cuesta et al. measured phase-based connectivity, using the phase-locking value, a very different measure than the amplitude correlations we used, and more prone to source leakage artefacts (Colclough et al., 2016). Interestingly, in our previous study we found clear changes in oscillatory amplitude, but not phase correlations, in the brains of AD patients (Koelewijn et al., 2017), though we did not investigate MCI patients. For these reasons, and to reduce multiple comparisons, we did not investigate phase-based connectivity here.

To our knowledge, only one small MEG study (N = 8) has previously investigated young APOE-ɛ4 carriers, and found increases in mainly medial-frontal theta-band (3.5–7 Hz) oscillatory power during a working memory task (Filbey et al., 2006). This study did not investigate connectivity, nor analyse the alpha or beta band. Of note is that many previous neuroimaging studies used very low numbers of participants, with the resulting APOE-ɛ4 carrier groups being typically below N = 20, with some notable exceptions in elderly participants (Babiloni et al., 2006; de Waal et al., 2013; Sheline et al., 2010). Our more highly-powered MEG study in young participants therefore provides valuable, reliable, and unique insights into the early effects of APOE-ɛ4 on oscillatory brain function.

Functional MRI (fMRI) resting-state studies typically show that the blood-oxygen-level-dependent (BOLD) response within the default-mode network (DMN) is reduced in healthy ageing (Damoiseaux et al., 2008). This reduction is accelerated in AD (Buckner et al., 2008) and is also modified in elderly APOE-ɛ4 carriers compared to non-carriers (Sheline et al., 2010). Filippini et al. (Filippini et al., 2009a) previously investigated DMN BOLD activity in a small sample of young APOE-ɛ4 carriers versus non-carriers (N = 18 each) using an independent component analysis (ICA) technique. They found increased co-activation of areas within the DMN for ɛ4 carriers, which were focused on hippocampal and frontal, but posterior regions were not significantly different. Furthermore, they also found an increase in the sensorimotor network. Despite the different spatial focus of effects, these fMRI findings are similar to ours in that they reveal increased brain connectivity in young APOE-ɛ4 carriers. In a more recent resting state-fMRI study of older, but healthy, APOE-ɛ4 carriers it was found that, as well as replicating Fillipini et al.’s finding of increased hippocampal DMN connectivity, significant increases in network activity were also found for APOE-ɛ4 carriers in posterior regions of the DMN, including the precuneus, and lateral parietal regions as well as frontal cortex (Westlye et al., 2011). Importantly, these frontal and posterior-lateral regions were only significantly increased in the right hemisphere (their Figure 1D and Table 3), showing good concordance with our findings of oscillatory hyperconnectivity in similar right-hemisphere regions. Finally, we note that some recent task-based fMRI studies have also shown APOE-ɛ4 carrier hyperactivity in posterior parts of the DMN (Hodgetts et al., 2019; Shine et al., 2015). Interestingly, the authors of these studies speculate that hyperactivity/hyperconnectivity of the DMN in these young APOE-ɛ4 carriers may reflect reduced network efficiency/flexibility.

Note that in our cohort, and other previous studies, there is little or no evidence of cortical volume changes, including hypothesised hippocampal volume decreases (Filippini et al., 2009a; Heise et al., 2011; O'Dwyer et al., 2012; Wishart et al., 2006), suggesting that changes in both haemodynamic and oscillatory brain function can be detected before changes in brain structure in the pathway of increased risk for AD (Figure 5, lower panel, (Hampel et al., 2011)).

Figure 5

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The results presented here are, of course, only correlational in nature but we can speculate, in the presence of prior in-vitro and in-vivo animal studies, what underlying neurophysiological changes underpin our hyperactivity and hyperconnectivity findings. It is known that increased amyloid-β is somehow related to increased neural activity, although the sequence of cause and effect is not known (Stargardt et al., 2015). For example, it was shown in a rodent disease model that during Braak pre-symptomatic stages 0, I and II, amyloid-β starts to accumulate within neurons, whilst at the same time synaptic activity rises within the neurons (i.e. hyperactivity). As the disease progresses to Braak stage VI, and amyloid-β becomes significant enough to cause plaques, synaptic activity then falls (i.e. hypoactivity). Interestingly, it appeared that the rise in amyloid-β appeared to be linked to a reduction of activity in the Insulin-Degrading Enzyme (IDE).

However, what it is not clear is how APOE-ɛ4 carrier status might lead to an increased risk of the hyperactivity <>amyloid-β relationship described above in young or even middle-aged people. Experimental studies have demonstrated that carrying the APOE-ɛ4 allele leads to a wide range of effects in the brain, including impaired myelination, mitochondrial dysfunction, reduced cholinergic function and vascular pathology (Reinvang et al., 2013). Healthy elderly APOE-ɛ4 carriers also show increased uptake of amyloid-binding PET ligands (PiB) (Morris et al., 2010; Reiman et al., 2009).

Already in middle age, post-mortem studies of APOE-ɛ4 carriers have shown an increase in senile amyloid-β plaques (Kok et al., 2009), with some evidence of this even at ages as young as 30. It is therefore reasonable for us to speculate that in our young cohort, although amyloid-β plaques may not have yet developed, amyloid-β may be accumulating and, as in the rodent model, be associated with neural hyperactivity (Figure 5, upper panel). The mechanism for how APOE-ɛ4 carrier status leads to increased amyloid-β deposition is complex and may involve multiple mechanisms, including disruptions of amyloid-β clearance systems dependent on receptor-mediated, cerebrovascular or proteolytic degradation (Kanekiyo et al., 2014; Zhao et al., 2018). For example, one study has shown that APOE-ɛ4 leads to reduced levels of insulin-degrading enzyme (IDE), one of the most important enzymes for amyloid-β clearance (Du et al., 2009) and, as described above, is reduced during pre-symptomatic stages in rodent disease models which also show synaptic hyperactivity (Stargardt et al., 2015).

It has also been shown in a recent study, however, that neural hyperactivity can also occur in APOE-ɛ4 mouse models in the absence of overt amyloid or tau pathology (Nuriel et al., 2017) via a mechanism in which APOE-ɛ4 leads to an overall decrease in inhibition caused by a reduced responsiveness of excitatory neurons to GABAergic input. The authors therefore suggest that it is this APOE-ɛ4 related shift in the excitation/inhibition balance that leads to hyperactivity that later results in increased amyloid deposition, and hence increased risk of AD.

It is important to note, though, that increased amyloid-β is not sufficient to cause AD (Herrup, 2015; Sheline et al., 2010) and the presence of APOE-ɛ4 does not guarantee progression to AD. It remains to be investigated what role other important factors for AD play in this cascade of events, such as the tau protein (Ossenkoppele et al., 2016). Interestingly, PET studies of AD have also demonstrated AD-related hypometabolism in bilateral parieto-temporal regions that are very similar to the regions we report here (Kato et al., 2016).

Our findings therefore support theories based on rodent models (Stargardt et al., 2015), human computational models (de Haan et al., 2012b) and MCI data (Maestú et al., 2015) that propose that hyperactivity, ultimately related to hyperconnectivity, may be at the heart of brain dysfunction that could eventually lead to AD, and provides compelling evidence that this may already happen in early adulthood. Areas predisposed to hyperconnectivity are thought to be prone to yield increased accumulation of amyloid-β (Myers et al., 2014), supported by longitudinal PET and fMRI studies which found that hippocampal hyperactivation in elderly individuals leads to widespread increased deposition of amyloid-β as well as hippocampal signal loss and memory decline, although these studies found no effect of APOE-ɛ4 (O'Brien et al., 2010) or there was insufficient power to test this (Leal et al., 2017). The present study suggests that the parietal cortex is an important common early focus for hyperconnectivity, possibly as part of the posterior DMN. Importantly, this hyperconnectivity is present during the task-free resting-state, both avoiding and highlighting the potential confound of baseline-level differences in task-based group studies.

One important methodological note, for future work, is that our exploratory analysis, in which we combined the connectivity maps from different frequency bands using a vector sum approach, revealed both more robust connectivity patterns across participants, with increased number of valid edge connections, and increased sensitivity to both APOE-ɛ4 carrier effects and Alzheimer disease-related changes in connectivity. This simple combination of effects may therefore be worth adding to future studies in order to, potentially, provide increased sensitivity to disease-related modulations.

In addition to classical statistics we used machine-learning classification approaches to investigate how much discriminative information is present within these static oscillatory connectivity matrices. Here, the support-vector-machine (SVM) was trained used a pooled set of features taken from all valid connectivity edges from all frequency bands and the combined map. The SVM will, as part of its decision boundary optimisation, choose the best weighting of all these features. When tested using leave-one-out cross validation, the classifier was able to decode APOE-ɛ4 carriers from controls and AD patients from elderly controls, with significantly better than chance performance. In our small AD cohort, prediction performance appeared close to 70%, however, given the low number of participants in this study, this needs to be evaluated using a larger independent sample. Although our results here must be considered preliminary, they are encouraging – particularly as the AD patients in our study were relatively early in their diagnosis.

In the current study, we employed MEG to show patterns of oscillatory hyperactivity and hyperconnectivity in neural activity in young-adult APOE-ɛ4 carriers, identifying the right parietal cortex as an important centre in these hyperconnectivity patterns. One particularly attractive feature of MEG is that it provides non-invasive high-temporal and spatial-resolution measures of neurophysiology that allow for comparison with cellular hyperactivity data in a translational context (Busche et al., 2008), providing potential biomarkers for new therapeutic and preventative studies in the pathological processes leading to AD. Finally, an important question is why and at what stage hyperconnectivity ‘collapses’ to convert to the profound disconnection and symptomology of AD, and what further factors determine whether ɛ4 carriers indeed go on to develop AD. Future studies focusing on the often-neglected middle-age group, between the very young and the elderly, as well as longitudinal studies will be invaluable to unravel the time course of these events.

In this paper we present the results of analysing two MEG resting-state experiments: 1) A study of young healthy participants who have been genotyped for APOE-ɛ4 and 2) A study of Alzheimer’s disease (AD) patients compared to a matched cohort of elderly controls.

Experiment 1 young APOE-ε4 cohort: data acquisition and analysis

Data acquisition

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Five-minute whole-head MEG recordings were acquired at a 1200 Hz sample rate using a 275-channel CTF radial gradiometer system. An additional 29 reference channels were recorded for noise cancellation purposes and the primary sensors were analysed as synthetic third-order gradiometers (Vrba and Robinson, 2001). Subjects were seated upright in the magnetically shielded room with their head supported with a chin rest to minimise movement. Participants were asked to rest and fixate their eyes on a central red fixation point, presented on either a CRT monitor or LCD projector. Horizontal and vertical electro-oculograms (EOG) were recorded to monitor eye blinks and eye movements.

Participants also underwent a magnetic resonance imaging (MRI) session in which a T1-weighted 1 mm anatomical scan was acquired, using an inversion recovery spoiled gradient echo acquisition (3T, General Electric).

Participants further performed the MATRICS Consensus Cognitive Battery (MCCB) (Kern et al., 2008; Nuechterlein et al., 2008). Group performance was compared using two-sample t-tests, excluding missing or incomplete data (leaving per task N = 47–48 for ɛ4 carriers and N = 98–100 for non-carriers, see Table 1).

MEG data analysis

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MEG generates multi-dimensional data, which can be analysed in a large variety of ways. We sought to conduct an analysis investigating only consistent functional connectivity across the brain, while reducing noise. We focused on amplitude-amplitude connectivity of beamformer-derived oscillatory source signals, one of the most robust and repeatable types of MEG connectivity measures (Colclough et al., 2016), across six frequency bands and 90 AAL atlas brain areas. The analysis pipeline also allowed a voxelwise assessment of resting-state activity within each frequency band and is displayed in Figure 6.

Figure 6

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All datasets were down-sampled to 600 Hz, and filtered with a 1 Hz high-pass and a 150 Hz low-pass filter. The datasets were then segmented into 2 s epochs. The data were visually inspected and epochs with major artefacts such as head movements, large muscle contractions, or ocular artefacts were excluded from subsequent analysis.

To achieve MRI/MEG co-registration, fiduciary markers were placed at fixed distances from three anatomical landmarks identifiable in the subject’s anatomical MR scan, and their locations were manually marked in the MR image. Head localization was performed at the start and end of each recording.

The MEG sensor data were source-localised using FieldTrip (RRID:SCR_004849) version 20161011 (Oostenveld et al., 2011), with an LCMV beamformer on a 6 mm grid, using a single-shell forward model (Nolte, 2003), where the covariance matrix was constructed per each of the following frequency bands: 1–4, 3–8, 8–13, 13–30, 40–60, and 60–140 Hz. For each band, the beamformer weights were normalized using a vector norm (Hillebrand et al., 2012), data were normalized to the MNI template, and reduced to 90 nodes following the Automatic Anatomical Labelling (AAL) atlas (Tzourio-Mazoyer et al., 2002). The selection of one node per AAL region was achieved by performing a frequency analysis on all virtual channels within the AAL region, and selecting the virtual channel with the greatest temporal standard-deviation within the region. Epochs were concatenated to generate a continuous virtual-sensor timecourse for each voxel and then band-passed into the relevant frequency-band of interest (Delta: 1–4 Hz, Theta: 3–8 Hz, Alpha: 8–13 Hz, Beta-13–30 Hz, Low Gamma: 40–60 Hz and High-Gamma: 60–140 Hz).

The resulting 90-node time series were orthogonalized to avoid spurious correlations using a multivariate regression approach known as symmetric orthogonalization (Colclough et al., 2015). The data then underwent a Hilbert transform to obtain the oscillatory amplitude envelope, and were subsequently despiked to remove artefactual temporal transients using a median filter, downsampled to 1 Hz and trimmed to avoid edge effects (removing the first 2 and last three samples). Amplitude correlations were then calculated by correlating the 90 downsampled Hilbert envelopes to each other, and converted to variance-normalized Z-scores by applying a Fisher transform. In addition to analysing effects within each frequency band separately, we also synthesised a combined measure of connectivity by calculating the vector-sum of all connectivity matrices, using the following formula for each element (i,j) in the square connectivity matrix:

${\displaystyle \mathrm{C}\mathrm{o}\mathrm{m}\mathrm{b}\mathrm{i}\mathrm{n}\mathrm{e}{\mathrm{d}}_{\mathrm{i}\mathrm{j}}=\mathrm{s}\mathrm{q}\mathrm{r}\mathrm{t}(\mathrm{D}\mathrm{e}\mathrm{l}\mathrm{t}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}{}^2+\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{t}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}{}^2+\mathrm{A}\mathrm{l}\mathrm{p}\mathrm{h}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}{}^2+\mathrm{B}\mathrm{e}\mathrm{t}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}{}^2+\mathrm{L}\mathrm{o}\mathrm{w}\mathrm{G}\mathrm{a}\mathrm{m}\mathrm{m}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}{}^2+\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{G}\mathrm{a}\mathrm{m}\mathrm{m}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}{}^2)}$

Assessment of activity differences

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In order to assess possible cohort differences in oscillatory activity, rather than connectivity, we also calculated a measure of temporal variance at each beamformer-reconstructed voxel in the brain, for each separate frequency band, via an assessment of the amplitude envelopes. Due to large variations in signal sensitivity throughout the brain, beamformer reconstructions of virtual-sensors result in signals in which the mean and temporal standard-deviation of the amplitude envelopes are highly correlated and show large variations across the source space. For that reason, we choose to use a normalized measure, the coefficient of variation, which is the simply the standard-deviation of the amplitude envelope divided by the mean of the amplitude envelope. Using this measure, voxels which show a high fractional temporal variability, compared to the mean, will have a high value, whilst those voxels that are of more stable oscillatory power will have a low value. We note that the measure is similar, by analogy, to common measures employed in BOLD fMRI, such as percentage-change from baseline. The statistical significance of group-effects was assessed using univariate voxelwise unpaired t-tests of this activity measure and then correcting for multiple comparisons using randomisation testing using FSL randomise (Nichols and Holmes, 2003; Singh et al., 2003; Winkler et al., 2014). The latter procedure was performed using threshold-free cluster enhancement (TFCE) and 5000 iterations, without variance smoothing. This procedure tests positive and negative cohort contrasts separately so a null hypothesis corrected p-value of 0.025 was chosen, so as to implement a corrected two-tailed test at p<0.05. Separately for each frequency band, thresholded t-statistics are displayed on a template brain surface mesh, extracted using FreeSurfer.

Both the necessary derived data and custom scripts used to create Figures 1, 2, 3 and 4 are freely available as an Open-Science Framework project (http://dx.doi.org/10.17605/OSF.IO/E4CJX). Due to the sensitive data of the genetic risk factors investigated here, and the need to prevent both self-identification and third-party identification of people in this project, both the raw data and pseudo-anonymised intermediate analysis results cannot be provided. Controlled access to the pseudo-anonymised intermediate analysis results might be possible via a research collaboration after signing of suitable data-transfer agreements and approval by both Cardiff University School of Psychology's Ethics Committee and the governing body for the cohort, i.e. the National Centre for Mental Health (NCMH). Further enquiries should be directed to Professor Krish D Singh at CUBRIC, School of Psychology, Maindy Road, Cardiff University, Cardiff CF24 4HQ or email: singhkd@cardiff.ac.uk.

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Author details

1. Loes Koelewijn

   Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Formal analysis, Validation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7890-171X

2. Thomas M Lancaster

   1. Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
   2. Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
   3. MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

3. David Linden

   1. Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
   2. Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
   3. MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

4. Diana C Dima

   Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9612-5574

5. Bethany C Routley

   Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Data curation, Investigation, Project administration

   Competing interests

   No competing interests declared

6. Lorenzo Magazzini

   Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Data curation, Investigation, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8934-8374

7. Kali Barawi

   MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom

   Contribution

   Data curation, Investigation, Project administration

   Competing interests

   No competing interests declared

8. Lisa Brindley

   Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Data curation, Investigation, Project administration

   Competing interests

   No competing interests declared

9. Rachael Adams

   Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Investigation, Project administration

   Competing interests

   No competing interests declared

10. Katherine E Tansey

    Core Bioinformatics and Statistics Team, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9663-3376

11. Aline Bompas

    Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

    Contribution

    Data curation, Investigation, Project administration

    Competing interests

    No competing interests declared

12. Andrea Tales

    Department of Psychology, College of Human and Health Sciences, Swansea University, Swansea, United Kingdom

    Contribution

    Resources

    Competing interests

    No competing interests declared

13. Antony Bayer

    School of Medicine, Cardiff University, Cardiff, United Kingdom

    Contribution

    Resources

    Competing interests

    No competing interests declared

14. Krish Singh

    1. Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
    2. Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom

    Contribution

    Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Visualization, Methodology, Project administration, Writing—review and editing

    For correspondence

    singhkd@cardiff.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3094-2475

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