High Precision Anatomy for MEG

Magnetoencephalography (MEG) is a non-invasive brain imaging method with high temporal resolution but relatively poor spatial resolution as compared to some other non-invasive techniques. This thesis examines how the spatial resolution of MEG can be improved using new recording paradigms in which the subject’s head position is fixed and known in advance. This is accomplished by using subject-specific head casts made using a combination of structural MRI and 3D printing technology. The resulting high-precision spatial models allow one to make inference at spatial scales of the order of cortical laminae. This thesis outlines the design of the head casts and examines the potential theoretical and empirical advances they offer. Specifically I outline simulation and then empirical investigations showing it is possible to non-invasively distinguish between electrophysiological signals in different layers of the cortex

Neuromagnetic effects of pico-Tesla stimulation

We used a double-blind experimental design to look for an effect of pico-Tesla magnetic stimulation in healthy subjects. Pico-Tesla stimulation is thought to increase the dominant frequency of 2–7 Hz oscillations in the human brain. We used magnetoencephalography to measure resting state brain activity. Each subject had two separate recording sessions consisting of three runs in between which they were given real or sham pT stimulation. We then tried to predict the real and sham stimulation sessions based on changes in the mean peak frequency in the 2–7 Hz band. Our predictions for these individual runs were 8 out of 14 at chance level (p = 0.39). After unblinding, we found no significant effect (p = 0.11) of an increase in the frequency range (2–7 Hz) across the subject group. Finally, we performed a Bayesian model comparison between the effect size predicted from previous clinical studies and a null model. Even though this study had a sensitivity advantage of at least one order of magnitude over previous work, we found the null model to be significantly (2000 times) more likely

Accelerated forgetting of a trauma-like event in healthy men and women after a single dose of hydrocortisone

Posttraumatic stress disorder (PTSD) is characterised by dysregulated hypothalamic-pituitary-adrenal axis activity and altered glucocorticoid receptor sensitivity. Early treatment with glucocorticoids may reduce PTSD risk, although the effect of such treatment on the aetiologically critical step of traumatic-memory-formation remains unclear. Here we examine the effects of exogenous cortisol (hydrocortisone) in a preclinical model of PTSD, using a factorial (Drug × Sex), randomised-controlled, double-blind design. Healthy men and women (n = 120) were randomised to receive 30 mg oral hydrocortisone or matched placebo immediately after watching a stressful film. Effects on film-related intrusions were assessed acutely in the lab, and ecologically using daily memory diaries for one week. We found that participants receiving hydrocortisone showed a faster reduction in daily intrusion frequency. Voluntary memory was assessed once, at the end of the week, but was unaffected by hydrocortisone. Exploratory analyses indicated sex-dependent associations between intrusions and baseline estradiol and progesterone levels. In men receiving hydrocortisone, higher baseline estradiol levels were associated with fewer intrusions, whereas women exhibited the opposite pattern. By contrast, progesterone levels were positively associated with intrusions only in men treated with hydrocortisone. The findings suggest that hydrocortisone promotes an accelerated degradation of sensory-perceptual representations underlying traumatic intrusive memories. In addition, while sex alone was not an important moderator, the combination of sex and sex-hormone levels (especially estradiol) influenced hydrocortisone’s effects on involuntary aversive memories. Future well-powered experimental studies may provide a basis for a precision-psychiatry approach to optimising early post-traumatic glucocorticoid treatments that target intrusive memories, based on individual endocrinological profiles

Rewarding Subjective Effects of the NMDAR Antagonist Nitrous Oxide (Laughing Gas) Are Moderated by Impulsivity and Depressive Symptoms in Healthy Volunteers

BACKGROUND: Nitrous oxide (N2O) is an anaesthetic gas with both therapeutic and abuse potential. As an NMDAR antagonist, its effects are expected to resemble those of the prototypical NMDAR antagonist, ketamine. Here, we examine the subjective rewarding effects of N2O using measures previously employed in studies of ketamine. We also test for moderation of these effects by bipolar phenotype, depressive symptoms, and impulsivity. METHODS: Healthy volunteers were randomised to either 50% N2O (n=40) or medical air (n=40). Self-reported rewarding (liking and wanting), and alcohol-like effects were assessed pre-, peri- and post-inhalation. RESULTS: Effect sizes for the various rewarding/alcohol-like effects of N2O were generally similar to those reported in studies of moderate-dose ketamine. Impulsivity moderated the subjective reinforcing (liking) effects of inhaled gas, while depressive symptoms moderated motivational (wanting [more]) effects. However, depression and impulsivity had opposite directional influences, such that higher impulsivity was associated with higher N2O-liking, and higher depression, with lower N2O-wanting. CONCLUSIONS: To the extent that static (versus longitudinal) subjective rewarding effects are a reliable indicator of future problematic drug use, our findings suggests that impulsivity and depression may respectively predispose and protect against N2O abuse. Future studies should examine if these moderators are relevant for other NMDAR antagonists, including ketamine, and novel ketamine-like therapeutics and recreational drugs. Similarities between moderate-dose N2O and moderate-dose ketamine in the intensity of certain subjective effects suggest that N2O may, at least partially, substitute for ketamine as a safe and easily-implemented experimental tool for probing reward-related NMDAR function and dysfunction in humans

Reconstructing anatomy from electro-physiological data

Here we show how it is possible to make estimates of brain structure based on MEG data. We do this by reconstructing functional estimates onto distorted cortical manifolds parameterised in terms of their spherical harmonics. We demonstrate that both empirical and simulated MEG data give rise to consistent and plausible anatomical estimates. Importantly, the estimation of structure from MEG data can be quantified in terms of millimetres from the true brain structure. We show, for simulated data, that the functional assumptions which are closer to the functional ground-truth give rise to anatomical estimates that are closer to the true anatomy

Discrimination of cortical laminae using MEG.

Typically MEG source reconstruction is used to estimate the distribution of current flow on a single anatomically derived cortical surface model. In this study we use two such models representing superficial and deep cortical laminae. We establish how well we can discriminate between these two different cortical layer models based on the same MEG data in the presence of different levels of co-registration noise, Signal-to-Noise Ratio (SNR) and cortical patch size. We demonstrate that it is possible to make a distinction between superficial and deep cortical laminae for levels of co-registration noise of less than 2mm translation and 2° rotation at SNR>11dB. We also show that an incorrect estimate of cortical patch size will tend to bias layer estimates. We then use a 3D printed head-cast (Troebinger et al., 2014) to achieve comparable levels of co-registration noise, in an auditory evoked response paradigm, and show that it is possible to discriminate between these cortical layer models in real data

High precision anatomy for MEG.

Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1mm. Estimates of relative co-registration error were <1.5mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG

Does function fit structure? A ground truth for non-invasive neuroimaging.

There are now a number of non-invasive methods to image human brain function in-vivo. However, the accuracy of these images remains unknown and can currently only be estimated through the use of invasive recordings to generate a functional ground truth. Neuronal activity follows grey matter structure and accurate estimates of neuronal activity will have stronger support from accurate generative models of anatomy. Here we introduce a general framework that, for the first time, enables the spatial distortion of a functional brain image to be estimated empirically. We use a spherical harmonic decomposition to modulate each cortical hemisphere from its original form towards progressively simpler structures, ending in an ellipsoid. Functional estimates that are not supported by the simpler cortical structures have less inherent spatial distortion. This method allows us to compare directly between magnetoencephalography (MEG) source reconstructions based upon different assumption sets without recourse to functional ground truth

Optimising experimental design for MEG resting state functional connectivity measurement

The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia