Reliability of dynamic causal modelling of resting-state magnetoencephalography.

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.

Multiplexity of human brain oscillations as a personal brain signature.

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

Genetic common variants associated with cerebellar volume and their overlap with mental disorders: a study on 33,265 individuals from the UK-Biobank.

Interest in the cerebellum is expanding given evidence of its contributions to cognition and emotion, and dysfunction in various psychopathologies. However, research into its genetic architecture and shared influences with liability for mental disorders is lacking. We conducted a genome-wide association study (GWAS) of total cerebellar volume and underlying cerebellar lobe volumes in 33,265 UK-Biobank participants. Total cerebellar volume was heritable (h2SNP = 50.6%), showing moderate genetic homogeneity across lobes (h2SNP from 35.4% to 57.1%; mean genetic correlation between lobes rg ≈ 0.44). We identified 33 GWAS signals associated with total cerebellar volume, of which 6 are known to alter protein-coding gene structure, while a further five mapped to genomic regions known to alter cerebellar tissue gene expression. Use of summary data-based Mendelian randomisation further prioritised genes whose change in expression appears to mediate the SNP-trait association. In total, we highlight 21 unique genes of greatest interest for follow-up analyses. Using LD-regression, we report significant genetic correlations between total cerebellar volume and brainstem, pallidum and thalamus volumes. While the same approach did not result in significant correlations with psychiatric phenotypes, we report enrichment of schizophrenia, bipolar disorder and autism spectrum disorder associated signals within total cerebellar GWAS results via conditional and conjunctional-FDR analysis. Via these methods and GWAS catalogue, we identify which of our cerebellar genomic regions also associate with psychiatric traits. Our results provide important insights into the common allele architecture of cerebellar volume and its overlap with other brain volumes and psychiatric phenotypes.

Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales.

Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or 'irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to 'scale frequencies, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections. These showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band. The physiological relevance of MRVE was also investigated by examining the relationship between connectivity strength and local variability. It was found that local activity at frequencies fS≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity, as measured by fine scale MRVE correlation (fS∼ 30-150Hz) and by alpha and beta band AEC. Analysis of the EOG recordings also revealed that eye movement affected both connectivity measures. Higher levels of eye movement were associated with stronger frontal connectivity, as measured by MRVE correlation. More eye movement was also associated with reduced occipital and parietal connectivity strength for both connectivity measures, although this was not significant after correction for multiple comparisons.

A comparison of GABA-ergic (propofol) and non-GABA-ergic (dexmedetomidine) sedation on visual and motor cortical oscillations, using magnetoencephalography.

Studying changes in cortical oscillations can help elucidate the mechanistic link between receptor physiology and the clinical effects of anaesthetic drugs. Propofol, a GABA-ergic drug produces divergent effects on visual cortical activity: increasing induced gamma-band responses (GBR) while decreasing evoked responses. Dexmedetomidine, an α2- adrenergic agonist, differs from GABA-ergic sedatives both mechanistically and clinically as it allows easy arousability from deep sedation with less cognitive side-effects. Here we use magnetoencephalography (MEG) to characterize and compare the effects of GABA-ergic (propofol) and non-GABA-ergic (dexmedetomidine) sedation, on visual and motor cortical oscillations. Sixteen male participants received target-controlled infusions of propofol and dexmedetomidine, producing mild-sedation, in a placebo-controlled, cross-over study. MEG data was collected during a combined visuomotor task. The key findings were that propofol significantly enhanced visual stimulus induced GBR (44% increase in amplitude) while dexmedetomidine decreased it (40%). Propofol also decreased the amplitudes of the Mv100 (visual M100) (27%) and Mv150 (52%) visual evoked fields (VEF), whilst dexmedetomidine had no effect on these. During the motor task, neither drug had any significant effect on movement related gamma synchrony (MRGS), movement related beta de-synchronisation (MRBD) or Mm100 (movement-related M100) movement-related evoked fields (MEF), although dexmedetomidine slowed the Mm300. Dexmedetomidine increased (92%) post-movement beta synchronisation/rebound (PMBR) power while propofol reduced it (70%, statistically non- significant). Overall, dexmedetomidine and propofol, at equi-sedative doses, produce contrasting effects on visual induced GBR, VEF, PMBR and MEF. These findings provide a mechanistic link between the known receptor physiology of these sedative drugs with their known clinical effects and may be used to explore mechanisms of other anaesthetic drugs on human consciousness.

Generative modelling of the thalamo-cortical circuit mechanisms underlying the neurophysiological effects of ketamine.

Cortical recordings of task-induced oscillations following subanaesthetic ketamine administration demonstrate alterations in amplitude, including increases at high-frequencies (gamma) and reductions at low frequencies (theta, alpha). To investigate the population-level interactions underlying these changes, we implemented a thalamo-cortical model (TCM) capable of recapitulating broadband spectral responses. Compared with an existing cortex-only 4-population model, Bayesian Model Selection preferred the TCM. The model was able to accurately and significantly recapitulate ketamine-induced reductions in alpha amplitude and increases in gamma amplitude. Parameter analysis revealed no change in receptor time-constants but significant increases in select synaptic connectivity with ketamine. Significantly increased connections included both AMPA and NMDA mediated connections from layer 2/3 superficial pyramidal cells to inhibitory interneurons and both GABAA and NMDA mediated within-population gain control of layer 5 pyramidal cells. These results support the use of extended generative models for explaining oscillatory data and provide in silico support for ketamine's ability to alter local coupling mediated by NMDA, AMPA and GABA-A.

Relationships between cortical myeloarchitecture and electrophysiological networks.

The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.

Neural correlates of the LSD experience revealed by multimodal neuroimaging.

Lysergic acid diethylamide (LSD) is the prototypical psychedelic drug, but its effects on the human brain have never been studied before with modern neuroimaging. Here, three complementary neuroimaging techniques: arterial spin labeling (ASL), blood oxygen level-dependent (BOLD) measures, and magnetoencephalography (MEG), implemented during resting state conditions, revealed marked changes in brain activity after LSD that correlated strongly with its characteristic psychological effects. Increased visual cortex cerebral blood flow (CBF), decreased visual cortex alpha power, and a greatly expanded primary visual cortex (V1) functional connectivity profile correlated strongly with ratings of visual hallucinations, implying that intrinsic brain activity exerts greater influence on visual processing in the psychedelic state, thereby defining its hallucinatory quality. LSD's marked effects on the visual cortex did not significantly correlate with the drug's other characteristic effects on consciousness, however. Rather, decreased connectivity between the parahippocampus and retrosplenial cortex (RSC) correlated strongly with ratings of "ego-dissolution" and "altered meaning," implying the importance of this particular circuit for the maintenance of "self" or "ego" and its processing of "meaning." Strong relationships were also found between the different imaging metrics, enabling firmer inferences to be made about their functional significance. This uniquely comprehensive examination of the LSD state represents an important advance in scientific research with psychedelic drugs at a time of growing interest in their scientific and therapeutic value. The present results contribute important new insights into the characteristic hallucinatory and consciousness-altering properties of psychedelics that inform on how they can model certain pathological states and potentially treat others.

Induced and Evoked Properties of Vibrotactile Adaptation in the Primary Somatosensory Cortex.

Prolonged exposure to afferent stimulation ("adaptation") can cause profound short-term changes in the responsiveness of cortical sensory neurons. While several models have been proposed that link adaptation to single-neuron dynamics, including GABAergic inhibition, the process is currently imperfectly understood at the whole-brain level in humans. Here, we used magnetoencephalography (MEG) to examine the neurophysiological correlates of adaptation within SI in humans. In one condition, a 25 Hz adapting stimulus (5 s) was followed by a 1 s 25 Hz probe ("same"), and in a second condition, the adapting stimulus was followed by a 1 s 180 Hz probe ("different"). We hypothesized that changes in the mu-beta activity band (reflecting GABAergic processing) would be modulated differently between the "same" and "different" probe stimuli. We show that the primary somatosensory (SI) mu-beta response to the "same" probe is significantly reduced (p = 0.014) compared to the adapting stimulus, whereas the mu-beta response to the "different" probe is not (p = n.s.). This reduction may reflect sharpening of the spatiotemporal pattern of activity after adaptation. The stimulus onset mu-beta response did not differ between a 25 Hz adapting stimulus and a 180 Hz probe, suggesting that the mu-beta response is independent of stimulus frequency. Furthermore, we show a sustained evoked and induced desynchronization for the duration of the adapting stimulus, consistent with invasive studies. Our findings are important in understanding the neurophysiology underlying short-term and stimulus-induced plasticity in the human brain and shows that the brain response to tactile stimulation is altered after only brief stimulation.

Spatial attention modulates visual gamma oscillations across the human ventral stream.

Oscillatory synchronization in the gamma frequency range has been proposed as a neuronal mechanism to prioritize processing of relevant stimuli over competing ones. Recent studies in animals found that selective spatial attention enhanced gamma-band synchronization in high-order visual areas (V4) and increased the gamma peak frequency in V1. The existence of such mechanisms in the human visual system is yet to be fully demonstrated. In this study, we used MEG, in combination with an optimised stimulus design, to record visual gamma oscillations from human early visual cortex, while participants performed a visuospatial attention cueing task. First, we reconstructed virtual sensors in V1/V2, where gamma oscillations were strongly induced by visual stimulation alone. Second, following the results of a statistical comparison between conditions of attention, we reconstructed cortical activity also in inferior occipital-temporal regions (V4). The results indicated that gamma amplitude was modulated by spatial attention across the cortical hierarchy, both in the early visual cortex and in higher-order regions of the ventral visual pathway. In contrast, we found no evidence for an increase in the gamma peak frequency in V1/V2 with attention. The gamma response tended to peak earlier in V1/V2 than in V4 by approximately 70 ms, consistent with a feed-forward role of gamma-band activity in propagating sensory representations across the visual cortical hierarchy. Together, these findings suggest that differences in experimental design or methodology can account for the inconsistencies in previous animal and human studies. Furthermore, our results are in line with the hypothesis of enhanced gamma-band synchronization as an attentional mechanism in the human visual cortex.

Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception.

In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at ∼180 ms and are linked to spatial frequency processing. Furthermore, dorsal and ventral stream areas reveal temporally and spatially overlapping representations of low and high-level layer activations extracted from a feedforward neural network. Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential in human visual perception.

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces.

Recognizing emotion in faces is important in human interaction and survival, yet existing studies do not paint a consistent picture of the neural representation supporting this task. To address this, we collected magnetoencephalography (MEG) data while participants passively viewed happy, angry and neutral faces. Using time-resolved decoding of sensor-level data, we show that responses to angry faces can be discriminated from happy and neutral faces as early as 90 ms after stimulus onset and only 10 ms later than faces can be discriminated from scrambled stimuli, even in the absence of differences in evoked responses. Time-resolved relevance patterns in source space track expression-related information from the visual cortex (100 ms) to higher-level temporal and frontal areas (200-500 ms). Together, our results point to a system optimised for rapid processing of emotional faces and preferentially tuned to threat, consistent with the important evolutionary role that such a system must have played in the development of human social interactions.

Peak visual gamma frequency is modified across the healthy menstrual cycle.

Fluctuations in gonadal hormones over the course of the menstrual cycle are known to cause functional brain changes and are thought to modulate changes in the balance of cortical excitation and inhibition. Animal research has shown this occurs primarily via the major metabolite of progesterone, allopregnanolone, and its action as a positive allosteric modulator of the GABAA receptor. Our study used EEG to record gamma oscillations induced in the visual cortex using stationary and moving gratings. Recordings took place during twenty females' mid-luteal phase when progesterone and estradiol are highest, and early follicular phase when progesterone and estradiol are lowest. Significantly higher (∼5 Hz) gamma frequency was recorded during the luteal compared to the follicular phase for both stimuli types. Using dynamic causal modeling, these changes were linked to stronger self-inhibition of superficial pyramidal cells in the luteal compared to the follicular phase. In addition, the connection from inhibitory interneurons to deep pyramidal cells was found to be stronger in the follicular compared to the luteal phase. These findings show that complex functional changes in synaptic microcircuitry occur across the menstrual cycle and that menstrual cycle phase should be taken into consideration when including female participants in research into gamma-band oscillations.

Assessment and elimination of the effects of head movement on MEG resting-state measures of oscillatory brain activity.

Magnetoencephalography (MEG) is increasingly being used to study brain function because of its excellent temporal resolution and its direct association with brain activity at the neuronal level. One possible cause of error in the analysis of MEG data comes from the fact that participants, even MEG-experienced ones, move their head in the MEG system. Head movement can cause source localization errors during the analysis of MEG data, which can result in the appearance of source variability that does not reflect brain activity. The MEG community places great importance in eliminating this source of possible errors as is evident, for example, by recent efforts to develop head casts that limit head movement in the MEG system. In this work we use software tools to identify, assess and eliminate from the analysis of MEG data any possible correlations between head movement in the MEG system and widely-used measures of brain activity derived from MEG resting-state recordings. The measures of brain activity we study are a) the Hilbert-transform derived amplitude envelope of the beamformer time series and b) functional networks; both measures derived by MEG resting-state recordings. Ten-minute MEG resting-state recordings were performed on healthy participants, with head position continuously recorded. The sources of the measured magnetic signals were localized via beamformer spatial filtering. Temporal independent component analysis was subsequently used to derive resting-state networks. Significant correlations were observed between the beamformer envelope time series and head movement. The correlations were substantially reduced, and in some cases eliminated, after a participant-specific temporal high-pass filter was applied to those time series. Regressing the head movement metrics out of the beamformer envelope time series had an even stronger effect in reducing these correlations. Correlation trends were also observed between head movement and the activation time series of the default-mode and frontal networks. Regressing the head movement metrics out of the beamformer envelope time series completely eliminated these correlations. Additionally, applying the head movement correction resulted in changes in the network spatial maps for the visual and sensorimotor networks. Our results a) show that the results of MEG resting-state studies that use the above-mentioned analysis methods are confounded by head movement effects, b) suggest that regressing the head movement metrics out of the beamformer envelope time series is a necessary step to be added to these analyses, in order to eliminate the effect that head movement has on the amplitude envelope of beamformer time series and the network time series and c) highlight changes in the connectivity spatial maps when head movement correction is applied.

Benign childhood epilepsy with centrotemporal spikes (BECTS) and developmental co-ordination disorder.

BACKGROUND: Benign epilepsy with centro-temporal spikes (BECTS) is a common childhood epilepsy syndrome also known as Rolandic Epilepsy (RE). Neurocognitive phenotypes have been described with greater focus on attention, reading and language domains but there have been far fewer studies focusing on motor functioning. This study included measures of motor, language and cognition in order to investigate the range, degree and pattern of difficulties associated with BECTS in a case series of children, but with a particular emphasis on motor skills. METHOD: Twenty-one children aged between 8 and 16years with a diagnosis of BECTS were asked to complete standardized assessments for language, cognition, motor functioning and handwriting. RESULTS: When measuring across language, cognitive and motor domains, 19 (90.48%) of the twenty-one children with a diagnosis of BECTS showed some difficulties on at least one area of functioning using standardized assessment tests. Of particular note nearly half (47.62%) of the children had some difficulties in one or more areas of motor functioning. DISCUSSION: Children with BECTS have a heterogeneous pattern of neurocognitive impairments. The presence of motor difficulties (DCD) should be considered in all children routinely seen in clinical settings with BECTS and included in any screening processes.

Transcranial modulation of brain oscillatory responses: A concurrent tDCS-MEG investigation.

Despite the increasing use of transcranial direct current stimulation (tDCS), the physiological mechanisms underlying its effects are still largely unknown. One approach to directly investigate the effects of the neuromodulation technique on the brain is to integrate tDCS with non-invasive neuroimaging in humans. To provide new insight into the neurobiology of the method, DC stimulation (1mA, 600s) was applied concurrently with Magnetoencephalography (MEG), while participants engaged in a visuomotor task before, during and after a period of tDCS. Responses in the motor beta band (15-30Hz) and visual gamma band (30-80Hz) were localised using Synthetic Aperture Magnetometry (SAM). The resulting induced and evoked oscillatory responses were subsequently analysed. A statistically significant reduction of average power in the visual gamma band was observed for anodal compared to sham stimulation. The magnitude of motor evoked responses was also found to be significantly modulated by anodal stimulation. These results demonstrate that MEG can be used to derive inferences on the cortical mechanisms of tDCS.

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields.

This article describes the first application of a generic (empirical) Bayesian analysis of between-subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non-invasive (MEG) data can be used to characterize subject-specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation-inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject-specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal-interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models-and optimize the corresponding connectivity estimates at the within and between-subject level. We also consider Bayesian cross-validation to obtain predictive estimates for gamma-response phenotypes, using a leave-one-out procedure. Hum Brain Mapp 37:4597-4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Significant reductions in human visual gamma frequency by the gaba reuptake inhibitor tiagabine revealed by robust peak frequency estimation.

The frequency of visual gamma oscillations is determined by both the neuronal excitation-inhibition balance and the time constants of GABAergic processes. The gamma peak frequency has been linked to sensory processing, cognitive function, cortical structure, and may have a genetic contribution. To disentangle the intricate relationship among these factors, accurate and reliable estimates of peak frequency are required. Here, a bootstrapping approach that provides estimates of peak frequency reliability, thereby increasing the robustness of the inferences made on this parameter was developed. The method using both simulated data and real data from two previous pharmacological MEG studies of visual gamma with alcohol and tiagabine was validated. In particular, the study by Muthukumaraswamy et al. [] (Neuropsychopharmacology 38(6):1105-1112), in which GABAergic enhancement by tiagabine had previously demonstrated a null effect on visual gamma oscillations, contrasting with strong evidence from both animal models and very recent human studies was re-evaluated. After improved peak frequency estimation and additional exclusion of unreliably measured data, it was found that the GABA reuptake inhibitor tiagabine did produce, as predicted, a marked decrease in visual gamma oscillation frequency. This result demonstrates the potential impact of objective approaches to data quality control, and provides additional translational evidence for the mechanisms of GABAergic transmission generating gamma oscillations in humans. Hum Brain Mapp 37:3882-3896, 2016. © 2016 Wiley Periodicals, Inc.

Comparison of the repeatability of GABA-edited magnetic resonance spectroscopy with and without macromolecule suppression.

PURPOSE: The inhibitory neurotransmitter γ-aminobutyric acid (GABA) can be measured in vivo using edited magnetic resonance spectroscopy (MRS), but quantification suffers from contamination by macromolecules (MM). It is possible to suppress this contamination using symmetric editing, but this procedure potentially compromises reliability of the GABA measurement. The aim of this study was to compare the repeatability of GABA-edited MRS with and without MM suppression. METHODS: GABA' (non-MM contaminated) and GABA'+MM (MM-contaminated) concentration was measured in the occipital lobe (OCC) and anterior cingulate (AC) using symmetric and standard editing (n = 15). Each method was performed twice in each region. RESULTS: Within-participant coefficients of variation for each technique were 4.0% (GABA'+MM) and 8.6% (GABA') in the OCC and 14.8% (GABA'+MM) and 12.6% (GABA') in the AC. Intraclass correlation coefficients were better for the suppression method than standard editing in both the OCC (0.72 versus 0.67) and AC (0.41 versus 0.16). These findings were replicated in the OCC of a second cohort (n = 15). CONCLUSION: Symmetric suppression is shown to be comparable in repeatability to standard GABA-editing. Measuring a purer quantification of GABA becomes increasingly important as more research is conducted on links between GABA concentration, pathology and healthy behavior.

Quantification of γ-aminobutyric acid (GABA) in 1 H MRS volumes composed heterogeneously of grey and white matter.

The quantification of γ-aminobutyric acid (GABA) concentration using localised MRS suffers from partial volume effects related to differences in the intrinsic concentration of GABA in grey (GM) and white (WM) matter. These differences can be represented as a ratio between intrinsic GABA in GM and WM: rM . Individual differences in GM tissue volume can therefore potentially drive apparent concentration differences. Here, a quantification method that corrects for these effects is formulated and empirically validated. Quantification using tissue water as an internal concentration reference has been described previously. Partial volume effects attributed to rM can be accounted for by incorporating into this established method an additional multiplicative correction factor based on measured or literature values of rM weighted by the proportion of GM and WM within tissue-segmented MRS volumes. Simulations were performed to test the sensitivity of this correction using different assumptions of rM taken from previous studies. The tissue correction method was then validated by applying it to an independent dataset of in vivo GABA measurements using an empirically measured value of rM . It was shown that incorrect assumptions of rM can lead to overcorrection and inflation of GABA concentration measurements quantified in volumes composed predominantly of WM. For the independent dataset, GABA concentration was linearly related to GM tissue volume when only the water signal was corrected for partial volume effects. Performing a full correction that additionally accounts for partial volume effects ascribed to rM successfully removed this dependence. With an appropriate assumption of the ratio of intrinsic GABA concentration in GM and WM, GABA measurements can be corrected for partial volume effects, potentially leading to a reduction in between-participant variance, increased power in statistical tests and better discriminability of true effects.