Biology subjects

Singh, K. D.

Publications and source records attributed to Singh, K. D..

At least 19 recordsLinked to original sources

bioRxiv2026

AT1R-Selective Negative Allosteric Modulator Targeting the Epitope for Pathogenic Autoantibody Activation

A major therapeutic strategy for managing cardiovascular and renal diseases is the use of angiotensin receptor blockers (ARBs). These drugs block both pathogenic and physiological signaling by the angiotensin II type 1 receptor (AT1R) by preventing angiotensin II (AngII) binding to orthosteric pocket in transmembrane portion of AT1R. However, in many autoimmune diseases, AT1R activation is chronically driven by pathogenic autoantibody binding to a distinct extracellular surface located allosteric site. Currently, there are no allosteric inhibitors of AT1R available to mechanistically prevent the autoantibodies and preserve the orthosteric hormonal functions of AngII. Here, we report the structure-guided hit-to-lead optimization of TP1, a first-in-class AT1R allosteric chemotype and negative allosteric modulator (NAM) with improved potency and drug-like properties. Computational docking and site-directed mutagenesis identified a TP1 binding mode that is distinct from the orthosteric-site binding of ARBs and AngII. Synthetically pure TP1 significantly reduced the capacity of AT1R to bind AngII without affecting AngII binding to AT2R, demonstrating an AT1R selective effect. TP1 reduced AngII-induced calcium signaling and migration in AT1R-expressing cells, arterial contraction, and increases in blood pressure in AngII-infused mice, demonstrating its activity as a NAM. Crucially, TP1 blocked binding of pathogenic autoantibodies to AT1R-expressing cells from the serum of patients with preeclampsia and dilated cardiomyopathy, a function distinct from ARBs. Together, these findings establish TP1 as a proof-of-concept, AT1R-selective NAM chemotype with therapeutic potential for AT1R autoantibody-driven conditions such as preeclampsia, where ARBs are strictly contraindicated. Developing AT1R-targeted NAM antihypertensive drugs may offer therapeutic advantages over ARBs and expand the pharmacological toolkit for this important CVD receptor. Key words: AT1R, AngII, AT1R-AAb, NAM, Allosteric ligand, GPCR, blood pressure, pharmacokinetics, antihypertensives

pharmacology and toxicology↗

bioRxiv2026

No overlap in neurophysiological predictors of moment-to-moment fluctuations of behavioural variability and subjective attentional state

Behaviour fluctuates from moment to moment - often attributed to changes in subjective attentional states (e.g., mind wandering). Yet, it remains unclear how closely behavioural variability and subjective attentional state are related, and whether they share biological mechanisms. Using [~]2 hours of Metronome Response Task data, we combined magnetoencephalography, eye and head tracking, pupillometry, and thought probes to predict trial-to-trial fluctuations in both. Although they covaried weakly, this relationship was largely accounted for by time-on-task. Several markers including blink duration, pupil variability, and head movement predicted both measures. Dissociations also emerged: behavioural variability was linked to broadband functional connectivity and delta power, whereas off-taskness was predicted by theta, alpha, and beta power. Critically, predictive strength of behavioural variability and subjective reports were uncorrelated at the individual level, with substantial individual differences. For both outcomes, multimodal models outperformed univariate analyses. Our findings show that behavioural variability and subjective attentional state are largely dissociable, challenging the assumption that they share underlying process or that one causes the other.

neuroscience↗

bioRxiv2026

From Brain Microstructure to Dynamics: Linking Grey and White Matter Architecture to Propagation Delays

Understanding the relationship between neural dynamics and underlying brain structure, and in particular the impact of the latter on conduction delays, remains a core question in neuroscience. In humans, we primarily access these phenomena at the macroscale through neurophysiological measures of propagation. At this scale, signal propagation is reflected in measurable delays arising from both white matter (WM) axonal conduction and grey matter (GM) synaptic and integrative processes. While specific WM features have been explicitly linked to single-axon conduction delays, no clear models link GM microstructural properties to large-scale propagation. To this aim, we combined advanced MRI microstructural modelling with resting-state MEG to link brain structure to signal propagation in a sample of 94 healthy controls. WM metrics included axonal diameter, myelination (g-ratio, myelin water fraction), and tract length, while GM was characterised using the SANDI model and cortical thickness. Propagation delays were quantified using the neuronal avalanches framework. Our findings indicated significant associations between all of WM and GM metrics and the propagation delays. When attempting to use our W/GM metrics we were able to predict up to ~26% of the observed delay using linear regression modelling. We also found associations between frequency-specific propagation dynamics and tract length. Finally, using a canonical correlation analysis we demonstrated multivariate coupling between our W/GM metrics and the MEG frequency-specific propagation delays. Our findings provide evidence for the link between tissue structure and large-scale neural dynamics, supporting the development of biologically grounded models of signal propagation.

neuroscience↗

bioRxiv2025

Cross-modal quality transfer: enhancing MEG spatial resolution using BOLD-fMRI and Explainable machine learning.

Purpose:1Blood-oxygen-level-dependent functional MRI (BOLD-fMRI) and magnetoencephalog-raphy (MEG) offer complementary insights into brain function, with BOLD-fMRI providing high spatial resolution and MEG offering high temporal sensitivity. This study aimed to enhance MEGs spatial resolution through learning inter-modal relationships via data-driven fusion with BOLD-fMRI using explainable machine learning (xML). MethodsMEG and BOLD-fMRI data were collected for sixteen participants watching the same naturalistic visual stimulus. MEG signals were filtered into standard frequency bands and processed to match the haemodynamic response. Data were then decomposed using Tenso-rial Independent Component Analysis (TICA), yielding 250 MEG (25 per frequency band) and 30 BOLD-fMRI components. A set of Extreme Gradient Boosted tree (XGBoost) models were trained to predict MEG component activity from downsampled BOLD-fMRI components. The models with best-performing configuration of hyperparameters at this resolution were then used to generate voxelwise upsampled MEG maps using the natively higher-resolution BOLD-fMRI data. Performance was compared to trilinear interpolation using R{superscript 2}, mean squared error (MSE), and structural similarity index (SSI). Model inter-pretability was enhanced by generating Shapley values (SHAP), describing relationships between input data and model output. ResultsModels trained on 6mm3 data achieved high predictive performance on previously unseen data (R{superscript 2} = 0.80, MSE = .07). Using higher-resolution 2mm3 BOLD-fMRI data, MEG activ-ity was upsampled to 2mm3, resulting in more detailed image, while maintaining reasonable congruence to naive interpolation (R{superscript 2} = .81, MSE = .06). ConclusionsThis work demonstrates that explainable machine learning enables spatial super-resolution of MEG maps via BOLD-fMRI-informed quality transfer, enabling enhancement of spatial resolution, and offering improved localization, as well as means of generating data-driven insights into neurovascular coupling.

neuroscience↗

bioRxiv2025

Disentangling Rhythmic and Aperiodic Neural Activity: Investigating the Role of GABA Modulation in Sensorimotor Beta Oscillations

Beta oscillations (13-30 Hz) in the sensorimotor cortex are fundamental to motor control and are disrupted in motor disorders such as Parkinsons disease and stroke. Animal studies and pharmacological research in humans have implicated GABAergic mechanisms in modulating these oscillations. Here, we explored the impact of GABAergic modulation on sensorimotor beta oscillations using magnetoencephalography (MEG) during a finger abduction task, with participants administered gaboxadol and zolpidem, two GABA-A positive allosteric modulators. We combined traditional oscillatory analyses with spectral parametrisation methods to separate periodic and aperiodic neural components. Time-frequency representations (TFRs) showed that gaboxadol induced a stronger modulation of beta dynamics than zolpidem, leading to deeper beta desynchronisation during movement and a more pronounced post-movement beta rebound. When accounting for aperiodic activity, gaboxadol showed modest effects limited to movement initiation, with increased beta power during movement onset but no significant impact during post-movement rebound. In contrast, zolpidem significantly enhanced beta power and altered aperiodic components, suggesting differential influences on GABAergic modulation and excitation-inhibition balance. These results underscore the necessity of distinguishing between periodic and aperiodic signals in spectral measures to improve the interpretation of neural oscillatory data.

neuroscience↗

bioRxiv2025

Microbial metabolite 4-ethylphenylsulfate (4EPS) interacts with AT1R, reduces blood pressure and outcome of AngII-induced aortic aneurysm

BACKGROUNDPlasma accumulation of the gut microbial metabolite, 4-ethylphenylsulfate (4EPS), produced from dietary protein aromatic amino acids has been observed in correlative and associative studies of cardiovascular, renal, metabolic and neurological diseases. 4EPS level increases upon AngII infusion in mice. How 4EPS alters host physiology to contribute to progression of any disease state is currently unknown. METHODSTo test the hypothesis that 4EPS interferes with angiotensin binding to AT1R, we used multiple approaches: AT1R pharmacology, cell-signaling, ex vivo vascular contraction and a mouse model of angiotensin-induced aortic aneurysm (AA) disease. ApoE-null mice were fed high-fat diet and infused with AngII, or co-infused with 4EPS and Olmesartan. BP was recorded. At the end of infusion, aortas were assessed for severity of AA, contractile response and histopathology. To evaluate signaling associated with different AA outcomes plasma proteomics analysis was done. RESULTSIn vitro, 4EPS reduced the binding of angiotensin and Candesartan to AT1R and calcium signaling. Ex vivo, 4EPS decreased vasomotor response of the aorta to AngII. In vivo, 4EPS inhibited AngII-mediated increase of BP and reduced mortality from AA. Abdominal aorta remodeling in 4EPS+AngII co-infused mice showed an increase of elastin area and reduced thickening of intimal/medial layers. Plasma proteome analysis indicated significant change in actin-cytoskeletal signaling associated with reduced ERK1/2 and Filamin-A activation, and cell motility. CONCLUSIONSBenign antagonism of AT1R by 4EPS involves direct interaction with AT1R. Molecular mechanisms of 4EPS responsible for reduced AA associated mortality in mice are distinct from those of AT1R blocker, Olmesartan. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=123 HEIGHT=200 SRC="FIGDIR/small/635371v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1a33049org.highwire.dtl.DTLVardef@b73071org.highwire.dtl.DTLVardef@cd5557org.highwire.dtl.DTLVardef@9dc63e_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2024

Restoring Synaptic Balance in Schizophrenia: Insights from a thalamo-cortical conductance-based model.

The dysconnectivity hypothesis of schizophrenia suggests that atypical, aberrant neural communication underlies the disorders diverse symptoms. Building on this framework, our study introduces a novel approach to understanding schizophrenia and exploring potential ways to adjust neural activity through synaptic restoration. Using a combination of magnetoencephalography data and dynamic causal modeling, we identified specific synaptic disturbances in schizophrenia patients, including increased NMDA receptor-mediated excitation in superficial pyramidal neurons and reduced GABA-B receptor-mediated inhibition between interneurons and pyramidal cells. These findings reveal a critical imbalance in excitation and inhibition within thalamo-cortical circuits, manifesting as altered gamma and alpha oscillations. The cornerstone of our research is an in silico synaptic restoration analysis, which demonstrates that targeted modifications to AMPA, NMDA, GABA-A, and GABA-B receptor-mediated connections can recalibrate altered neural activity in schizophrenia, aligning it with healthy control patterns. This restoration approach not only highlights the complex nature of synaptic dysfunction in the disorder but also identifies specific pathways as potential therapeutic targets, offering new avenues for investigating schizophrenias diverse symptomatology.

neuroscience↗

bioRxiv2024

Deep-learning and analytical models give distinct results for the brain structure-function relationship in health and in psychosis

Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a comprehensive framework for mapping structural connectivity measured via diffusion-MRI to resting-state functional connectivity measured via magnetoencephalography, utilizing a deep-learning model based on a Graph Multi-Head Attention AutoEncoder. We compare the results to those from an analytical model that utilizes shortest-path-length and search-information communication mechanisms. The deep-learning model outperformed the analytical model in predicting functional connectivity in healthy participants at the individual level, achieving mean correlation coefficients higher than 0.8 in the alpha and beta frequency bands. Our results imply that human brain structural connectivity and electrophysiological functional connectivity are tightly coupled. The two models suggested distinct structure-function coupling in people with psychosis compared to healthy participants (p < 2 x 10-4 for the deep-learning model, p < 3 x 10-3 in the delta band for the analytical model). Importantly, the alterations in the structure-function relationship were much more pronounced than any structure-specific or function-specific alterations observed in the psychosis participants. The findings demonstrate that analytical algorithms effectively model communication between brain areas in psychosis patients within the delta and theta bands, whereas more sophisticated models are necessary to capture the dynamics in the alpha and beta band.

neuroscience↗

bioRxiv2024

Measuring the neurodevelopmental trajectory of excitatory-inhibitory balance via visual gamma oscillations

Disruption of the balance between excitatory and inhibitory neurotransmission (E-I balance) underlies theories of many neurodevelopmental disorders, however, its study is typically restricted to adults, animal models and the lab-bench. Neurophysiological oscillations in the gamma frequency band relate closely to E-I balance, and a new technology - OPM-MEG - offers the possibility to measure such signals across the lifespan. We used OPM-MEG to measure gamma oscillations induced by visual stimulation in >100 participants, aged 2-34 years. We demonstrate a significantly changing spectrum with age, with low amplitude broadband gamma oscillations in children and high amplitude band limited oscillations dominating in adults. We used a canonical cortical microcircuit to model these gamma signals, revealing significant age-related shifts in E-I balance in superficial pyramidal neurons in visual cortex. Our findings detail the first MEG metrics of gamma oscillations and their underlying generators from toddlerhood, providing a benchmark against which future studies can contextualise.

neuroscience↗

bioRxiv2024

Multimodal MEG and microstructure-MRI investigations of the human hippocampal scene network

Although several studies have demonstrated that perceptual discrimination of complex scenes relies on an extended hippocampal network, distinct from an anterotemporal network supporting the perceptual discrimination of faces, we currently have limited insight into the specific functional and structural properties of these networks. Here, combining electrophysiological (magnetoencephalography, MEG) and microstructural (multi-shell diffusion MRI, dMRI) imaging in healthy human adults (30 female/10 male), we show that both hippocampal theta power modulation and fibre restriction of the fornix (a major input/output pathway of the hippocampus) independently related to accuracy during scene, but not face, perceptual discrimination. Conversely, microstructural features of the inferior longitudinal fasciculus (a long-range occipito- anterotemporal tract) correlated with face, but not scene, perceptual discrimination accuracy. Our results provide new mechanistic insight into the neurocognitive systems underpinning complex scene and face perception, providing support for multiple-system representation-based accounts of the medial temporal lobe. Significance StatementIn contrast to theories positing segregated cortical areas for perception and memory, the specialized representations of the hippocampus may support both the perception and memory of visual scenes. To investigate, we utilised the unique window into hippocampal electrophysiological activity offered by Magnetoencephalography (MEG). We found hippocampal theta activity modulations in the hippocampus and posteromedial cortex during scene, versus face and shape-size, perceptual oddity discrimination, the magnitude of which correlated with scene, but not face or shape- size, discrimination accuracy. Moreover, multimodal white matter imaging revealed that tissue restriction of the fornix - the major hippocampal output tract - independently predicted scene discrimination performance. Our multimodal MEG-microstructure study provides novel evidence that the hippocampus and connected structures conjointly support online scene processing.

neuroscience↗

bioRxiv2023

Reliability of dynamic causal modelling of resting state magnetoencephalography

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magneto-electroencephalography under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting state magneto-electroencephalography (MEG) data from two sessions, acquired two weeks apart, from a cohort with high between-subject variance arising from Alzheimers disease. Our focus is not on the effect of disease, but on the predictive validity of the methods implicit in their reliability, which is crucial for future studies of disease progression and drug intervention. To assess the predictive validity 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 vs. without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the predictive validity of DCM parameters at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, 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 predictive validity and reliability 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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/562379v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@135c964org.highwire.dtl.DTLVardef@38e4a7org.highwire.dtl.DTLVardef@12bc358org.highwire.dtl.DTLVardef@25f110_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Altered brain criticality in Schizophrenia: New insights from MEG

Schizophrenia has a complex etiology and symptomatology that is difficult to untangle. After decades of research, important advancements towards a central biomarker are still lacking. One of the missing pieces is a better understanding of how non-linear neural dynamics are altered in this patient population. In this study, the resting-state neuromagnetic signals of schizophrenia patients and healthy controls were analyzed in the framework of criticality. When biological systems like the brain are in a state of criticality, they are thought to be functioning at maximum efficiency (e.g., optimal communication and storage of information) and with maximum adaptability to incoming information. Here, we assessed the self-similarity and multifractality of resting-state brain signals recorded with magnetoencephalography in patients with schizophrenia patients and in matched controls. Our analysis showed a clear ascending, rostral to caudal gradient of self-similarity values in healthy controls, and an opposite gradient for multifractality (descending values, rostral to caudal). Schizophrenia patients had similar, although attenuated, gradients of self-similarity and multifractality values. Statistical tests showed that patients had higher values of self-similarity than controls in fronto-temporal regions, indicative of more regularity and memory in the signal. In contrast, patients had less multifractality than controls in the parietal and occipital regions, indicative of less diverse singularities and reduced variability in the signal. In addition, supervised machine-learning, based on logistic regression, successfully discriminated the two groups using measures of self-similarity and multifractality as features. Our results provide new insights into the baseline cognitive functioning of schizophrenia patients by identifying key alterations of criticality properties in their resting-state brain data.

neuroscience↗

bioRxiv2021

Novel AT1R-Allosteric Ligands Mask the Preeclampsia Auto-antibody Epitope and Decrease Angiotensin-induced Vasoconstriction

Maternal blood pressure regulation by the hormone angiotensin II (AngII) sustains fetal growth through feto-placental circulation. AngII binding to orthosteric pocket in the angiotensin type 1 receptor (AT1R) induces G protein and {beta}-arrestin signaling. AT1R blocking drugs and {beta}-arrestin biased ligands also bind to the orthosteric pocket but evoke different inactive and active states1-6. AT1R-directed auto-antibodies observed in preeclampsia bound outside the orthosteric pocket to extracellular loop-2 (ECL2) of AT1R7-9. How auto-antibodies modulate AT1R activity causing preeclampsia pathogenesis is unknown. Here we report a druggable cryptic allosteric pocket encompassing the preeclampsia epitope on ECL2. Using structure based high-throughput small molecule screening we discovered 18 ligands specific for AT1Rs allosteric pocket. After procuring these ligands we validated inhibition of preeclampsia epitope-specific antibody binding. We characterize their inhibitory effect on antibody and AngII-signaling in cells and vasoconstriction ex vivo. These novel AT1R allosteric ligands, thus act as dual action negative modulators of auto-antibody action and vasoconstriction. Our study demonstrates that positive allosteric modulator action of auto-antibody causes a disease linked to AT1R. We anticipate our findings to kindle structure-based discovery of AT1R allosteric ligands for intervention in maladies such as preeclampsia7-10, rejection of organ transplants11, vasodilatory shock12, 13 and metabolic syndrome14.

pharmacology and toxicology↗

bioRxiv2020

Identifying genetic variants associated with cerebellar volume in 33,265 individuals from the UK-Biobank

There is expanding interest in researching the cerebellum given accumulating evidence of its important contributions to cognitive and emotional functions, in addition to more established sensorimotor roles. While large genome-wide association studies (GWAS) have shed light on the common allele architecture of cortical and subcortical brain structures, the cerebellum remains under investigated. We conducted a meta-GWAS of cerebellar volume in 33,265 UK-Biobank European participants. Results show cerebellar volume to be moderately heritable (h2SNP=50.6%). We identified 33 independent genome-wide associated SNPs with total cerebellar volume, with 6 of these SNPs mapped to protein-coding genes and 5 more shown to alter cerebellar gene expression. We highlight 21 unique candidate genes for follow-up analysis. Cerebellar volume showed significant genetic correlation with brainstem, pallidum and thalamus volumes, but no significant correlations with neuropsychiatric phenotypes. Our results provide important new knowledge of the genetic architecture of cerebellar volume and its relationship with other brain phenotypes.

neuroscience↗

bioRxiv2020

Predicting MEG resting-state functional connectivity using microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries at the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology for better understanding of functional mechanisms. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

neuroscience↗

bioRxiv2020

Electrophysiological receptor mapping of GABA-A receptors

The non-invasive study of cortical oscillations provides a window onto neuronal processing. Temporal correlation of these oscillations between distinct anatomical regions is considered a marker of functional connectedness. As the most abundant inhibitory neurotransmitter in the mammalian brain, {gamma}-aminobutyric acid (GABA) is thought to play a crucial role in shaping the frequency and amplitude of oscillations, which thereby suggests a role for GABA in shaping the topography of functional activity and connectivity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) through oral administration of the GABA transporter 1 (GAT1) blocker tiagabine (15 mg). We show that the spatial distribution of tiagabine-induced activity changes, across the brain, corresponds to group-average flumazenil PET maps of GABAA receptor distribution. In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy male individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo pill. Using leakage-corrected amplitude envelope correlations (AECs), we quantified the functional connectivity in discrete frequency bands across the whole brain, using the 90-region Automatic Anatomical Labelling atlas (AAL90), as well as quantifying the average oscillatory activity across the brain. Analysis of variance in connectivity using a drug-by-session (2x4) design revealed interaction effects, accompanied by main effects of drug and session. Post-hoc permutation testing of each post-drug recording against the respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, and across 1- 3- and 5- hour recordings following tiagabine, but not placebo. The same analysis applied to activity, across the brain, also revealed a significant interaction, with post-hoc permutation testing demonstrating significant increases in activity across frontal regions, coupled with reductions in activity in posterior regions, across the delta, theta, alpha and beta frequency bands. Crucially, we show that the spatial distribution of tiagabine-induced changes in oscillatory activity overlap significantly with group-averaged maps of the estimated distribution of GABAA receptors, derived from scaled flumazenil volume-of-distribution (FMZ-VT) PET, hence demonstrating a possible mechanistic link between GABA availability, GABAA receptor distribution, and low-frequency network oscillations. We therefore propose that electrophysiologically-derived maps of oscillatory connectivity and activity can be used as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging at the group level, providing direct measures of target engagement and pharmacodynamics.

neuroscience↗

bioRxiv2020

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 then compared these MRVE connectivity patterns to those derived using more standard amplitude-amplitude coupling measures, using methods designed to quantify the consistency of these patterns across participants. Using oscillatory amplitude envelope correlation (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 and 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. It was also found that average mid-to fine scale variability within each region (fS [~] 10-150Hz) negatively correlated with the regions overall connectivity strength with other brain areas, as measured by fine scale MRVE correlation (fS [~] 30-150Hz) and by alpha and beta band AEC. These findings suggest that local activity at frequencies fS {gtrsim} 10Hz becomes more regular when a region exhibits high levels of resting state connectivity.

neuroscience↗

bioRxiv2020

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

BackgroundUnderstanding the effects of anaesthetic drugs on cortical oscillations can help to elucidate the mechanistic link between receptor physiology and their clinical effects. Propofol produces divergent effects on visual cortical activity: increasing induced gamma-band responses (GBR) while decreasing stimulus-onset-evoked responses) 1. Dexmedetomidine, an 2-adrenergic agonist, differs from GABA-ergic sedatives both mechanistically and clinically as it allows easy arousability from deeper sedation with less cognitive side-effects. Here we use magnetoencephalography (MEG) to characterize and compare the effects of GABAergic (propofol) and non-GABA-ergic (dexmedetomidine) sedation, on visual and motor cortical oscillations. MethodsSixteen 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 visual and motor task. ResultsThe 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 M100 (27%) and M150 (52%) evoked responses, whilst dexmedetomidine had no effect on these. During the motor task, neither drug had any significant effect on motor GBR or movement related beta de-synchronisation (MRBD). However, dexmedetomidine increased (92%) post-movement beta synchronisation/rebound (PMBR) power while propofol reduced it (70%). ConclusionsDexmedetomidine and propofol, at equi-sedative doses, have contrasting effects on visual stimulus induced GBR, visual evoked responses and PMBR. These findings provide a mechanistic link between the known receptor physiology of these sedative drugs and their known clinical effects and may be used to explore mechanisms of other anaesthetic drugs on human consciousness.

neuroscience↗