Biology subjects

Murley, A. G.

Publications and source records attributed to Murley, A. G..

3 recordsLinked to original sources

bioRxiv2026

From group-level neurophysiological biomarkers to individual-level phenotyping to identify mechanisms of neurodegenerative disease

Precision medicine approaches require physiological effects to be informative when translated to the individual. Reliable group differences may have limited individual value when distributions overlap, mechanisms are heterogeneous, or patients differ in phenotype. A feature can differ between group means and still overlap heavily across individuals Moreover, combinations of physiological abnormalities may reveal subject-level structure that is not captured by any single marker at the group level. We evaluate the translation between group- and individual-level features derived from resting-state magnetoencephalography (MEG) using behavioural-variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP) as demonstrator conditions. These illnesses have distinct molecular pathology but share the pressing need for therapeutic development and have partially overlapping clinical and physiological features. We combine clinical phenotype, power spectral density (PSD), and latent network mechanisms inferred by dynamic causal modelling (DCM) to compare groups and to access their individuals mapping translations. We used classification as an exploratory probe of how group-defined physiological features from 74 healthy controls, 45 participants with bvFTD and 55 participants with PSP support individual assignment. Clinical scores showed strong individual-level translation, consistent with clinical diagnostic definitions (e.g, controls-vs-PSP accuracy 0.94; bvFTD-vs-PSP accuracy 0.83). MEG spectra feature classified individuals weakly (accuracy ~0.6), despite group level re-organization (e.g., reduced beta-band and increased fronto-insular alpha-phase/beta-amplitude coupling in bvFTD). Inferred biophysical parameters gave intermediate performance (e.g. PSP-vs-control accuracy 0.70; bvFTD-vs-PSP accuracy 0.74) while retaining mechanistic interpretability. Using this approach, experimental medicine studies of neurological disease can formally trade between mechanistic insight and classification performance, in using biomarkers that can account for individual heterogeneity within and between diagnostic groups.

bioengineering↗

bioRxiv2022

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical and empirical Bayesian framework for testing hypotheses about synaptic neurotransmission, based on the integration of ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals neurophysiological observations. At the second level, individuals 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare models of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. We perform cross-validation using split-sampling of the MEG dataset. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

neuroscience↗

bioRxiv2019

GABA-ergic dynamics in human frontotemporal networks confirmed by pharmaco-magnetoencephalography.

To bridge the gap between preclinical cellular models of disease and in vivo imaging of human cognitive network dynamics, there is a pressing need for informative biophysical models. Here we assess dynamic causal models (DCM) of cortical network responses, inverted to magnetoencephalographic observations during an auditory oddball roving paradigm in healthy adults. This paradigm induces robust perturbations that permeate frontotemporal networks, including an evoked mismatch negativity response and transiently induced oscillations. Here, we probe GABAergic influences of the networks using double-blind placebo-controlled randomised-crossover administration of the GABA re-uptake inhibitor, tiagabine (oral, 10mg) in healthy older adults. We demonstrate the facility of conductance-based neural mass mean-field models, incorporating local synaptic connectivity, to investigate laminar-specific and GABAergic mechanisms of the auditory response. The neuronal model accurately recapitulated the observed magnetoencephalographic data. Using parametric empirical Bayes for optimal model inversion across both sessions, we identify the effect of tiagabine on GABAergic modulation of deep pyramidal and interneuronal cell populations. Moreover, in keeping with the hierarchical coding of beliefs and sensory evidence, we found a transition of the main GABAergic drug effects from auditory cortex in standard trials to prefrontal cortex in deviant trials. The successful integration of pharmaco-magnetoencephalography with dynamic causal models of frontotemporal networks provides a potential platform on which to evaluate the effects of disease and pharmacological interventions.\n\nSignificance StatementUnderstanding human brain function and developing new treatments require good models of brain function. We tested a detailed generative model of cortical microcircuits that accurately reproduced human magnetoencephalography, to quantify network dynamics and connectivity in frontotemporal cortex. This approach correctly identified the effect of a test drug (tiagabine) on neuronal function (GABA-ergic dynamics), opening the way for psychopharmacological studies in health and disease with the mechanistic precision afforded by generative models of the brain.

neuroscience↗