Biology subjects

Lawn, T.

Publications and source records attributed to Lawn, T..

4 recordsLinked to original sources

bioRxiv2026

Spatial collinearity constrains multivariate molecular-enriched network estimation

Analyses of neuroimaging data increasingly leverage the distribution of neurotransmitter receptors derived from Positron Emission Tomography (PET) to bridge the gap between micro- and macro-scale brain function. However, these receptor maps are highly spatially overlapping which can give rise to interpretive and analytical challenges. Here, we systematically investigate the impact of spatial collinearity among PET maps in the context of Receptor-Enriched Analysis of functional Connectivity by Targets (REACT), a method that uses receptor maps as spatial regressors to derive subject-level molecular-enriched functional connectivity networks. Exhaustive combinatorial analysis across 19 receptor and transporter maps showed that collinearity scales rapidly with the number of receptors modelled simultaneously, and that this was relatively stable across parcellation scales, reflecting the intrinsic organisation of neurotransmitter systems. Using test-retest fMRI data from the Human Connectome Project, we demonstrate that modelling greater numbers of receptors degrades the reliability of molecular-enriched networks derived from conventional multivariate REACT models, and that collinearity among receptor maps drives this degradation. An alternative univariate approach, in which each receptor is modelled independently, yielded more reliable networks and, when applied to a within-subjects study of LSD compared to placebo, better recovered the role of the 5HT-2A receptor in LSDs neural effects. These findings identify spatial collinearity as a fundamental constraint on multivariate molecular-enriched network estimation and support univariate modelling as a more robust default for this class of analysis.

neuroscience↗

bioRxiv2025

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual responses vary, underscoring the need for predictive tools to guide treatment selection. Here, we present graphTRIP (graph-based Treatment Response Interpretability and Prediction) - a geometric deep learning architecture that enables three advances: 1) accurate prediction of post-treatment depression severity using only pretreatment clinical and neuroimaging data; 2) identification of robust biomarkers; and 3) causal analysis of treatment effects and underlying mechanisms. Trained on data from a clinical trial comparing psilocybin and escitalopram (NCT03429075), graphTRIP achieves strong predictive accuracy (r = 0.72, p = 6.8 x10-8), and shows clear generalization to both an independent dataset and across brain atlases. The model identifies stronger functional connectivity within sensory networks as a robust predictor of poorer response across both treatments. In contrast, causal analysis implicates frontoparietal and default mode networks as key moderators of differential response, with stronger 5-HT1A- and 5-HT2A-related signalling in the frontoparietal network predicting escitalopram response but psilocybin resistance. Overall, this work advances precision medicine and biomarker discovery in depression.

neuroscience↗

bioRxiv2023

Normative Modelling of Molecular-based Functional Neurocircuits Captures Clinical Heterogeneity Transdiagnostically in Neuropsychiatric Patients

Clinical neuroscience principally aims to delineate the neurobiology underpinning the symptoms of various disorders, with the ultimate goal of developing mechanistically informed treatments for these conditions. This has been hindered by the complex hierarchical organisation of the brain and extreme heterogeneity of neuropsychiatric disorders. However, recent advances in multimodal analytic techniques - such as Receptor Enriched Analysis of Connectivity by Targets (REACT) - have allowed to integrate the functional dynamics seen in fMRI with the brains receptor landscape, providing novel trans-hierarchical insights. Similarly, normative modelling of brain features has allowed translational neuroscience to move beyond group average differences between patients and controls and characterise deviations from health at an individual level. Here, we bring these novel methods together for the first time in order to address these two longstanding translational barriers in clinical neuroscience. REACT was used create functional networks enriched with the main modulatory (noradrenaline, dopamine, serotonin, acetylcholine), inhibitory (GABA), and excitatory (glutamate) neurotransmitter systems in a large group of healthy participants [N=607]. Next, we generated normative models of these networks across the spectrum of healthy ageing and demonstrated that these capture deviations within and across patients with Schizophrenia, Bipolar-disorder, and ADHD [N=119]. Our results align with prior accounts of excitatory-inhibitory imbalance in schizophrenia and bipolar disorder, with the former also related to deviations within the cholinergic system. Our transdiagnostic analyses also emphasised the substantial overlap in symptoms and deviations across these disorders. Altogether, this work provides impetus for the development of novel biomarkers that characterise both molecular- and systems-level dysfunction at the individual level, helping facilitate the transition towards mechanistically targeted treatments. Significance statementHuman beings show enormous variability, with inter-individual differences spanning from neurotransmitters to networks. Understanding how these mechanisms interact across scales and produce heterogenous symptomatology within psychiatric disorders presents an enormous challenge. Here, we provide a novel analytic framework to overcome these barriers, combining molecular-enriched neuroimaging with normative modelling to examine neuropathology across scales at the individual level. Our results converge on prior neurobiological accounts of schizophrenia and bipolar disorder as well as the heterogeneity of ADHD. Moreover, we map symptomatology to molecular-enriched functional networks transdiagnostically across these disorders. By bridging the gap between dysfunctional brain networks and underlying neurotransmitter systems, these methods can facilitate the transition from one-size-fits-all approaches to personalized pharmacological interventions at the individual level.

neuroscience↗

bioRxiv2022

The Effects of Propofol Anaesthesia on Molecular-enriched Networks During Resting-state and Naturalistic Stimulation

Placing a patient in a state of anaesthesia is crucial for modern surgical practice. However, the mechanisms by which anaesthetic drugs, such as propofol, impart their effects on consciousness remain poorly understood. Propofol potentiates GABAergic transmission, which purportedly has direct actions on cortex as well as indirect actions via ascending neuromodulatory systems. Functional imaging studies to date have been limited in their ability to unravel how these effects on neurotransmission impact system-level dynamics of the brain. Here, we leveraged advances in multi-modal imaging, Receptor-Enriched Analysis of functional Connectivity by Targets (REACT), to investigate how different levels of propofol-induced sedation alters neurotransmission-related functional connectivity (FC), both at rest and when individuals are exposed to naturalistic auditory stimulation. Propofol increased GABA-A- and noradrenaline transporter-enriched FC within occipital and somatosensory regions respectively. Additionally, during auditory stimulation, the network related to the vesicular acetylcholine transporter showed reduced FC within the right superior temporal gyrus, regardless of level of anaesthesia, and a spatial configuration correlating with a broad range of meta-analytic measures of audition- and emotion-related cognition. In bringing together these micro- and macro-scale systems, we provide support for both direct GABAergic and indirect noradrenergic-related network changes under anaesthesia and describe a cognition-related reconfiguration of the cholinergic network, highlighting the utility of REACT to explore the molecular substrates consciousness and cognition.

neuroscience↗