Biology subjects

Greaves, M. D.

Publications and source records attributed to Greaves, M. D..

7 recordsLinked to original sources

bioRxiv2026

What Is a Generative Model? Definitions, Disagreements, and Evaluation in Human Neuroimaging

The term generative model is widely used in human neuroimaging; however, its meaning is often left implicit. Prompted by observations and discussions at the 2025 Organization for Human Brain Mapping (OHBM) Annual Meeting, we surveyed members of the neuroimaging community to examine how generative models are defined, used, and evaluated in practice. Responses revealed some agreement on functional criteria-- such as a models ability to simulate data--alongside marked disagreement about whether specific, widely used methods should be considered generative models. Evaluative priorities also varied across respondents, though out-of-sample generalization and interpretability were consistently emphasized. Rather than proposing a single definition, this perspective highlights the diversity of current usage and argues for greater clarity when the term is invoked.

neuroscience↗

bioRxiv2025

Depression reduces structurally informed network flexibility in premanifest Huntington's disease

1Background and objectivesThe extent to which structural connectivity constrains effective connectivity in both depression and neurodegenerative contexts remains poorly understood. In particular, the relationship between structural connectivity aberrations and effective dysconnectivity associated with depression in Huntingtins disease remains uncharacterized. Here, we applied a novel procedure that implements structural connectivity-informed spectral dynamic causal modelling to examine how structural connectivity shapes directed inter-regional influences in premanifest Huntingtons disease gene expansion carriers (HDGECs) with and without depression history. MethodsUsing spectral dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data from 98 premanifest HDGECs across default mode network and striatum (caudate and putamen). HDGECs were split into two groups based on either having a history of depression or not. Depression severity on both the Beck Depression Inventory, 2nd Edition (BDI-II) and Hospital Anxiety and Depression Scale, Depression Subscale (HADS-D) was used to measure clinically elevated depression symptoms. Leave-one-out cross-validation was implemented to test predictive validity. ResultsModel evidence substantially favored structurally informed over uninformed approaches across all participants. For HDGECs, having a history of depression was associated with reduced baseline variability in effective connectivity (decreased parameter), with particularly tight regularization of near-zero-valued structural connections toward zero effective connectivity values while leaving strongly connected pathways relatively unaffected. Effects converged on striatal self-connectivity and hippocampal-striatal pathways, with distinct patterns emerging between depression history groups. Notably, clinically elevated depression revealed differential connectivity signatures, with right caudate self-connectivity showing positive correlations with clinical cut-offs for HDGECs with and without depression history. In leave-one-out cross-validation, specific connections including DMN-to-striatum (BDI: r = -0.31, p = .002; HADS-D: r = -0.33, p = .001), right hippocampus-to-left caudate (BDI: r = -0.46, p < .001; HADS-D: r = -0.30, p = .002), and left caudate-to-left putamen (BDI: r = -0.48, p < .001; HADS-D: r = -0.30, p = .003) significantly predicted individual differences in depression severity scores. DiscussionTogether, these findings link reduced network flexibility to depression vulnerability in premanifest neurodegeneration, providing a mechanistic bridge between anatomical constraints, effective connectivity alterations, and clinical depression phenotypes.

neuroscience↗

bioRxiv2025

Context-dependent structurally informed effective connectivity under psilocybin

The extent to which anatomical connectivity constrains pharmacologically altered brain dynamics remains poorly understood. Here, we combined psilocybin administration with a structurally informed effective-connectivity model to examine how structural connectivity shapes directed inter-regional influences across experiential contexts. Using dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data acquired from a hippocampo-thalamo-cortical network during rest, guided meditation, music listening and movie viewing. Across contexts, psilocybin reorganized directed interactions while preserving structure-based scaling. Effects converged on efferents (outgoing influences) from the left hippocampus--a hub interfacing mnemonic and associative systems with the default-mode network and thalamus. Notably, the left-hippocampus-to-thalamus pathway showed a sign-reversed association with mystical-experience scores (downregulation during guided meditation and upregulation during music listening). In model-based leave-one-out cross-validation, left-hippocampal efferents predicted individual differences in mystical-experience intensity. A minimal model-free benchmark (hippocampal signal variability) also showed modest associations with mystical experience. Together, these findings link context-specific, structurally informed effective connectivity to individual differences in the acute psychedelic experience, providing a mechanistic bridge between anatomy, neurodynamics, and phenomenology.

neuroscience↗

bioRxiv2025

Spectral imprint of structural embedding in effective connectivity

Neural fluctuations exhibit rich spectral profiles that reflects both local dynamics and structural (or anatomical) embedding. Yet, standard models of resting-state effective connectivity neglect structural embedding and assume uniformity in the timescales of regions endogenous fluctuations. We introduce a chromatic dynamic causal model (DCM) in which structural valency (or degree) modulates the spectral color of endogenous fluctuations. Specifically, we assume a linear mapping between regional structural valency and the spectral exponent of scale-free auto-spectra. Simulations show this mapping can emerge as a generic consequence of structural embedding under minimal coupling in a non-equilibrium regime. We show chromatic DCM reliably recovers ground-truth parameters across network sizes and noise conditions, outperforming standard spectral DCM. Applied to empirical data, chromatic DCM reveals that valency-exponent mappings vary across a cortical hierarchy, and that its parameters are conserved across a homologous network in humans, macaques, marmosets, and mice. These findings advance a generative account of structure-function coupling and expand the repertoire of biophysical mechanisms available for inference in effective connectivity modeling.

neuroscience↗

bioRxiv2025

PsiConnect: A Multimodal Neuroimaging Study of Psilocybin-Induced Changes in Brain and Behaviour

PsiConnect is a large-scale neuroimaging study designed to investigate the neural and subjective effects of psilocybin using multimodal neuroimaging. It combines functional, structural, and diffusion-weighted MRI with EEG to examine brain activity in 62 participants before and after a 19 mg dose of psilocybin. The design includes resting-state scans and three naturalistic conditions: guided meditation, music listening, and movie watching. Half of the cohort underwent an 8-week meditation training program, enabling the exploration of interactions among meditation, psilocybin, and brain function. The fMRI data was obtained through multi-echo fMRI, which enhances the signal-to-noise ratio and reduces susceptibility artifacts, thereby improving the reliability of the analyses. A comprehensive battery of behavioural and self-report measures captured both acute and longitudinal cognitive and subjective effects, with follow-ups extending to one year post-administration. The large sample size, multimodal neuroimaging, diversity of contexts, and longitudinal behavioural follow-ups enable the study of psilocybin-induced changes in brain and behaviour with an unprecedented level of detail and reliability. Furthermore, the data is curated according to open science principles to ensure accessibility and interoperability with established neuroimaging processing pipelines. These factors make PsiConnect a valuable and highly reusable resource for researchers in cognitive and computational neuroscience.

neuroscience↗

bioRxiv2025

Psychedelics Align Brain Activity with Context

Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on context, we collected and comprehensively analysed the largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with functional MRI and EEG during rest and naturalistic stimuli (meditation, music, and movie), before and after ingesting 19 mg of psilocybin (functional MRI {approx}80 min post-dose; EEG {approx}150 min post-dose). Half the participants ranked the experience among the most meaningful of their lives. Under psilocybin, functional MRI and EEG signals recorded during eyes-closed conditions became similar to those recorded during an eyes-open condition. Global functional connectivity increased in associative regions and decreased in sensory areas. Using machine learning to represent neural activity as low-dimensional trajectories, we found that psilocybin reorganised these into structured, context-sensitive patterns of brain activity that reflected both experimental condition and the quality of subjective experience, revealing an organisation that was missed by time-averaged connectivity measures. Under psilocybin, brain networks that ordinarily segregate internal and external processing coherently integrated and aligned neural dynamics with context. This context-alignment manifested as distinct and cohesive neural trajectories in participants reporting positively felt self- and boundary-dissolving effects, corresponding to the felt experience of being part of the environment, which we refer to as embeddedness--the subjective experience of being continuous with, rather than separate from, the surrounding environment. The strength of this context-alignment was associated with next-day mindset change, bridging the neural, experiential, and therapeutic dimensions of the psychedelic state. These findings show that the organisation of brain activity covaries with the experiential coherence of the psychedelic state, and provide a systems-level framework for how context-sensitive brain dynamics link neurobiology to subjective experience and behavioural change.

neuroscience↗

bioRxiv2024

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

Neuronal communication relies on the anatomy of the brain, yet it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints for modeling effective connectivity. Here, we assess a hierarchical empirical Bayes model that builds on a well-established dynamic causal model by integrating structural connectivity into resting-state effective connectivity via priors. In silico analyses show that the model successfully recovers ground-truth effective connectivity and compares favorably with a popular alternative. Analyses of empirical data reveal that a positive, monotonic relationship between structural connectivity and the prior variance of group-level effective connectivity generalizes across sessions and samples. Finally, attesting to the models biological plausibility, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal- transmodal hierarchy. These findings underscore the value of integrating structural and effective connectivity to enhance understanding of functional integration, with implications for health and disease. Significance statementTo advance the understanding of how neuronal populations interact in vivo, it is essential to develop models that integrate neuroimaging modalities. Here, we show that integrating structural connectivity into dynamic causal modeling of resting-state effective connectivity substantially improves model evidence, yields reliable inferences, and demonstrates face and construct validity in silico. Furthermore, this integration reveals that structural connectivitys influence on effective connectivity varies along an established unimodal-transmodal cortical hierarchy. This finding provides the first evidence of a network-dependent modulation of the relationship between structural and effective connectivity in humans. Against the backdrop of sustained and widespread interest in dynamic causal modeling, this study highlights the added value of integrating structural connectivity-based constraints, offering a more biologically grounded account of brain dynamics.

neuroscience↗