A5073637763- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Psychedelics and Drug Studies
- Mental Health Research Topics
- Neurotransmitter Receptor Influence on Behavior
- Ecosystem dynamics and resilience
- Advanced MRI Techniques and Applications
- Complex Systems and Time Series Analysis
- Neural Networks and Applications
- Advanced Neuroimaging Techniques and Applications
- Chemical synthesis and alkaloids
- Genetic Neurodegenerative Diseases
- Photoreceptor and optogenetics research
- Olfactory and Sensory Function Studies
- Tryptophan and brain disorders
- Gene Regulatory Network Analysis
- Nicotinic Acetylcholine Receptors Study
- Blind Source Separation Techniques
- Complementary and Alternative Medicine Studies
- Heart Rate Variability and Autonomic Control
- stochastic dynamics and bifurcation
- EEG and Brain-Computer Interfaces
- Opinion Dynamics and Social Influence
- Statistical and numerical algorithms
- Bayesian Modeling and Causal Inference
Monash University
2021-2025
Australian Regenerative Medicine Institute
2024
University of Zurich
2020-2022
The University of Sydney
2019-2021
Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited this task, being a model-free measure that captures nonlinear and lagged dependencies between time series to infer minimal directed network model. Greedy have been proposed efficiently deal with high-dimensional datasets while avoiding redundant inferences capturing synergistic effects. However, multiple statistical comparisons may inflate false...
Wollstadt et al., (2019). IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. Journal Open Source Software, 4(34), 1081, https://doi.org/10.21105/joss.01081
Edge time series are increasingly used in brain imaging to study the node functional connectivity (nFC) dynamics at finest temporal resolution while avoiding sliding windows. Here, we lay mathematical foundations for edge-centric analysis of neuroimaging series, explaining why a few high-amplitude cofluctuations drive nFC across datasets. Our exposition also constitutes critique existing studies, showing that their main findings can be derived from under static null hypothesis disregards...
Classic psychedelic-induced ego dissolution involves a shift in the sense of self and blurring boundary between world. A similar phenomenon is identified psychopathology associated with balance anticorrelated activity default mode network, which directs attention inward, salience recruits dorsal network to direct outward.To test whether changes networks underlie peak effects lysergic acid diethylamide (LSD), we applied dynamic causal modeling infer effective connectivity resting-state...
Background Serotonergic psychedelics, such as psilocybin, alter perceptual and cognitive systems that are functionally integrated with the amygdala. These changes can cognition emotions hypothesised to contribute their therapeutic utility. However, neural mechanisms of subcortical altered by psychedelics not well understood. Methods We used functional MRI resting state images collected during a randomised, double-blinded, placebo-controlled clinical trial 24 healthy adults under 0.2mg/kg...
Abstract We present a didactic introduction to spectral dynamic causal modeling (DCM), Bayesian state-space approach used infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied variant for resting-state functional MRI analysis. Our aim explain its technical foundations an audience with limited expertise in and data Particular attention will be paid cross-spectral density, which distinctive feature of closely related connectivity, as...
Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting their properties requires models to reflect key underlying structural features; however, even a few spurious links can distort measures, challenging functional connectomes. We study extent which micro- macroscopic be by algorithms based on mutual information bivariate/multivariate transfer entropy. The validation is performed two macaque connectomes synthetic with various...
Abstract Visual alterations under classic psychedelics can include rich phenomenological accounts of eyes-closed imagery. Preclinical evidence suggests agonism the 5-HT2A receptor may reduce synaptic gain to produce psychedelic-induced However, this has not been investigated in humans. To infer directed connectivity changes visual underlying psychedelic imagery healthy adults, a double-blind, randomised, placebo-controlled, cross-over study was performed, and dynamic causal modelling applied...
Abstract Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on the context, we collected and comprehensively analysed largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with functional MRI EEG during rest naturalistic stimuli (meditation, music, visual), before after ingesting 19 mg of psilocybin. Half participants ranked experience among...
PsiConnect is a large-scale neuroimaging study designed to investigate the neural and subjective effects of psilocybin using multimodal neuroimaging. It combines functional, structural, diffusion-weighted MRI with EEG examine brain activity in 62 participants before after 19 mg dose psilocybin. The design includes resting-state scans three naturalistic conditions: guided meditation, music listening, movie watching. Half cohort underwent an 8-week meditation training program, enabling...
Interregional brain communication is mediated by the brain's physical wiring (i.e., structural connectivity). Yet, it remains unclear whether models describing directed, functional interactions between latent neuronal populations—effective connectivity—benefit from incorporating macroscale connectivity. Here, we assess a hierarchical empirical Bayes method: connectivity-based priors constrain inversion of group-level resting-state effective connectivity, using subject-level posteriors as...
Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity 12 or 48 of mice trained tactile discrimination With improving task performance, most shift their peak from time reward-related...
Transfer entropy (TE) is an established method for quantifying directed statistical dependencies in neuroimaging and complex systems datasets. The pairwise (or bivariate) TE from a source to target node network does not depend solely on the local source-target link weight, but wider structure that embedded in. This relationship studied using discrete-time linearly coupled Gaussian model, which allows us derive each topology. It shown analytically dependence weight only first approximation,...
Inferring linear dependence between time series is central to our understanding of natural and artificial systems. Unfortunately, the hypothesis tests that are used determine statistically significant directed or multivariate relationships from time-series data often yield spurious associations (Type I errors) omit causal II errors). This due autocorrelation present in analyzed series—a property ubiquitous across diverse applications, brain dynamics climate change. Here we show that, for...
We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), Bayesian state-space modelling approach used infer effective connectivity from non-invasive neuroimaging data. Spectral DCM is currently the most widely applied variant for resting-state functional MRI analysis. Our aim explain its technical foundations an audience with limited expertise in and data Particular attention will be paid cross-spectral density, which distinctive feature of closely related connectivity,...
Abstract Visual alterations under classic psychedelics can include rich phenomenological accounts of eyes-closed imagery. Preclinical evidence suggests agonism the 5-HT2A receptor may reduce synaptic gain to produce psychedelic-induced However, this has not been investigated in humans. To infer directed connectivity changes visual sensory underlying psychedelic imagery healthy adults, a double-blind, randomised, placebo-controlled, cross-over study was performed, and dynamic causal modelling...
Abstract Classic psychedelic-induced ego dissolution involves a shift in the sense of self and blurring boundary between world. A similar phenomenon is identified psychopathology associated to balance anticorrelated activity default mode network (DMN) – which directs attention inwards salience (SN) recruits dorsal (DAN) direct outward. To test whether change networks underlie peak effects LSD, we applied dynamic causal modeling infer effective connectivity resting state functional MRI scans...
Abstract Classic psychedelics alter sense of self and patterns self-related thought. These changes are hypothesised to underlie their therapeutic efficacy across internalising pathologies such as addiction depression. Using resting-state functional MRI images from a randomised, double blinded, placebo-controlled clinical trial 24 healthy adults under 0.215mg/kg psilocybin, we investigated how psilocybin modulates the effective connectivity between resting state networks amygdala that...
Abstract Depression is one of the most common and impactful features in premanifest Huntington’s disease (HD). increasingly being conceptualised as a dysconnection syndrome two large-scale networks surmised to contribute expression depressive symptoms HD are striatum default mode network. Existing neuroimaging studies limited relied on functional connectivity: an inherently undirected measure connectivity. Dynamic causal modelling allows testing neurobiologically plausible models...
Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions. How cross-regional interactions reorganize during learning remains elusive. We applied multi-fiber photometry to chronically record simultaneous activity of 12-48 mouse regions while mice learned a tactile discrimination task. found with most shifted their peak from reward-related action the reward-predicting stimulus. corroborated this finding functional connectivity estimation using...
Many functional magnetic resonance imaging (fMRI) studies rely on estimates of hierarchically organised brain networks whose segregation and integration reflect the dynamic transitions latent cognitive states. However, most existing methods for estimating community structure from both individual group-level analysis neglect variability between subjects lack validation. In this paper, we develop a new multilayer detection method based Bayesian block modelling. The can robustly detect weighted...