Biology subjects

Rowe, E. G.

Publications and source records attributed to Rowe, E. G..

5 recordsLinked to original sources

bioRxiv2024

Computational modelling shows evidence in support of both sensory and frontal theories of consciousness

The role of the prefrontal cortex (PFC) in consciousness is hotly debated. Frontal theories argue that the PFC is necessary for consciousness, while sensory theories propose that consciousness arises from recurrent activity in the posterior cortex alone, with activity in the PFC resulting from the mere act of reporting. To resolve this dispute, we re-analysed an EEG dataset of 30 participants from a no-report inattentional blindness paradigm where faces are (un)consciously perceived. Dynamic causal modelling was used to estimate the effective connectivity between the key contended brain regions, the prefrontal and the posterior cortices. Then, a second-level parametric empirical Bayesian model was conducted to determine how connectivity was modulated by awareness and task-relevance. While an initial data-driven search could not corroborate neither sensory nor frontal theories of consciousness, a more directed hypothesis-driven analysis revealed strong evidence that both theories could explain the data, with a very slight preference for frontal theories. Specifically, a model with backward connections switched off within the posterior cortex explained awareness better (53%) than a model without backward connections from the PFC to sensory regions. Our findings provide some support for a subtle, yet crucial, contribution of the frontal cortex in consciousness, and highlight the need to revise current theories of consciousness.

neuroscience↗

bioRxiv2021

Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity

Anxiety can alter an individuals perception of their external sensory environment. Previous studies suggest that anxiety can increase the magnitude of neural responses to unexpected (or surprising) stimuli. Additionally, surprise responses are reported to be boosted during stable compared to volatile environments. Few studies, however, have examined how learning is impacted by both threat and volatility. To investigate these effects, we used threat-of-shock to transiently increase subjective anxiety in healthy adults during an auditory oddball task, in which the regularity could be stable or volatile, while undergoing functional Magnetic Resonance Imaging (fMRI) scanning. We then used Bayesian Model Selection (BMS) mapping to pinpoint the brain areas where different models of anxiety displayed the highest evidence. Behaviourally, we found that threat-of-shock eliminated the accuracy advantage conferred by environmental stability over volatility in the task at hand. Neurally, we found that threat-of-shock led to both attenuation and loss of volatility-attuning of neural activity evoked by surprising sounds across most subcortical and limbic brain regions including the thalamus, basal ganglia, claustrum, insula, anterior cingulate, hippocampal gyrus and also the superior temporal gyrus. Conversely, within two small clusters in the left medial frontal gyrus and extrastriate area, threat-of-shock boosted the neural activity (relative to the safe and volatile condition) to the levels observed during the safe and stable condition, while also inducing a loss of volatility-attuning. Taken together, our findings suggest that threat eliminates the learning advantage conferred by statistical stability compared to volatility. Thus, we propose that anxiety disrupts behavioural adaptation to environmental statistics, and that multiple subcortical and limbic regions are implicated in this process.

neuroscience↗

bioRxiv2019

Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors

Detecting changes in the environment is fundamental for our survival. According to predictive coding theory, detecting these irregularities relies both on incoming sensory information and our top-down prior expectations (or internal generative models) about the world. Prediction errors (PEs), detectable in event-related potentials (ERPs), occur when there is a mismatch between the sensory input and our internal model (i.e., a surprise event). Many changes occurring in our environment are irrelevant for survival and may remain unseen. Such changes, even if subtle, can nevertheless be detected by the brain without emerging into consciousness. What remains unclear is how these changes are processed in the brain at the network level. Here, we used a visual oddball paradigm, in which participants engaged in a central letter task during electroencephalographic (EEG) recordings while presented with task-irrelevant high- or low-coherence background, random-dot motion. Critically, once in a while, the direction of the dots changed. After the EEG session, we confirmed that changes in motion direction at high- and low-coherence were visible and invisible, respectively, using psychophysical measurements. ERP analyses revealed that changes in motion direction elicited PE regardless of the visibility, but with distinct spatiotemporal patterns. To understand these responses, we applied Dynamic Causal Modelling (DCM) to the EEG data. Bayesian Model Averaging showed visible PE relied on a release from adaptation (repetition suppression) within bilateral MT+, whereas invisible PE relied on adaptation at bilateral V1 (and left MT+). Furthermore, while feedforward upregulation was present for invisible PE, the visible change PE also included downregulation of feedback between right MT+ to V1. Our findings reveal a complex interplay of modulation in the generative network models underlying visible and invisible motion changes.

neuroscience↗

bioRxiv2018

Bayesian Model Selection Maps for group studies using M/EEG data

Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called Bayesian brains continuously create and update generative models of the world, inferring (hidden) causes from (sensory) consequences. Neuroimaging datasets enable the detailed investigation of such modelling and updating processes, and these datasets can themselves be analysed with Bayesian approaches. These offer methodological advantages over classical statistics. Specifically, any number of models can be compared, the models need not be nested, and the null model can be accepted (rather than only failing to be rejected as in frequentist inference). This methodological paper explains how to construct posterior probability maps (PPMs) for Bayesian Model Selection (BMS) at the group level using electroencephalography (EEG) or magnetoencephalography (MEG) data. The method has only recently been used for EEG data, after originally being developed and applied in the context of functional magnetic resonance imaging (fMRI) analysis. Here, we describe how this method can be adapted for EEG using the Statistical Parametric Mapping (SPM) software package for MATLAB. The method enables the comparison of an arbitrary number of hypotheses (or explanations for observed responses), at each and every voxel in the brain (source level) and/or in the scalp-time volume (scalp level), both within participants and at the group level. The method is illustrated here using mismatch negativity (MMN) data from a group of participants performing an audio-spatial oddball attention task. All data and code are provided in keeping with the Open Science movement. In so doing, we hope to enable others in the field of M/EEG to implement our methods so as to address their own questions of interest.

neuroscience↗

bioRxiv2018

Escher-FBA: A web application for interactive flux balance analysis

BackgroundFlux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works.\n\nResultsTo meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses.\n\nConclusionsWe designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba.

bioengineering↗