Biology subjects

Haydock, D.

Publications and source records attributed to Haydock, D..

3 recordsLinked to original sources

bioRxiv2026

AutoNeuro: An Open-Source fMRI Toolbox for Real-Time Neuroadaptive Task Design

Real-time functional magnetic resonance imaging (fMRI) offers a powerful means of studying brain function adaptively, enabling experimental parameters to be updated dynamically in response to ongoing neural activity. However, current approaches remain limited by complex infrastructure requirements, bespoke implementations, and a lack of flexible frameworks for closed-loop neuroimaging, with many primarily focusing on neurofeedback experimental designs. Here we present AutoNeuro, an open-source framework for real-time fMRI acquisition, preprocessing, feature extraction, and adaptive experimental control. AutoNeuro connects directly to the MRI scanner, receiving reconstructed slices as soon as they become available, and streams them into a modular analysis pipeline designed for low-latency processing. Neural features are estimated at the temporal resolution of acquisition and are passed to a Bayesian optimisation agent that selects task conditions to maximise a user-defined objective function. Experimental conditions are represented within a bounded "experiment space", allowing heterogeneous conditions to be explored within a common coordinate system. We demonstrate AutoNeuro in a real-time fMRI experiment in which the system adaptively sampled task conditions to obtain a continuous map of brain response to the range of conditions contained within the experimental space. The system operated within the temporal constraints of real-time preprocessing and analysis, maintaining stable model estimates across iterations, converging on experimental conditions most relevant to the measured brain metric. These results establish AutoNeuro as a flexible platform for closed-loop neuroimaging, supporting hypothesis-driven optimisation as well as exploratory mapping of brain metrics across large experimental spaces.

neuroscience↗

bioRxiv2025

Auditory Cortical Gradients Integrate Bottom-Up and Top-Down Structure During Natural Sound Categorisation

Understanding how the brain organises natural categories is a central challenge in neuroscience. While prior work has shown that categories can be decoded from distributed activity patterns in auditory cortex, it remains unclear how these categories are globally arranged relative to one another, and how low-level acoustic and higher-level semantic structure jointly shape this organisation. Here, we addressed these questions by deriving low-dimensional functional gradients from high-depth functional magnetic resonance imaging (fMRI) data (three participants, [~]4.7 hours each) acquired during a category-specific one-back task. These gradients captured the principal axes of population activity in auditory cortex. Gradient-based models of the auditory cortex explained category structure more accurately than region-of-interest or whole-brain approaches, revealing that category information is distributed across multiple continuous axes rather than aligned with any single organisational dimension. Projecting acoustic (gammatone filter-bank) and behavioural similarity spaces directly into a shared framework with the fMRI functional axes showed that both contribute to the brains category geometry, with acoustic structure exerting a somewhat stronger influence. However, representational relationships varied across category pairs: some reflected primarily acoustic similarity, others semantic distinctions, and many a combination of both. This pairwise heterogeneity shows how auditory cortex may integrate multiple representational dimensions that define higher-level categories. Significance StatementCategorising natural sounds requires the brain to transform diverse acoustic signals into meaningful concepts. How this is achieved remains unclear: prior work has shown distributed activation patterns in auditory cortex, but not how category relationships are organised within its functional architecture. We show that natural sound categories are embedded across continuous cortical gradients that integrate bottom-up acoustic structure with top-down semantic information. Different categories emerge as flexible combinations of these spectral and behavioural dimensions. These findings reveal that natural sound categorisation arises from the geometry of cortical organisation, moving beyond localist accounts and offering a new framework for understanding how perception and cognition are linked in the human auditory system.

neuroscience↗

bioRxiv2024

The spatial layout of antagonistic brain regions are explicable based on geometric principles

Brain activity emerges in a dynamic landscape of regional increases and decreases that span the cortex. Increases in activity during a cognitive task are often assumed to reflect the processing of task-relevant information, while reductions can be interpreted as suppression of irrelevant activity to facilitate task goals. Here, we explore the relationship between task-induced increases and decreases in activity from a geometric perspective. Using a technique known as kriging, developed in earth sciences, we examined whether the spatial organisation of brain regions showing positive activity could be predicted based on the spatial layout of regions showing activity decreases (and vice versa). Consistent with this hypothesis we established the spatial distribution of regions showing reductions in activity could predict (i) regions showing task-relevant increases in activity in both groups of humans and single individuals; (ii) patterns of neural activity captured by calcium imaging in mice; and, (iii) showed a high degree of generalisability across task contexts. Our analysis, therefore, establishes that antagonistic relationships between brain regions are topographically determined, a spatial analog for the well documented anti-correlation between brain systems over time. Significance StatementIt is well documented that brain activity changes in response to the demands of different situations, although what gives rise to the observed cortical activity patterns remains poorly understood. Using analytic tools from earth sciences, we examined whether the landscape of regional changes in activity emerge from a set of common topographical causes. Using only regions showing decreases in activity, we could predict the landscape of regions showing increases in activity using fMRI in humans and calcium imaging in mice. Our results suggest topographical principles determine the landscape of peaks and valleys in brain activity -- a spatial analog for the well documented anti-correlation between sets of brain regions over time.

neuroscience↗