Biology subjects

Penny, W.

Publications and source records attributed to Penny, W..

6 recordsLinked to original sources

bioRxiv2025

Testing for Interactions in Multivariate Data

Factorial designs are a mainstay of the scientific paradigm, allowing the effects of multiple experimental factors and their interactions to be efficiently studied within a single experiment. In brain imaging, however, multivariate data analyses commonly proceed using multivariate decoding and we argue that the standard "difference of accuracies" test is not a true test of interactions. To remedy this situation we propose an encoding method based on a Bayesian Multivariate Linear model which is ideally suited to the factorial analysis of such data. We show how it can be used to test for multivariate main effects and interactions using data from EEG studies of Reward Learning and Declarative Memory. This approach additionally allows for null hypotheses to be accepted and allows one to infer whether multivariate effects are driven by collections of univariate effects or voxel dependencies. We also propose that those wishing to pursue tests for interactions using decoding methods use an "accuracy of differences" test. Author summaryOur understanding of human brain function has been transformed by non-invasive imaging methods such as functional Magnetic Resonance Imaging and Electroencephalography. Statistical modelling has been central to this endeavour with foundational work employing a univariate encoding method in which multiple characteristics of participants behaviour (the stimuli they are exposed to, the decisions they make, the events they remember) are used to predict brain activity at a single point or voxel element (pixel/voxel) in a brain recording, and this process repeats for all voxels in an image. Subsequent work has used multivariate decoding methods which identify categorical behavioural variables from multivariate brain imaging data. Here we propose a new type of multivariate encoding approach in which a Bayesian multivariate linear model is used to predict multivariate image data from multivariate behavioural variables. This approach has three advantages (i) it can correctly test for interactions among experimental factors, (ii) we can quantify the experimental evidence in support of no experimental effects being present and (iii) we can make inferences about the nature of the multivariate effects.

neuroscience↗

bioRxiv2020

An Introduction to Thermodynamic Integration and Application to Dynamic Causal Models

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS. Author summaryWhen fitting computational models to data in the setting of Bayesian inference, a user has the choice between two broad classes of algorithms: variational inference and Monte Carlo simulation. While both methods have advantages and drawbacks, variational inference has become standard in the domain of modelling directed brain connectivity due to its computational efficiency, especially when the challenges to select between competing hypotheses that explain the observed data. By contrast, the high computational demand by Monte Carlo methods has so far prevented their widespread use for inference on brain connectivity, despite their capability to overcome some of the shortcomings of variational inference. In this paper, we introduce the user to thermodynamic integration (TI), a Monte Carlo method designed for model fitting and model selection. By covering its foundations and historical origins in statistical physics, we hope to convey an in-depth understanding of TI that goes beyond a purely technical treatment. In addition, we also provide examples for concrete applications, demonstrating that, given an efficient implementation and up-to-date hardware, the challenge of high computational demand can be overcome successfully.

neuroscience↗

bioRxiv2020

Multitask Learning over Shared Subspaces

This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the second task if it shared the same subspace as the first. Additionally, accuracy was positively correlated over subjects learning same-subspace tasks, and negatively correlated for those learning different-subspace tasks. These results were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning. Human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning. Author summaryHow does knowledge gained from previous experience affect learning of new tasks ? This question of "Transfer Learning" has been addressed by teachers, psychologists, and more recently by researchers in the fields of neural networks and machine learning. Leveraging constructs from machine learning, we designed pairs of learning tasks that either shared or did not share a common subspace. We compared the dynamics of transfer learning in humans with those of a multitask neural network model, finding that human performance was consistent with a minimal capacity variant of the model. Learning was boosted in the second task if the same subspace was shared between tasks. Additionally, accuracy between tasks was positively correlated but only when they shared the same subspace. Our results highlight the roles of subspaces, showing how they could act as a learning boost if shared, and be detrimental if not.

animal behavior and cognition↗

bioRxiv2019

Dynamics of cortical degeneration over a decade in Huntington's Disease

The neurodegenerative process is typically slowly progressive and complex. While simple models of neurodegeneration suggest that brain changes progress at a near constant rate, previous research shows regional variation within the temporal progression of atrophy, indicating that over the course of neurodegeneration, different regions may undergo changing rates of atrophy. Characterization of long-term dynamic brain changes in neurodegeneration requires both extensive longitudinal MRI datasets and an advanced modeling framework. Until recently, both of these elements were not available. Here, we implement a novel dynamic systems approach to infer patterns of regional progression spatially and temporally in a unique longitudinal dataset with up to seven annual individual brain scans per participant from 49 Huntingtons Disease (HD) gene-carriers. We map participant-and group-level trajectories of cortical atrophy in HD using a decade of data that encompasses motor symptom onset and, for the first time, show that neurodegenerative brain changes exhibit complex temporal dynamics of atrophy with substantial regional variation in progressive cortical atrophy. Some fronto-occipital cortical areas show an almost constant rate of atrophy, while medial-inferior temporal areas undergo only minor change. Interestingly, cortical sensory-motor areas were found to show a noticeable acceleration of atrophy following HD diagnosis. Furthermore, we establish links between individual atrophy and genetic markers of HD (CAG repeat length), as well as showing that cortical motor network changes predict subsequent decline in task-based motor performance, demonstrating face-validity of the model. Our findings highlight the complex pattern of dynamic cortical change occurring in HD that can help to resolve the biological underpinnings of HD progression.

neuroscience↗

bioRxiv2018

Neural signatures of detours, shortcuts and back-tracking during navigation

Central to the concept of the cognitive map is that it confers behavioural flexibility, allowing animals to take efficient detours, exploit shortcuts and realise the need to back-track rather than persevere on a poorly chosen route. The neural underpinnings of such naturalistic and flexible behaviour remain unclear. During fMRI we tested human subjects on their ability to navigate to a set of goal locations in a virtual desert island riven by lava, which occasionally shifted to block selected paths (necessitating detours) or receded to open new paths (affording shortcuts). We found that during self-initiated back-tracking, activity increased in frontal regions and the dorsal anterior cingulate cortex, while activity in regions associated with the core default-mode network was suppressed. Detours activated a network of frontal regions compared to shortcuts. Activity in right dorsolateral prefrontal cortex specifically increased when participants encountered new plausible shortcuts but which in fact added to the path (false shortcuts). These results help inform current models as to how the brain supports navigation and planning in dynamic environments.\n\nSignificance StatementAdaptation to change is important for survival. Although real-world spatial environments are prone to continual change, little is known about how the brain supports navigation in dynamic environments where flexible adjustments to route plans are needed. Here, we used fMRI to examine the brain activity elicited when humans took forced detours, identified shortcuts and spontaneously back-tracked along their recent path. Both externally and internally generated changes in the route activated the fronto-parietal attention network, whereas only internally generated changes generated increased activity in the dorsal anterior cingulate cortex with a concomitant disengagement in regions associated with the default-mode network. The results provide new insights into how the brain plans and re-plans in the face of a changing environment.

neuroscience↗

bioRxiv2018

Spotting the path that leads nowhere: Modulation of human theta and alpha oscillations induced by trajectory changes during navigation

The capacity to take efficient detours and exploit novel shortcuts during navigation is thought to be supported by a cognitive map of the environment. Despite advances in understanding the neural basis of the cognitive map, little is known about the neural dynamics associated with detours and shortcuts. Here, we recorded magnetoencephalography from humans as they navigated a virtual desert island riven by shifting lava flows. The task probed their ability to take efficient detours and shortcuts to remembered goals. We report modulation in event-related fields and theta power as participants identified real shortcuts and differentiated these from false shortcuts that led along suboptimal paths. Additionally, we found that a decrease in alpha power preceded back-tracking where participants spontaneously turned back along a previous path. These findings help advance our understanding of the fine-grained temporal dynamics of human brain activity during navigation and support the development of models of brain networks that support navigation.

neuroscience↗