Linking fast and slow: The case for generative models

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Soundscape phenology: The effect of environmental and climatic factors on birds and insects in a subtropical woodland

Climate change and biodiversity loss are significant global environmental issues. However, to understand their impacts we need to know how fauna respond to environmental and climatic variation over time. In this study, remote sensing techniques (satellite imagery and passive acoustic recorders) were used to investigate the variation in biophony over different timescales, ranging from one day to one year, in a sub-tropical woodland in eastern Australia. The prominent sources of biophony were birds at dawn and during the day, nocturnal insects at dusk and during the night, and diurnal birds and insects (mainly cicadas) over the summer period of December, January, and February. While different environmental factors were found to be key drivers of phenological response in different faunal groups, temperature, humidity and the interactions between temperature, humidity, moon illumination and vegetation greenness were most important factors overall. Using observed temperatures relative to the historical mean for each day of the year, we evaluated the impact of higher-than-average temperatures on calling activity. We found that nocturnal insects call less frequently on days when the temperature was hotter than average in winter months (June, July, and August), and birds call less frequently in hot spring days (September, October, and November) meaning these groups can be susceptible to temperature increase as consequence, for example, of climate change. This study demonstrates how animal calling behaviour is affected by different environmental variables over different temporal scales. This study also demonstrates the utility of remote sensing techniques for assessing the impacts of climate change on biodiversity. It is highly recommended that monitoring schemes and impact assessments account for phenological changes and environmental variability, as these are complex and important processes shaping animal communities.

The reliability and psychometric structure of Multi-Scale Entropy measured from EEG signals at rest and during face and object recognition tasks

BACKGROUND

Multi-Scale Entropy (MSE) is a widely used marker of Brain Signal Complexity (BSC) at multiple temporal scales.

METHODOLOGICAL IMPROVEMENT

There is no systematic research addressing the psychometric quality and reliability of MSE. It is unknown how recording conditions of EEG signals affect individual differences in MSE. These gaps can be addressed by means of Structural Equation Modeling (SEM).

RESULTS

Based on a large sample of 210 young adults, we estimated measurement models for MSE derived from multiple epochs of EEG signal measured during resting state conditions with closed and open eyes, and during a visual task with multiple experimental manipulations. Factor reliability estimates, quantified by the McDonald's ω coefficient, are high at lower and acceptable at higher time scales. Above individual differences in signal entropy observed across all recording conditions, persons specifically differ with respect to their BSC in open eyes resting state condition as compared with closed eyes state, and in task processing state MSE as compared with resting state.

COMPARISON WITH EXISTING METHODS

By means of SEM, we decomposed individual differences in BSC into different factors depending on the recording condition of EEG signals. This goes beyond existing methods that aim at estimating average MSE differences across recording conditions, but do not address whether individual differences are additionally affected by the type of EEG recording condition.

CONCLUSION

Eyes closed and open and task conditions strongly influence individual differences in MSE. We provide recommendations for future studies aiming to address BSC using MSE as a neural marker of cognitive abilities.

Color contrast adaptation: fMRI fails to predict behavioral adaptation

fMRI-adaptation is a valuable tool for inferring the selectivity of neural responses. Here we use it in human color vision to test the selectivity of responses to S-cone opponent (blue-yellow), L/M-cone opponent (red-green), and achromatic (Ach) contrast across nine regions of interest in visual cortex. We measure psychophysical adaptation, using comparable stimuli to the fMRI-adaptation, and find significant selective adaptation for all three stimulus types, implying separable visual responses to each. For fMRI-adaptation, we find robust adaptation but, surprisingly, much less selectivity due to high levels of cross-stimulus adaptation in all conditions. For all BY and Ach test/adaptor pairs, selectivity is absent across all ROIs. For RG/Ach stimulus pairs, this paradigm has previously shown selectivity for RG in ventral areas and for Ach in dorsal areas. For chromatic stimulus pairs (RG/BY), we find a trend for selectivity in ventral areas. In conclusion, we find an overall lack of correspondence between BOLD and behavioral adaptation suggesting they reflect different aspects of the underlying neural processes. For example, raised cross-stimulus adaptation in fMRI may reflect adaptation of the broadly-tuned normalization pool. Finally, we also identify a longer-timescale adaptation (1h) in both BOLD and behavioral data. This is greater for chromatic than achromatic contrast. The longer-timescale BOLD effect was more evident in the higher ventral areas than in V1, consistent with increasing windows of temporal integration for higher-order areas.