Biology subjects

Stevner, A.

Publications and source records attributed to Stevner, A..

8 recordsLinked to original sources

bioRxiv2024

Modelling of brain dynamics reveals reduced switching between brain states in insomnia disorder - a resting state fMRI study

Insomnia disorder is the most common sleep disorder, and neuroimaging research indicates that it is related to dysfunction in large-scale brain networks. Recently developed methods have enabled the investigation of the dynamic aspects of brain activity varying over time. In the present study, we used a novel data-driven approach to evaluate time-varying brain activity in adults with insomnia disorder compared to matched controls with no sleep problems. We acquired ten minutes resting state functional magnetic resonance images and T1-weighed images in all participants. We used Hidden Markov modelling for a data-driven definition of dynamic changes in whole-brain activity. The results showed that insomnia disorder is characterised by reduced switching rates between brain states. In line with the reduced switching, the HMM analyses suggested reduced prevalence of two whole-brain states - the default mode network and a fronto-parietal network - and an increase in just one brain state - a global activation state - in insomnia patients compared to controls. The findings suggest that insomnia disorder is characterised by less flexible transitions between brain states at wakeful rest, and thus highlight the importance of evaluating the spatiotemporal dynamics of brain activity to advance the understanding of the neural underpinnings of insomnia disorder.

neuroscience↗

bioRxiv2022

A potential marker for problematic mother-infant bonding revealed by magnetoencephalography study of first-time mothers listening to infant cries

Studies using magnetoencephalography (MEG) have identified the orbitofrontal cortex (OFC) to be an important early hub for a parental instinct in the brain. This complements the finding from functional magnetic resonance imaging (fMRI) studies linking reward, emotion regulation, empathy and mentalisation networks to the parental brain. Here, we used MEG in 43 first-time mothers listening to infant and adult cry vocalisations to investigate the link with mother-infant postpartum bonding scores and their level of sleep deprivation (assessed using both actigraphy and sleep logs). We found significant differences 800-1000ms after onset of infant compared to adult cries in source-reconstructed brain activity in areas previously linked to the parental brain. Importantly, mothers with weaker bonding scores showed decreased brain responses to infant cries in the auditory cortex, middle and superior temporal gyrus, OFC, hippocampal areas, supramarginal gyrus and inferior frontal gyrus at around 100-200ms after stimulus onset. In contrast, we did not find correlations with sleep deprivation scores. The significant changes in brain processing of an infants distress signals could be a novel marker of weaker infant bonding in new mothers and should be investigated in vulnerable populations.

neuroscience↗

bioRxiv2021

Data and model considerations for estimating time-varying functional connectivity in fMRI

Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the models ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the models ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model. HighlightsO_LITime-varying FC models sometimes fail to detect temporal changes in fMRI data C_LIO_LIBetween- and within-subject FC variability affect model stasis C_LIO_LIThe choice of parcellation affects model stasis in real fMRI data C_LIO_LIThe number of observations and free parameters critically affect model stasis Keywords: fMRI; time-varying FC; Hidden Markov Model (HMM); resting state C_LI

neuroscience↗

bioRxiv2021

Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a novel method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this novel intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with an average accuracy of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.

neuroscience↗

bioRxiv2020

Spatiotemporal brain dynamics during recognition of the music of Johann Sebastian Bach

Music is a non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of previously memorized J.S. Bachs musical patterns from prelude in C minor BWV 847 compared to novel patterns matched in terms of entropy and information content. Remarkably, the recognition of the memorized music ignited a widespread brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus and hippocampus. Furthermore, measures of both brain activity and functional connectivity presented an overall increase over time, following the evolution and unfolding of the memorized musical patterns. Specifically, while the auditory cortex responded mainly to the first tones of the patterns, the activity and synchronization of higher-order brain areas such as cingulate, frontal operculum, hippocampus and orbitofrontal cortex largely increased over time, arguably representing the key whole-brain mechanisms for conscious recognition of auditory patterns as predicted by the global neuronal workspace hypothesis. In conclusion, our study described the fine-grained whole-brain activity and functional connectivity dynamics responsible for processing and recognition of previously memorized music. Further, the study highlights how the use of musical patterns in combination with a wide array of analytical tools and neuroscientific measures spanning from decoding to fast neural phase synchronization can shed new light on meaningful, complex cognitive processes.

neuroscience↗

bioRxiv2020

The early adolescent brain on music: analysis of functional dynamics reveals engagement of orbitofrontal cortex reward system

Music listening plays a pivotal role for children and adolescents, yet surprisingly few neuroimaging studies have studied the underlying functional dynamics. We used functional magnetic resonance imaging to scan 17 preadolescents aged 10-11 years old while listening to music. We subsequently tracked the occurrence of functional brain networks over time by using a recent method that detects recurrent BOLD phase-locking states: the Leading Eigenvector Dynamics Analysis (LEiDA). In particular, we compared the probabilities of occurrence and switching profiles of different patterns of BOLD phase-locking between music and no music. Moreover, we used an adapted version of the Barcelona Music Reward Questionnaire (BMRQ) to measure the music reward sensitivity of the participants. Our results showed significantly increased occurrence of a BOLD phase-locking pattern during music listening compared to no music, characterized by a phase-shift in the BOLD signals of the medial orbitofrontal and ventromedial prefrontal cortices - a brain subsystem associated to reward processing - from the rest of the brain. Moreover, we observed a significantly higher probability of switching to this pattern while listening to music. We also found a positive correlation between the individual musical reward sensitivity and the tendency to switch to this reward state during music. Our findings highlight the involvement of a brain subsystem involved in hedonic processing during music listening in the early adolescent brain. These results offer novel insight into the neural underpinnings of musical reward in early adolescence and may help us to understand the importance of music at this delicate age.

neuroscience↗

bioRxiv2019

Reduced structural connectivity in Insomnia Disorder

Insomnia Disorder is the most prevalent sleep disorder and it involves both sleep difficulties and daytime complaints. The neural underpinnings of Insomnia Disorder are poorly understood. Existing neuroimaging studies are limited by their focus on local measures and specific regions of interests. To address this shortcoming, we applied a data-driven approach to assess differences in whole-brain structural connectivity between adults with Insomnia Disorder and matched controls without sleep complaints. We used diffusion tensor imaging and probabilistic tractography to assess whole-brain structural connectivity and examined group differences using Network-Based Statistics. The results revealed a significant difference in the structural connectivity of the two groups. Participants with Insomnia Disorder showed reduced connectivity in a subnetwork that was largely left lateralized, including mainly fronto-subcortical connections with the insula as a key region. By taking a whole-brain network perspective, our study succeeds at integrating previous inconsistent findings, and our results reveal that reduced structural connectivity of the left insula and the connections between frontal and subcortical regions are central neurobiological features of Insomnia Disorder. The importance of these areas for interoception, emotional processing, stress responses and the generation of slow wave sleep may help guide the development of neurobiology-based models of the highly prevalent condition of Insomnia Disorder.

neuroscience↗

bioRxiv2016

Single or Multi-Frequency Generators in On-going Brain Activity: a Mechanistic Whole-Brain Model following empirical MEG evidences

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.\n\nConsidering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

neuroscience↗