Biology subjects

Gross, J.

Publications and source records attributed to Gross, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

EEG-based analysis of intrinsic brain network function in chronic pain: Insights from a comprehensive multi-data set study

Chronic pain is associated with alterations in brain function. A better understanding of these alterations might help to develop new approaches for the diagnosis, prediction, and treatment of chronic pain. Here, we analyzed associations between chronic pain and alterations of intrinsic brain network function using resting-state electroencephalography. We included data from 537 people with chronic pain obtained from various research groups worldwide. We found strong evidence for associations between pain intensity and intrinsic brain network connectivity, but the replicability of these associations in independent data was mostly low. However, a mega-analysis revealed associations of chronic pain with salience-somatomotor network connectivity at theta frequencies and with more complex patterns of intrinsic brain network connectivity. These findings provide novel insights into brain network function in chronic pain. Moreover, they highlight the need for collaborative multi-center studies, which can be guided by the present approach to promote replicability and consistency of findings.

neuroscience↗

bioRxiv2024

Assessing the balance between excitation and inhibition in chronic pain through the aperiodic component of EEG

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

neuroscience↗

bioRxiv2024

Neurophysiological correlates of cortical hierarchy across the lifespan

The brain processes information along a hierarchical structure, forming a gradient of cortical hierarchy from sensorimotor areas to transmodal areas. Here, we aim to understand which aspects of neural dynamics characterize this gradient and whether the respective spatial distribution varies across the lifespan. Therefore, we extracted neurophysiological features from magnetoencephalography recordings in 350 participants between 18 and 88 years during rest. Among traditional features related to the power spectrum, delta power (1-4 Hz) showed the most robust association with cortical hierarchy, increasing along this axis. Beyond traditional features, we employed comprehensive time-series characterization and identified a novel hierarchy-sensitive feature capturing the variability of the signals mean over time. This feature increases along the cortical hierarchy, suggesting that higher-level brain areas exhibit more dynamic and context-dependent activity patterns. Furthermore, we highlight changes in the gradient of brain dynamics across the lifespan. Alpha power distribution, for instance, exhibits a posterior-anterior gradient in young adults that becomes less pronounced with increasing age. Further, the change of the autocorrelation and auto mutual information function along the cortical hierarchy is heavily modulated by age. These findings reveal simple but robust neurophysiological markers of cortical hierarchy and highlight the dynamic nature of the brains organization throughout life.

neuroscience↗

bioRxiv2024

Assessing the predictive value of peak alpha frequency for the sensitivity to pain

Pain perception varies considerably between and within individuals. How the brain determines these variations has yet to be fully understood. The peak frequency of alpha oscillations (PAF) has recently been shown to predict an individuals sensitivity to longer-lasting experimental and clinical pain. PAF is, thus, discussed as a potential biomarker and novel target for neuromodulatory treatments of pain. Here, we scrutinized the generalizability of the relation between PAF and pain. We applied brief painful laser stimuli to 159 healthy participants and related inter- and intra-individual variations of pain perception to PAF measured with electroencephalography. Comprehensive multiverse analyses across two sessions did not provide consistent evidence for a predictive role of PAF for brief experimental pain. This indicates that the relationship between PAF and pain does not generalize to all types of pain and calls for a systematic exploration of the relationship between PAF, pain perception, and other neuropsychiatric symptoms.

neuroscience↗

bioRxiv2024

Extensive MEG time-series phenotyping unveils neural markers predictive of age

Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography in 350 adults (18-88 years). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred novel time-series features proved superior. Notably, temporal autocorrelation emerged as the most robust predictor of age. Distinct patterns of autocorrelation within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.

neuroscience↗

bioRxiv2024

Beyond oscillations - A novel feature space for characterizing brain states

Our moment-to-moment conscious experience is paced by transitions between states, each one corresponding to a change in the electromagnetic brain activity. One consolidated analytical choice is to characterize these changes in the frequency domain, such that the transition from one state to the other corresponds to a difference in the strength of oscillatory power, often in pre-defined, theory-driven frequency bands of interest. Today, the huge leap in available computational power allows us to explore new ways to characterize electromagnetic brain activity and its changes. Here we leveraged an innovative set of features on an MEG dataset with 29 human participants, to test how these features described some of those state transitions known to elicit prominent changes in the frequency spectrum, such as eyes-closed vs eyes-open resting-state or the occurrence of visual stimulation. We then compared the informativeness of multiple sets of features by submitting them to a multivariate classifier (SVM). We found that the new features outperformed traditional ones in generalizing states classification across participants. Moreover, some of these new features yielded systematically better decoding accuracy than the power in canonical frequency bands that has been often considered a landmark in defining these state changes. Critically, we replicated these findings, after pre-registration, in an independent EEG dataset (N=210). In conclusion, the present work highlights the importance of a full characterization of the state changes in the electromagnetic brain activity, which takes into account also other dimensions of the signal on top of its description in theory-driven frequency bands of interest.

neuroscience↗

bioRxiv2024

Frequency-specific cortico-subcortical interaction in continuous speaking and listening

Speech production and perception involve complex neural dynamics in the human brain. Using magnetoencephalography (MEG), our study explores the interaction between cortico-cortical and cortico-subcortical connectivities during these processes. Our connectivity findings during speaking revealed a significant connection from the right cerebellum to the left temporal areas in low frequencies, which displayed an opposite trend in high frequencies. Notably, high-frequency connectivity was absent during the listening condition. These findings underscore the vital roles of cortico-cortical and cortico-subcortical connections within the speech production and perception network. The results of our new study enhance our understanding of the complex dynamics of brain connectivity during speech processes, emphasizing the distinct frequency-based interactions between various brain regions.

neuroscience↗

bioRxiv2024

Entrainment Echoes in the Cerebellum

Historically, researchers have considered the cerebellum a coordinator of motor programs that ensures precise timing of movements and their adaptation to external events1. However, it has become increasingly clear that this role is not restricted to the motor system2. Rather, the cerebellum seems to play an important role in temporal prediction in general, as shown in its involvement in multiple functions that rely on precise event timing3. Although previous work suggested that the cerebellum exclusively predicts the interval between two events4, rather than tracking a global rhythm, it is also active when a rhythmic stimulus changes in rate5. The latter finding is in line with a cerebellar role in speech processing6 that entails frequent rate changes7. Neural mechanisms underlying the cerebellums involvement in speech processing, however, remain poorly understood. Moreover, there is a lack of studies contrasting speech and non-speech stimuli to establish speech-specificity of the observed effects8. In a re-analysis of magnetoencephalography (MEG) data9, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these "entrained" responses persist, and how they interact with other brain regions, when the rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced rhythmic responses in the cerebellum that outlasted the stimulus. During this "entrainment echo", but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus (IFG), and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents unprecedented evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions.

neuroscience↗

bioRxiv2024

Dissociating endogenous and exogenous delta activity during natural speech comprehension

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech are prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher frequency auditory activity at speech onsets (around 25 - 45 Hz). Critically, we also find that multi-word chunks--defined as short, coherent bundles of inter-word dependencies--are processed through the rhythmic fluctuations of low frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas, while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity.

neuroscience↗

bioRxiv2024

Optimal trade-off between boosted tolerance and growth fitness during adaptive evolution of yeast to ethanol shocks

The design of selection protocols to obtain bioethanol yeasts with higher alcohol tolerance poses the challenge of improving industrial strains that are already robust to high ethanol levels. Furthermore, yeasts subjected to mutagenesis and selection, or laboratory evolution, often present adaptation trade-offs wherein higher stress tolerance is attained at the expense of growth and fermentation performance. We conducted an adaptive laboratory evolution by challenging four populations (P1-P4) of the Brazilian bioethanol yeast, Saccharomyces cerevisiae PE-2_H4, through 68-82 cycles of 2-h ethanol shocks (19%-30% v/v) and outgrowths. Colonies isolated from the final populations (P1c-P4c) were subjected to whole-genome sequencing, revealing mutations in genes enriched for the cAMP/PKA and trehalose degradation pathways. Fitness analyses of the isolated clones P1c-P3c and reverse-engineered strains demonstrated that mutations were primarily selected for cell viability under ethanol stress, at the cost of decreased growth rates in cultures with or without ethanol. Under this selection regime for stress survival, the population P4 evolved a protective snowflake phenotype resulting from BUD3 disruption. Despite marked adaptation trade-offs, the combination of reverse-engineered mutations cyr1A1474T/usv1{Delta} conferred 5.46% higher fitness than the parental PE-2_H4 for propagation in 8% (v/v) ethanol, with only a 1.07% fitness cost in a culture medium without alcohol. The cyr1A1474T/usv1{Delta} strain and evolved P1c displayed robust fermentations of sugarcane molasses using cell recycling and sulfuric acid treatments, mimicking Brazilian bioethanol production. These results demonstrate that some alleles selected for acute stress survival may further confer stress tolerance and optimal performance under industrial conditions.

evolutionary biology↗

bioRxiv2023

Sequence reliance of a Drosophila context-dependent transcription factor

Despite binding similar cis elements in multiple locations, a single transcription factor often performs context-dependent functions at different loci. How factors integrate cis sequence and genomic context is still poorly understood and has implications for off-target effects in genetic engineering. The Drosophila context-dependent transcription factor CLAMP targets similar GA-rich cis elements on the X-chromosome and at the histone gene locus but recruits very different, loci-specific factors. We discover that CLAMP leverages information from both cis element and local sequence to perform context-specific functions. Our observations imply the importance of other cues, including protein-protein interactions and the presence of additional cofactors.

genetics↗

bioRxiv2023

A dynamic link between respiration and arousal

Viewing brain function through the lense of other physiological processes has critically added to our understanding of human cognition. Further advances though may need a closer look at the interactions between these physiological processes themselves. Here we characterise the interplay of the highly periodic, and metabolically vital respiratory process and fluctuations in arousal neuromodulation, a process classically seen as non-periodic. In data of three experiments (N = 56 / 27 / 25 women and men) we tested for covariations in respiratory and pupil size (arousal) dynamics. After substantiating a robust coupling in the largest dataset, we further show that coupling strength decreases during task performance compared with rest, and that it mirrors a decreased respiratory rate when participants take deeper breaths. Taken together, these findings suggest a stronger link between respiratory and arousal processes than previously thought. Moreover, these links imply a stronger coupling during periods of rest, and the effect of respiratory rate on the coupling suggests a driving role. As a consequence, studying the role of neuromodulatory arousal on cortical function may also need to consider respiratory influences. Significance statementWe characterise the interplay of the respiratory rhythm and pupil diameter dynamics as a well-known proxy for arousal. Although we consistently find respiratory modulation of pupillary changes, they were most pronounced during periods of rest (compared to during task performance) and dependent on respiratory rate (deep vs. normal breathing).

neuroscience↗

bioRxiv2023

Respiratory modulations of cortical excitabilityand interictal spike timing in focal epilepsy - a case report

Brain activity in focal epilepsy is marked by a pronounced excitation-inhibition (E:I) imbalance and interictal epileptiform discharges (IEDs) observed in periods between recurrent seizures. As a marker of E:I balance, aperiodic neural activity and its underlying 1/f characteristic reflect the dynamic interplay of excitatory and inhibitory currents. Recent studies have independently assessed 1/f changes both in epilepsy and in the context of body-brain interactions in neurotypical individuals where the respiratory rhythm has emerged as a potential modulator of excitability states in the brain. Here, we investigate respiration phase-locked modulations of E:I balance and their involvement in the timing of spike discharges in a case report of a 25 year-old focal epilepsy patient using magnetoencephalography (MEG). We show that i) respiration differentially modulates E:I balance in focal epilepsy compared to N = 40 neurotypical controls and ii) IED timing depends on both excitability and respiratory states. These findings overall suggest an intricate interplay of respiration phase-locked changes in excitation and the consequential susceptibility for IED generation and we hope they will spark interest in subsequent work on body-brain coupling and E:I balance in epilepsy.

neuroscience↗

bioRxiv2023

Pseudomonas Putida Dynamics of Adaptation under Prolonged Resource Exhaustion

Many non-sporulating Bacterial species survive prolonged resource exhaustion, by entering a state termed long-term stationary phase (LTSP). Here, we performed LTSP evolutionary experiments on the bacterium Pseudomonas putida, followed by whole genome sequencing of evolved clones. We show that P. putida is able to persist and adapt genetically under LTSP. We observed a gradual accumulation of mutations within the evolving P. putida populations. Within each population, independently evolving lineages are established early on and persist throughout the four-month-long experiment. Mutations accumulate in a highly convergent manner, with similar loci being mutated across independently evolving populations. Across populations, mutators emerge, that due to mutations within mismatch repair genes developed a much higher rate of mutation than other clones with which they co-existed within their respective populations. While these general dynamics of the adaptive process are quite similar to those we previously observed in the model bacterium Escherichia coli, the specific loci that are involved in adaptation only partially overlap between P. putida and E. coli. Significance statementBacteria often face conditions of prolonged nutrient limitation, following periods of growth. One strategy for dealing with this challenge is entry into a state termed long-term stationary phase (LTSP), in which a small minority of cells within a population can survive and persist, by recycling the remains of their deceased brethren. Here, we broaden our understanding of adaptation under LTSP, by studying it in the bacterium Pseudomonas putida. We show that many of the dynamics of LTSP genetic adaptation are quite general, as reflected by great similarity to what was previously observed in the model bacterium Escherichia coli. However, the specific loci that are involved in adaptation substantially vary between P. putida and E. coli.

evolutionary biology↗

bioRxiv2023

Towards a more robust non-invasive assessment of functional connectivity

Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of third party noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.

neuroscience↗

bioRxiv2023

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher- dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets, the LEMON dataset, containing 213 EEG recordings of healthy participants, and the TDBRAIN dataset, containing 1274 EEG recordings, mainly from patients with a psychiatric condition. Additionally, we performed an exploratory analysis of the LEMON dataset that could inspire biomarkers of healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

neuroscience↗

bioRxiv2022

The Timing of Cortical Activation in Associator Grapheme-Colour Synaesthetes using MEG

Grapheme-colour synaesthetes experience an anomalous form of perception in which graphemes systematically induce specific colour concurrents in their minds eye ("associator" type). Although grapheme-colour synaesthesia has been well characterized behaviourally, its neural mechanisms remain largely unresolved. There are currently several competing models, which can primarily be distinguished according to the anatomical and temporal predictions of synaesthesia-inducing neural activity. The first main model (Cross-Activation/ Cascaded Cross-Tuning) posits early recruitment of occipital colour areas in the initial feed-forward sweep of brain activity. The second (Disinhibited Feedback) posits: (i) later involvement of a multisensory convergence zone (for example, in parietal cortices) after graphemes have been processed in their entirety; and (ii) subsequent feedback to early visual areas (i.e., occipital colour areas). In this study, we examine both the timing and anatomical correlates of associator grapheme-colour synaesthetes (n=6) using MEG. Using unbiased analysis methods with little a priori assumptions, we applied Independent Component Analysis (ICA) on a single-subject level to identify the dominant patterns of the induced, synaesthetic percept. We observed evoked activity that significantly dissociates between synaesthesia-inducing and non-inducing conditions at approximately 190 ms following grapheme presentation. This effect is present in grapheme-colour synaesthetes, but not in matched controls, and exhibits an occipito-parietal topology localised consistently across individuals to extrastriate visual cortices and to superior parietal lobes. Due to the observed timing of this evoked activity and its localisation, our results support a model predicting relatively late synaesthesia-inducing activity, more akin to the Disinhibited Feedback model. In light of previous findings, our study suggest that differential timing and neural mechanisms may account for associator grapheme-colour synaesthetes, as compared to projectors. Highlights- first MEG study to-date examining associator grapheme-colour synaesthetes - we use single-subj. ICA to dissociate synaesthesia-inducing vs. non-inducing activity - we observe evoked activity [~]190 ms following grapheme presentation effects localize consistently to extrastriate visual and superior parietal cortices - the observed timing is consistent with a model of synaesthesia supporting relatively late activity of synaesthesia-inducing stimuli - our findings suggest that differential mechanisms may underlie different sub-types of grapheme-colour synaesthesia

neuroscience↗

bioRxiv2022

Predictive coordination of breathing during speaking and listening

It has long been known that human breathing is altered during listening and speaking compared to rest. Theoretical models of human communication suggest two distinct phenomena during speaking and listening: During speaking, inhalation depth is adjusted to the air volume required for the upcoming utterance. During listening, inhalation is temporally aligned to inhalation of the speaker. While evidence for the former is relatively strong, it is virtually absent for the latter. We address both questions using recordings of speech envelope and respiration in 30 participants during 14 minutes of speaking and listening. We extend existing evidence for the first phenomenon by using the speech envelope to show that inhalation depth is positively correlated with the total power of the speech envelope in the following utterance. Pertaining to the second phenomenon, we provide first evidence that inhalation during listening to your own speech is significantly more likely at time points of inhalation during speaking. These findings are compatible with models that postulate alignment of internal forward models of interlocutors with the aim to facilitate communication.

neuroscience↗