Biology subjects

Ball, T.

Publications and source records attributed to Ball, T..

8 recordsLinked to original sources

bioRxiv2026

Diet-derived Microbial Metabolites Modulate Stress-Responsive Gene Expression in Germ-free Zebrafish

The gut microbiome plays a pivotal role in overall host health, yet the extent at which diet-derived microbial metabolites affect neurodevelopment and inflammation remains unclear. Here, we employed the robogut bioreactor system seeded with fecal samples from two healthy pediatric donors to generate microbial communities exposed to four different diets: low fiber Western (LFW), high fiber Western (HFW), Mediterranean (MED), and Yanomami (YAN), as well as three fiber supplements: fruit and vegetable fiber (FVF), cereal fiber (CRF), and resistant starch fiber (RSF). Metabolites produced by these microbial communities were isolated and applied to germ-free zebrafish (Danio rerio) embryos to assess their effects on neurodevelopment and inflammatory gene expression under basal and stress-induced conditions. Despite minimal changes in microbial composition across diets and fiber sources, significant differences in short-chain fatty acid concentrations were observed. Metabolite treatments had limited effects on the expression of neural and inflammatory genes under basal conditions. Under stress conditions, metabolites from any diet mitigated stress-induced bdnf expression, suggesting a possible modulatory role of microbial metabolites on stress responses. Overall, these findings highlight the resilience of microbial communities to dietary changes and underscore the importance of microbial metabolite output and its donor-specific nature in influencing host neurodevelopment and immune responses.

developmental biology↗

bioRxiv2022

A Modular, Adaptive, Deep-Learning-Based Brain-VR Interface

Brain-Computer Interfaces (BCIs) may open up new possibilities for Virtual Reality (VR) applications: BCIs may be used for active brain control of VR avatars, or to make VR content passively-adaptive based on information decoded from ongoing brain activity. Application domains for such Brain-VR Interfaces (BVRI) include medical and healthcare, entertainment, and education. Conversely, VR technology also opens up new possibilities for BCI research and development: E.g., gamified immersive BCI paradigms may improve subject engagement and long-term motivation, helping to study learning and adaptivity in the BCI-control context. Previously, we have demonstrated a first adaptive, deep-learning-based online BCI for the control of robotic assistants. Here, we describe the extension of this setup to a modular, extensible, VR-compatible online BCI setup. We describe how we integrated a classical active BCI control paradigm using motor imagery into a gamified interactive VR scenario, designed to enhance the long-term motivation of subjects. We also present an initial quality assessment of electroencephalographic (EEG) signals acquired with a dry-electrode system. We anticipate that the presented modular adaptive Brain-VR Interface will help to understand and facilitate (co-)adaptivity during long-term BCI usage.

neuroscience↗

bioRxiv2021

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints. Highlights- We provide systematic reusable benchmarks for brain age from M/EEG signals - The benchmarks were carried out on M/EEG from four countries > 2500 recordings - We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning - Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread - The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS format

neuroscience↗

bioRxiv2019

Topography, Spectral Characteristics, and Extra-to-Intracranial Propagation Pathways of EMG

ObjectiveIntracranial EEG (iEEG) plays an increasingly important role in neuroscientific research and can provide informative control signal for brain-machine interfaces (BMI). While it is clear that electromyographic (EMG) activity of extracranial origin reaches intracranial recordings, the topographic and spectral characteristics of intracranial EMG have been scarcely investigated. It is currently unclear how these characteristics compare to those of physiological brain activity. Little is also known about the exact pathways of extra- to intracranial volume conduction, including the role of craniotomy defects.\n\nMethodsIn 5 epilepsy patients under invasive pre-neurosurgical EEG monitoring, we examined chewing-related effects (ChREs) as a source of intracranial EMG activity and compared those effects with physiological brain activity of 9 patients during several behavioural tasks. These included speech production, finger movements, and music perception. Further, we analyzed the association of craniotomy defects (burr-holes and saw-lines) and the intracranial EMG-effects based on the individual post-operative images.\n\nResultsChRE presented with a spatially smooth distribution across almost all intracranial electrodes with the maximum below the temporal muscle. In contrast, the responses of neural origin were spatially more focalized. ChREs were broad-banded and had a higher spectral power and affected higher frequencies than event-related neural activity. ChRE were largely independent of the individual configuration of craniotomy defects. However, we found indications that the silicone sheet, in which electrocorticography (ECoG) electrodes are embedded, attenuates EMG influences, when sufficiently large.\n\nConclusionThe present work is the first comprehensive evaluation of topographic and spectral characteristics of EMG effects in iEEG based on a large sample of subjects. It shows that chewing-related EMG can affect iEEG recordings with higher power than typical physiological brain activity, especially in higher spectral frequencies. As the topographic pattern of ChRE is largely independent of the individual position of craniotomy defects, a direct pathway of volume conduction through the intact skull plays an important role for extra- to-intracranial signal propagation. Intracranial EMG activity related to natural behavior should be accounted for in neuroscientific and BMI applications, especially when based on high-frequency iEEG components. A detailed knowledge of EMG properties may help to design both EMG-reducing algorithms and ECoG grids with a high shielding factor.\n\nHighlightsO_LIFirst comprehensive description of chewing-related EMG artifacts in iEEG recordings\nC_LIO_LIEMG artifacts and brain activity have distinct topographic and spectral iEEG characteristics\nC_LIO_LIChewing EMG reaches the brain with higher spectral power than task-related brain activity\nC_LIO_LIChewing-related EMG artifacts are largely independent of the the position of craniotomy defects\nC_LI

neuroscience↗

bioRxiv2019

International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda

The growing occurrence of multidrug-resistant (MDR) Salmonella enterica in poultry has been reported with public health concern worldwide. We reported, recently, the occurrence of Escherichia coli and Salmonella enterica serovars carrying clinically relevant resistance genes in dairy cattle farms in the Wakiso District, Uganda, highlighting an urgent need to monitor food-producing animal environments. Here, we present the prevalence, antimicrobial resistance, and sequence type of 51 Salmonella isolates recovered from 400 environmental samples from chicken farms in Uganda. Among the Salmonella isolates, 32/51 (62.7%) were resistant to at least one antimicrobial, and 10/51 (19.6%) displayed multiple drug resistance. Through PCR, five replicon plasmids were identified among all chicken Salmonella including IncFIIS 17/51 (33.3%), IncI1 12/51 (23.5%), IncP 8/51 (15.7%), IncX1 8/51 (15.7%), and IncX2 1/51 (2.0%). In addition, we identified replicons through WGS (ColpVC and IncFIB). A significant seasonal difference between chicken sampling periods was observed (p= 0.0017). We conclude that MDR Salmonella highlights the risks posed to the animals, environment, and humans for infection. Implementing a robust integrated surveillance system in Uganda will help monitor MDR to help control infectious threats.

microbiology↗

bioRxiv2019

Modulating dream experience: Noninvasive brrain stimulation over the sensorimotor cortex reduces dream movement

Recently, cortical correlates of specific dream contents have been reported, such as the activation of the sensorimotor cortex during dreamed hand clenching. Yet, the causal mechanisms underlying specific dream content remain largely elusive. Here, we investigated how alterations in the excitability of sensorimotor areas through transcranial direct current stimulation (tDCS) might alter dream content. Following bihemispheric tDCS or sham stimulation, participants who were awakened from REM sleep filled out a questionnaire on bodily sensations in dreams. tDCS, compared to sham stimulation, significantly decreased reports of dream movement, especially repetitive actions. Contrary to this, other types of bodily experiences, such as tactile or vestibular sensations, were not affected by tDCS, confirming the specificity of stimulation effects. In addition, tDCS reduced interhemispheric coherence in parietal areas and altered the phasic electromyography correlation between the two arms. These findings reveal that a complex reorganization of the motor network co-occurred with the reduction of dream movement, confirming spatial specificity of the stimulation site. We conclude that tDCS over the sensorimotor cortex causally interferes with dream movement during REM sleep.

neuroscience↗

bioRxiv2018

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

neuroscience↗

bioRxiv2017

Global and Local Dynamics of High-Gamma Activity Underlying Error Processing in the Human Brain Revealed by Noninvasive and Intracranial EEG

Error detection in motor behavior is a fundamental cognitive function heavily relying on cortical information processing. Neural activity in the high-gamma frequency band (HGB) closely reflects such local cortical processing, but little is known about its role in error processing, particularly in the healthy human brain. Here we characterize the error-related response of the human brain based on data obtained with noninvasive EEG optimized for HGB mapping in 31 healthy subjects (15 females, 16 males), and additional intracranial EEG data from 9 epilepsy patients (4 females, 5 males). Our findings reveal a comprehensive picture of the global and local dynamics of error-related HGB activity in the human brain. On the global level as reflected in the noninvasive EEG, the error-related response started with an early component dominated by anterior brain regions, followed by a shift to parietal regions, and a subsequent phase characterized by sustained parietal HGB activity. This phase lasted for more than 1 s after the error onset. On the local level reflected in the intracranial EEG, a cascade of both transient and sustained error-related responses involved an even more extended network, spanning beyond frontal and parietal regions to the insula and the hippocampus. HGB mapping appeared especially well suited to investigate late, sustained components of the error response, possibly linked to downstream functional stages such as error-related learning and behavioral adaptation. Our findings establish the basic spatio-temporal properties of HGB activity as a neural correlate of error processing, complementing traditional error-related potential studies.\n\nSignificance StatementThere is great interest to understand how the human brain reacts to errors in goal-directed behavior. An important index of cortical and subcortical information processing is fast oscillatory brain activity, particularly in the high-gamma band (above 50 Hz). Here we show that it is possible to detect signatures of errors in event-related high-gamma responses with noninvasive techniques, characterize these responses comprehensively, and validate the EEG procedure for the detection of such signals. In addition, we demonstrate the added value of intracranial recordings pinpointing the fine-grained spatio-temporal patterns in error-related brain networks. We anticipate that the optimized noninvasive EEG techniques as described here will be helpful in many areas of cognitive neuroscience where fast oscillatory brain activity is of interest.

neuroscience↗