Biology subjects

Kohl, O.

Publications and source records attributed to Kohl, O..

6 recordsLinked to original sources

bioRxiv2026

Role of GABA and NMDA receptors in shaping cortical timescales and large-scale network dynamics

Cortical brain regions integrate information across different timescales, ranging from fast sensory processing to longer integration windows, allowing cognitive functions like working memory. At the large-scale, brain regions organize into transient network states that rapidly switch over time and similarly contribute to cognition. Both cortical timescales and large-scale network dynamics are proposed to be determined by the balance between recurrent synaptic excitation and GABAergic inhibition. Here, we pharmacologically manipulated synaptic transmission at GABAA and NMDA receptors in 60 healthy male participants and acquired resting-state magnetoencephalography. Neuronal timescales followed a hierarchical gradient with shorter timescales in early sensory regions. Increasing GABAergic activity prolonged neuronal timescales across cortical regions. This effect was most prominent in the frontal default mode and in the dorsal attention network. Notably, dynamic network analyses revealed that the occurrence probability of the frontal default mode network increased, whereas the occurrence of the dorsal attention network was reduced. NMDA receptor modulation resulted in no significant changes. Together, these findings provide causal evidence that GABAergic inhibition is a key regulator of cortical temporal organization, linking microscale synaptic mechanisms to neuronal timescales and network dynamics that support diverse cognitive function.

neuroscience↗

bioRxiv2025

Robust and replicable effects of ageing on resting state brain electrophysiology measured with MEG.

Non-invasive recordings using brain electrophysiology provide credible insights into decline in neuronal functioning with age. New approaches are required to translate these results into compelling clinical metrics and meet the global challenge of preserving brain health in ageing. Changes in neuronal dynamics with ageing are observable in the power spectra of EEG and MEG recordings. Highly promising candidates for electrophysiological markers have been identified, but progress is hindered by substantial methodological variability across studies. This makes it challenging to establish a clear consensus on the frequency, location, and direction of any single reported effect within the larger body of research. We estimate a full-frequency whole-head profile of the ageing effect on eyes-closed resting-state MEG using the GLM-Spectrum. This data representation is easily sharable, facilitates meta-analyses and provides a framework for estimating statistical power of age effects for future study planning. We use this to show that the effect of age replicates across open-access MEG datasets and is robust to modelling of common covariates. Distinct components within the full frequency profile have different effect sizes, indicating that sample-size planning for ageing effects must consider the specific features of interest. The frequency profile of ageing is strongly robust to a range of common covariates and partially robust to modelling of grey matter volume. We establish that what seems a well-powered study may become underpowered when analyses target an age effect that is linearly separable from an age-relevant covariate such as grey matter volume. These results provide a pathway towards formal comparison and assessment of candidate markers for brain health in ageing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/668093v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1a9c351org.highwire.dtl.DTLVardef@563837org.highwire.dtl.DTLVardef@84b898org.highwire.dtl.DTLVardef@1730472_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThere is a strong effect of ageing across the adult lifespan on neuronal oscillations across a range of frequencies and spatial locations. C_LIO_LIThe effect is replicable across multiple open-access datasets. C_LIO_LIThe effect is robust to a range of sources of inter-subject variability. C_LIO_LIThe age effect has heterogeneous effect size across space and frequency with implications for future study planning. C_LI

neuroscience↗

bioRxiv2024

Effects of age on resting-state cortical networks

Understanding how ageing affects brain function remains a central challenge in neuroscience. Electrophysiological brain imaging techniques provide a near-direct measure of neuronal activity, which is useful for characterising neurophysiological health. They offer us the ability to track large-scale networks of functional activity with high temporal precision. The effects of healthy ageing on these networks remain poorly understood, in part due to small sample sizes and limited control for confounding factors in previous studies. Here, we analysed resting-state source-reconstructed magnetoencephalography (MEG) data from a large cross-sectional cohort of healthy adults (N =612, 18-88 years old) to characterise the effect of age using not only time-averaged (static), but also transient (dynamic) network activity. We examined time-averaged power and coherence across canonical frequency bands ({delta}, {theta}, , {beta}, {gamma}), as well as transient network dynamics identified using Hidden Markov Modelling. We included many confounding variables known to be affected by age, such as brain volume, as well as head size and position, which have previously been overlooked. Ageing was associated with frequency-specific changes in oscillatory power, with decreases in low-frequency ({delta}, {theta}) power and increases in high-frequency ({beta}) power. Coherence increased across all frequency bands and was positively associated with cognitive performance. Transient network analyses additionally revealed that frontal network occurrences declined with age, with evidence suggesting a compensatory role in supporting cognition. These findings provide a more comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change. HighlightsO_LIUsing source reconstructed magnetoencephalography data, we studied cortical functional networks of oscillatory activity from a large healthy population (612 subjects, 18-88 years old). C_LIO_LITime-averaged (static) network analysis in canonical frequency bands ({delta}, {theta}, , {beta}, {gamma}) showed: {beta} power and coherence increased with age; {delta}/{theta} power decreased with age, but coherence increased. Coherence in all frequencies increased with cognitive performance. C_LIO_LITransient (dynamic) network analysis showed frontal networks decreased in occurrence with age and their relation to cognitive performance showed that these changes were compensatory. The other networks showed a decreased occurrence with age. C_LIO_LIIn testing age and cognitive performance effects for statistical significance, we included a comprehensive set of confounds, such as brain volumes, head size and position, which has previously been overlooked. C_LIO_LIWe provide a comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change. C_LI

neuroscience↗

bioRxiv2024

Dynamic Network Analysis of Electrophysiological Task Data

An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. Whilst this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches, can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks. HighlightsO_LIWe show how oscillatory task response analysis can be carried out at the network level using two state-of-the-art methods: the Hidden Markov Model (HMM) and Dynamics Network Modes (DyNeMo). C_LIO_LIThe HMM and DyNeMo can identify oscillatory task responses that conventional time-frequency methods fail to detect. C_LIO_LIDyNeMo provides a more interpretable and precise network decomposition of the data compared to the HMM. C_LIO_LIThe dataset and (Python) scripts used to perform dynamic network analysis on task data are made publicly available. C_LI

neuroscience↗

bioRxiv2022

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multi-level analysis of power spectra, with adaptive covariance and confound modelling.

neuroscience↗

bioRxiv2020

Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks

Visual information processing requires an efficient visual attention system. The neural theory of visual attention (TVA) proposes that visual processing speed depends on the coordinated activity between frontoparietal and occipital brain areas. Previous research has shown that the coordinated activity between (i.e., functional connectivity, inter-FC) cingulo-opercular (COn) and right-frontoparietal (RFPn) networks is linked to visual processing speed. However, evidence for how inter-FC of COn and RFPn with visual networks links to visual processing speed is still missing. Forty-eight healthy human adult participants (27 females) underwent resting-state (rs-)fMRI and performed a whole-report psychophysical task. To obtain inter-FC, we analyzed the entire frequency range available in our rs-fMRI data (i.e., 0.01-0.4 Hz) to avoid discarding neural information. Following previous approaches, we analyzed the data across frequency bins (Hz): Slow-5 (0.01-0.027), Slow-4 (0.027-0.073), Slow-3 (0.073-0.198), and Slow-2 (0.198-0.4). We used the mathematical TVA framework to estimate an individual, latent-level visual processing speed parameter. We found that visual processing speed was negatively associated with inter-FC between RFPn and visual networks in Slow-5 and Slow-2, with no corresponding significant association for inter-FC between COn and visual networks. These results provide first empirical evidence that links inter-FC between RFPn and visual networks with the visual processing speed parameter. These findings suggest a direct connectivity between occipital and right frontoparietal, but not frontoinsular, regions, to support visual processing speed. Significance statementAn efficient visual processing is at the core of visual cognition. Here, we provide evidence for a brain correlate of how fast individuals process visual stimuli. We used mathematical modeling of performance in a visual report task to estimate visual processing speed. A frequency-based analysis of resting-state fMRI signals revealed that functional connectivity between the right frontoparietal network and primary and dorsal occipital networks is linked to visual processing speed. This link was present in the slowest, typical frequency of the fMRI signal but also in the higher frequencies that are routinely discarded. These findings imply that the coordinated spontaneous activity between right frontoparietal and occipital regions supports the individual potential of the visual system for efficient processing.

neuroscience↗