Search bioRxiv⌕ Search

Biology subjects

Kuhlmann, L.

Publications and source records attributed to Kuhlmann, L..

15 recordsLinked to original sources
bioRxiv2026

Context Shapes Alpha-Band Electrophysiological Signatures Under Psilocybin

Background: Psilocybin is a serotonergic psychedelic that provides a pharmacological tool for examining neural activity associated with altered states of consciousness. Existing studies report reduced power (4-30 Hz) in posterior regions and altered functional connectivity (FC) at rest, but have typically involved small samples and coarse anatomical parcellations. Additionally, perceptual engagement has been shown to attenuate psilocybin-related whole-brain FC changes. However, how naturalistic context modifies psilocybin's frequency-specific electrophysiological effects remains incompletely characterised. Methods: We analysed the largest psilocybin electroencephalography (EEG) dataset to date (n = 56), with matched baseline recordings acquired before psilocybin administration. EEG was recorded during movie viewing, eyes-closed rest, guided meditation, and music listening. Cortical source activity was estimated using minimum-norm estimation and analysed across six canonical frequency bands (1-80 Hz). Spectral power, alpha peak frequency (APF), and magnitude-squared coherence within and between seven functional networks were quantified. Psilocybin effects were assessed using permutation testing with FDR correction, and linear mixed-effects models separated the contributions of drug, context, and their interaction to alpha-band connectivity. Results: At rest, psilocybin decreased alpha (8-13 Hz) power, increased gamma-range power, and elevated APF. Alpha-band FC decreased within and between higher-order networks implicated in top-down control. Relative to eyes-closed rest, psilocybin-context interactions showed that structured sensory environments attenuated or reversed psilocybin-related alpha-band connectivity changes. Conclusions: These findings identify alpha-band activity as a key electrophysiological correlate of psilocybin-related changes in cortical communication. Structured sensory input reshapes psilocybin-related cortical dynamics, particularly alpha-band connectivity across large-scale cortical networks, with the strength and direction of this modulation differing by context.

neuroscience↗
bioRxiv2026

Appetite for change: How psilocybin reshapes food reward learning through striatal dopamine function

Psilocybin has emerged as a promising therapeutic agent for psychiatric disorders characterised by cognitive rigidity and disrupted reward processing, including anorexia nervosa. While its pro-cognitive effects have been mechanistically probed almost exclusively through serotonin receptor subtype antagonism, the downstream contributions of dopaminergic systems to these outcomes remain poorly understood. Here, we examined how psilocybin (1.5 mg/kg) alters cognitive flexibility across multiple operant paradigms in female rats and whether ventral striatal dopamine, nutritional state or prior activity-based anorexia (ABA) exposure moderate these effects. In food-restricted rats, psilocybin improved reversal learning with an apparent temporal delay relative to our previously published ad libitum fed cohort. In vivo fiber photometry revealed that psilocybin broadly amplified nucleus accumbens core (NAc) dopamine transients time-locked to both expected and unexpected outcomes during probabilistic reversal learning across 7 days. Computational modelling identified psilocybin-specific increases in learning rate and reductions in prior value weighting, consistent with strengthened feedback-driven updating. In touchscreen paradigms, psilocybin enhanced discrimination accuracy and accelerated reversal learning acquisition when administered prior to initial discrimination, but impaired serial reversal accuracy when administered after initial discrimination. ABA exposure constrained psilocybin's pro-cognitive effects, abolishing discrimination accuracy benefits, suggesting that metabolic disruption and stress alter the neural substrates required for psilocybin-enhanced neuroplasticity and reward learning. These findings provide the first direct evidence that psilocybin modulates striatal dopamine signalling in a behaving animal and demonstrate that current nutritional state and prior experience with pathological weight loss and reward learning moderate its cognitive effects.

neuroscience↗
bioRxiv2026

A Neural Mass Modelling Framework for Evaluating EEG Source Localisation of Seizure Activity

Electroencephalography and magnetoencephalography (EEG/MEG) provide non-invasive measurements of large-scale neural activity but do not directly reveal the underlying cortical sources, motivating the use of source localisation algorithms. However, objective evaluation of these methods remains challenging due to the absence of an experimentally verifiable ground truth. This study presents a simulation framework for generating biologically plausible ictal dynamics and their corresponding EEG signals to enable systematic benchmarking of source imaging approaches. Cortical seizure initiation and propagation were simulated using network-coupled neural mass (Epileptor) models, and combined with realistic forward models of the human head to produce macroscopic, electrophysiological data with known ground truth under varying conditions. Using this dataset, we evaluated established source localisation methods across idealised and realistic scenarios. Existing approaches achieved reasonable spatial accuracy under high-density, noise-free conditions; however, performance degraded substantially with reduced sensor coverage and added noise. This degradation was driven primarily by failures to recover source polarity, even when spatial localisation remained relatively accurate. These results suggest that current methods may be sufficient for identifying epileptogenic regions or tracking regional recruitment, but highlight polarity reconstruction as a key limitation for studies of seizure dynamics and network organisation. The proposed framework provides a reproducible and biologically grounded testbed for the development and evaluation of electrophysiological source localisation techniques.

neuroscience↗
bioRxiv2025

Structural Eigenmodes of the Brain to Improve the Source Localisation of EEG: Application to Epileptiform Activity

A fundamental view of neuroscience is that, in addition to neuronal activity, the structure of the brain constrains and explains brain function. An alluring formalism in computational neuroscience has been the generation of structural eigenmodes of neural activity from a matrix representing the anatomy of the brain. Traditionally, brain connectomics has been the gold standard for the coupling between structure and function. However, it has recently been suggested that simpler brain geometry can provide more explanatory power in fMRI. An adjacent modality is the source localisation problem of EEG, which aims to identify the underlying generators of EEG recordings. The underdetermined nature of the problem requires sufficient constraints to produce realistic and unique solutions of source activity. In this work, we presented a simple framework for incorporating different forms of structural brain eigenmodes to constrain the source localisation problem in epilepsy. We found that geometric eigenmodes were able to reconstruct the spread of a seizure through the brain slightly better than connectome eigenmodes, and both types of structural modes significantly outperformed commonly used approaches.

neuroscience↗
bioRxiv2024

Cortical connectivity, local dynamics and stability correlates of global conscious states

Consciousness emerges from complex neural processes, yet its precise neurobiological correlates remain uncertain. Here a space-time-resolved inference-based framework is applied to estimate the neurophysiological variables of a wholecortex model and analyze the neural mechanism correlates of global consciousness by way of a correlation analysis between behavioural and neural variable time-series. Using magnetoencephalography (MEG) data from 15 participants under Xenon-induced anesthesia, interconnected neural mass models (NMMs) were developed and time-evolving regional neurophysiological variables and inter-regional connectivity strengths were inferred from the data. Analyses revealed significant correlations between consciousness levels and inter-regional connectivity, particularly in posterior parietal, occipital, and prefrontal regions. Moreover, results support a parietal, rather than frontal, network backbone to facilitate global consciousness. Regional-level analyses further identified correlates of consciousness within the posterior parietal and occipital regions. Lastly, reductions in consciousness were linked to stabilized cortical dynamics, reflected by changes in the eigenmodes of the system. This framework provides a novel, inference-based approach to investigating consciousness, offering a time-resolved perspective on neural mechanism correlates during altered states.

neuroscience↗
bioRxiv2022

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

neuroscience↗
bioRxiv2022

A neural framework for spontaneous development of orientation selectivity in the primary visual cortex

The mechanism or microcircuitry behind orientation selectivity in primary visual cortex (V1), and the means by which it develops without supervision or visual input, both remain unresolved questions. Work on the developmental question has assumed the prevalent spatial convergence model of orientation selectivity as the target mechanism. Encouraged by growing evidence challenging both the completeness of this model and its developmental viability, we investigated an alternative scheme. Accordingly, we demonstrate computationally how a scheme in which orientation selectivity originates from the orientation biases already in the retina and lateral geniculate nucelus (LGN) can answer both the mechanistic and developmental questions. In this scheme, the divergence of outputs from the retina allows retinal spontaneous activity to create correlations within the LGN. These correlations in turn allow a Hebbian plasticity mechanism to strengthen those LGN inputs to V1 which carry similar orientation biases and thus provide an orientation tuned excitatory input.

neuroscience↗
bioRxiv2022

Glycoproteomics Identifies Plexin-B3 as Targetable Cell Surface Protein Required for Growth and Invasion of Triple Negative Breast Cancer Cells

Driven by the lack of targeted therapies, triple negative breast cancers (TNBC) have the worst overall survival of all breast cancer subtypes. Considering cell surface proteins are favorable drug targets and are predominantly glycosylated, glycoproteome profiling has significant potential to facilitate the identification of much-needed drug targets for TNBC. Here, we performed N-glycoproteomics on six TNBC and five normal control (NC) cell lines using hydrazide-based enrichment. Quantitative proteomics and integrative data mining led to the discovery of Plexin-B3 (PLXNB3), a previously undescribed TNBC-enriched cell surface protein. Furthermore, siRNA knock-down and CRISPR-Cas9 editing of in vitro and in vivo models show that PLXNB3 is required for TNBC cell line growth, invasion, and migration. Altogether, we provide insight into N-glycoproteome remodeling associated with TNBC and functional evaluation of an extracted target, which indicate the surface protein PLXNB3 as a potential therapeutic target for TNBC. HighlightsO_LIIn-depth N-glycoproteomic profiles of six TNBC and five NC cell line models C_LIO_LIIdentification of PLXNB3 as a novel TNBC-enriched cell surface protein C_LIO_LIPLXNB3 affects growth, invasion, and migration in TNBC models C_LIO_LIPLXNB3 inhibition represents a targeted treatment option for TNBC C_LI

cancer biology↗
bioRxiv2022

Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm

Neural mechanisms are complex and difficult to image. This paper presents a new space-time resolved whole-brain imaging framework, called Neurophysiological Mechanism Imaging (NMI), that identifies neurophysiological mechanisms within cerebral cortex at the macroscopic scale. By fitting neural mass models to electromagnetic source imaging data using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately imaged across the whole brain at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural mechanisms in different brain states. HighlightsO_LIThe whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner. C_LIO_LIDeveloped a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series. C_LIO_LIThe semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter. C_LIO_LIProvided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms. C_LIO_LIApplied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power. C_LI

neuroscience↗
bioRxiv2021

Hitting more birds with one stone: CD70 as an actionable immunotherapeutic target in recurrent glioblastoma

PurposeGlioblastoma (GBM) patients suffer from a dismal prognosis, with standard of care therapy inevitably leading to therapy-resistant recurrent tumors. The presence of brain tumor initiating cells (BTICs) drives the extensive heterogeneity seen in GBM, prompting the need for novel therapies specifically targeting this subset of tumor-driving cells. Here we identify CD70 as a potential therapeutic target for recurrent GBM BTICs. Experimental DesignIn the current study, we identified the relevance and functional influence of CD70 on primary and recurrent GBM cells, and further define its function using established stem cell assays. We utilize CD70 knockdown studies, subsequent RNAseq pathway analysis, and in vivo xenotransplantation to validate CD70s role in GBM. Next, we developed and tested an anti-CD70 CAR-T therapy, which we validated in vitro and in vivo using our established preclinical model of human GBM. Lastly, we explored the importance of CD70 in the tumor immune microenvironment (TIME) by assessing the presence of its receptor, CD27, in immune infiltrates derived from freshly resected GBM tumor samples. ResultsCD70 expression is elevated in recurrent GBM and CD70 knockdown reduces tumorigenicity in vitro and in vivo. CD70 CAR-T therapy significantly improves prognosis in vivo. We also found CD27 to be present on the cell surface of multiple relevant GBM TIME cell populations. ConclusionCD70 plays a key role in recurrent GBM cell aggressiveness and maintenance. Immunotherapeutic targeting of CD70 significantly improves survival in animal models and the CD70/CD27 axis may be a viable poly-therapeutic avenue to co-target both GBM and its TIME.

cancer biology↗
bioRxiv2021

linc2function: A deep learning model to identify and assign function to long noncoding RNA (lncRNA)

MotivationLncRNAs are much more versatile and are involved in many regulatory roles inside the cell than previously believed. Existing databases lack consistencies in lncRNA annotations, and the functionality of over 95% of the known lncRNAs are yet to be established. LncRNA transcript identification involves discriminating them from their coding counterparts, which can be done with traditional experimental approaches, or via in silico methods. The later approach employs various computational algorithms, including machine learning classifiers to predict the lncRNA forming potential of a given transcript. Such approaches provide an economical and faster alternative to the experimental methods. Current in silico methods mainly use primary-sequence based features to build predictive models limiting their accuracy and robustness. Moreover, many of these tools make use of reference genome based features, in consequence making them unsuitable for non-model species. Hence, there is a need to comprehensively evaluate the efficacy of different predictive features to build computational models. Additionally, effective models will have to provide maximum prediction performance using the least number of features in a species-agnostic manner. It is popularly known in the protein world that "structure is function". This also applies to lncRNAs as their functional mechanisms are similar to those of proteins. Generally, lncRNA function by structurally binding to its target proteins or nucleic acid forming complexes. The secondary structures of the lncRNAs are modular providing interaction sites for their interactome made of DNA, RNA, and proteins. Through these interactions, they epigenetically regulate cellular biology, thereby forming a layer of genomic programming on top of the coding genes. We demonstrate that in addition to using transcript sequence, we can provide comprehensive functional annotation by collating their interactome and secondary structure information. ResultsHere, we evaluated an exhaustive list of sequence-based, secondary-structure, interactome, and physicochemical features for their ability to predict the lncRNA potential of a transcript. Based on our analysis, we built different machine learning models using optimum feature-set. We found our model to be on par or exceeding the execution of the state-of-the-art methods with AUC values of over 0.9 for a diverse collection of species tested. Finally, we built a pipeline called linc2function that provides the information necessary to functionally annotate a lncRNA conveniently in a single window. AvailabilityThe source code is accessible use under MIT license in standalone mode, and as a webserver (https://bioinformaticslab.erc.monash.edu/linc2function).

bioinformatics↗
bioRxiv2020

Epidynamics characterize and navigate the map of seizure dynamics

Seizures are a disruption of normal brain activity present across a vast range of species, diseases, and conditions. Here we introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory, and applied it to the analysis of EEG data. The "dynamotype" of a seizure is the part of its dynamic composition that defines its observable characteristics, including how it starts, evolves and terminates. Analyzing over 2000 focal-onset seizures recorded from 7 epilepsy centers on five continents, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain dynamotypes are more common), non-bijectivity (a patient may display multiple dynamotypes) and pairing preference (multiple dynamotypes may occur during one seizure). TSD not only provides a way to stratify patients in complement to present practical classifications but also guides biophysically based mechanistic approaches and provides a language to describe the most critical features of seizure dynamics. Impact statementTaxonomy of Seizure Dynamics (TSD) provides a rigorous method for classifying and quantifying seizures and a principled framework for understanding seizure initiation and propagation.

bioinformatics↗
bioRxiv2019

Critical slowing as a biomarker for seizure susceptibility

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system would suggest that prior to a seizure recorded brain signals may exhibit critical slowing, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we found key signatures of critical slowing prior to seizures. Signals related to a critically slowing process fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing is a reliable indicator that could be used in seizure forecasting algorithms.

neuroscience↗
bioRxiv2019

In search of Universal Cortical Power Changes Linked to NMDA-Antagonist based Anesthetic Induced Reductions in Consciousness.

B. AbstractO_ST_ABSBackground.C_ST_ABSDespite their intriguing nature, investigations of the neurophysiology of N-methyl-D-aspartate (NMDA)-antagonists Xenon (Xe) and nitrous oxide (N2O) are limited and have revealed inconsistent frequency-dependent alterations, in spectral power and functional connectivity. Discrepancies are likely due to using low resolution electroencephalography restricted to sensor level changes, concomitant anesthetic agent administration and dosage. Our intention was to describe the effects of equivalent stepwise levels of Xe and N2O administration on oscillatory source power using a crossover design, to explore universal mechanisms of NMDA-based anesthesia.\n\nMethods.22 healthy males participated in a study of simultaneous magnetoencephalography and electroencephalography recordings. In separate sessions, equivalent subanesthetic doses of gaseous anesthetic agents N2O and Xe (0.25, 0.50, 0.75 equi MAC-awake) and 1.30 MAC-awake Xe (for Loss of Responsiveness) were administered. Source power in various frequency bands was computed and statistically assessed relative to a conscious baseline.\n\nResults.Delta (l-4Hz) and theta (4-8Hz) band power was significantly increased at the highest Xe concentration (42%, 1.30 MAC-awake) relative to baseline for both magnetoencephalography and electroencephalography source power (p<0.005). A reduction in frontal alpha (8-13 Hz) power was observed upon N2O administration, and shown to be stronger than equivalent Xe dosage reductions (p=0.005). Higher frequency activity increases were observed in magnetoencephalographic but not encephalographic signals for N2O alone with occipital low gamma (30-49Hz) and widespread high gamma (51-99Hz) rise in source power.\n\nConclusions.Magnetoencephalography source imaging revealed unequivocal and widespread power changes in dissociative anesthesia, which were divergent to source electroencephalography. Loss of Responsiveness anesthesia at 42% Xe (1.30 MAC-awake) demonstrated, similar to inductive agents, low frequency power increases in frontal delta and global theta. N2O sedation yielded a rise in high frequency power in the gamma range which was primarily occipital for lower gamma bandwidth (3049 Hz) and substantially decreased alpha power, particularly in frontal regions.\n\nClinical trial number and Registry URLNot applicable.\n\nPrior PresentationsPelentritou Andria, Kuhlmann Levin; Lee Heonsoo; Cormack John; Mcguigan Steven; Woods Will; Sleigh Jamie; Lee UnCheol; Muthukumaraswamy Suresh; Liley David. Searching For Universal Cortical Power Changes Linked To Anesthetic Induced Reductions In Consciousness. The Science of Consciousness April 4th 2018. Tucson, Arizona, USA.\n\nSummary StatementNot applicable.

neuroscience↗
bioRxiv2018

Speed Regulation and Gradual Enhancer Switching Models as Flexible and Evolvable Patterning Mechanisms

Short AbstractUsing simple animations, mathematical formulations, and computational implementation in Matlab, we present a newly devised embryonic patterning mechanism: the Speed Regulation model, and its molecular realization: the Gradual Enhancer Switching model. We show how our models shed light on the phenomenology of insect development and evolution.\n\nLong AbstractPartitioning an initially homogeneous group of cells into different fates is a common problem in development. A curious case is the anterior-posterior (AP) fate specification during early embryogenesis in insects. The AP fates of most insects are specified in two different phases: (i) the blastoderm, where the AP axis does not undergo any axial elongation, and (ii) the germband, where the AP axis undergoes gradual axis elongation. Throughout evolution, insects show remarkable flexibility in the number of fates specified in the blastoderm vs germband. This hints that AP specification in insects relies on a flexible mechanism that can pattern both non-elongating embryonic structures (like the blastoderm) and elongating tissues (like the germband). Here we describe the Speed Regulation model, a recently suggested patterning mechanism, that can pattern both elongating and non-elongating tissues and ensures the evolvability between them. The model is successful in reproducing the phenomenology of AP axis specification and evolution in insects. In addition, it explains the temporal-based patterning of other embryonic structures like the AP axis of vertebrates and the dorsoventral axis of vertebrate neural tube. The Speed Regulation model is phenomenological in its formulation, in the sense that it does not specify a particular molecular realization. We then present the Gradual Enhancer Switching model, in which we describe a specific molecular implementation of the Speed Gradient model that incorporates a novel scheme of cis-regulation within gene regulatory networks. The paper is linked to two videos on YouTube referred to below.\n\nLinked VideosVideo I: https://youtu.be/YcGotl8OdYw\n\nVideo II: https://youtu.be/f-JnjF2aNLw

developmental biology↗