Biology subjects

Daun, S.

Publications and source records attributed to Daun, S..

10 recordsLinked to original sources

bioRxiv2026

Reconciling Two Sides: Novel Analytical Statistics for Lesion Network Mapping

Recent studies have reached conflicting conclusions about the statistical significance of lesion network mapping (LNM), especially regarding the impact of regional node strength on lesion-associated network detection. Here, we present a statistical framework that clarifies the origin of these apparently conflicting findings. Based on the simplified formulation of LNM introduced by van den Heuvel et al. (2026), we propose alternative, permutation-based null models corresponding to the sensitivity and specificity tests used in standard LNM analysis. We derive analytical expressions for both the proposed null models and the node-strength-constrained approach recently introduced by Zalesky and Cash (2026), thereby providing a computationally efficient alternative to permutation-based significance testing. Using synthetic lesion and connectivity matrices, as well as clinical datasets, we compare the outcome of the proposed null models with the standard LNM analysis and demonstrate how the choice of null models influences statistical inference and the resulting lesion network maps. Our framework shows that the apparent disagreements in the recent LNM literature can be understood as a consequence of different statistical null models rather than to conflicting conclusions about LNM itself.

neuroscience↗

bioRxiv2026

Developmental Trajectories of Dynamic Brain Network Organization and Their Alteration in Tourette Syndrome

Typical brain network maturation involves an increase in network flexibility and hemispheric specialization. Tourette syndrome (TS) disrupts these trajectories, with tic severity potentially modulating deviations. This study examined theta-band EEG source connectivity states in typically developing children and children with TS. We assessed age-related trajectories and the impact of tic severity using generalized linear modeling, accounting for sex and multiple comparisons. K-means clustering identified four recurrent source connectivity states (A-D), with metrics including Coverage, representing state prevalence (proportion of time spent in each state), Average Dwell Time, an index of state stability (mean duration of stable persistence of each state), and Transition Rate Per Minute, reflecting global network flexibility (frequency of state switches per minute). In healthy controls (HC), typical maturation was characterized by increased left intra-hemisphere connectivity state stability and prevalence, decreased diffuse connectivity state stability, and rising network flexibility. TS patients exhibited deviant trajectories, including age-dependent decreasing global network flexibility across subgroups stratified by tic severity and marginally divergent diffuse activity patterns, with high-severity cases showing increased diffuse connectivity state stability. The normative patterns suggest typical motor development requiring dynamic network reconfiguration and hemispheric specialization, processes that appear altered in TS. TS patients exhibit age-dependent network rigidity across severity subgroups, as reflected by decreased transition rates, alongside severity- modulated network imbalances, indicating that tic disorders disrupt mechanisms of brain network maturation underlying motor control. These findings suggest that atypical trajectories of network stability and flexibility represent a key feature of tic pathophysiology, highlighting the role of altered network dynamics in TS during maturation.

neuroscience↗

bioRxiv2024

Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns

Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship between FC and neurobiology non-invasively. These techniques allow for targeted manipulations to study the effect of network disturbances on FC. Most modelling research has concentrated on replicating empirical static FC (sFC). However, FC changes over time, and its dynamic properties are closely linked to behaviour and symptomatology. In this study, we adapted computational models to reflect both sFC and dynamic FC (dFC) of individuals, allowing for a more comprehensive characterisation of the neurobiological origins of FC. We modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. Simulations were conducted using a group-averaged structural connectome and four parameters guiding regional brain activity: i) G, a global coupling scaling parameter; ii) J_i, the local inhibitory current; iii) J_NMDA, the excitatory NMDA synaptic coupling parameter; and iv) w_p, the excitatory population recurrence weight. We evaluated the models based on four metrics: a) the sFC, b) the FC variance, c) the temporal correlation (TC), and d) the node cohesion (NC). The optimal model for each subject was identified by the fit to both sFC and TC. We analysed associations between brain-wide sFC and TC features with optimal model parameters and fits with a univariate correlation approach and multivariate prediction models. In addition, we used a group-average perturbation approach to investigate the effect of coupling in each region on overall network connectivity. Our models could replicate empirical sFC and TC but not the FC variance and NC. Both fits and parameters exhibited strong associations with brain connectivity. G correlated positively and J_NMDA negatively with a range of static and dynamic FC features (|r| > 0.2, p(FDR) < 0.05). TC fit correlated negatively, and sFC fit positively with static and dynamic FC features. TC features were predictive of TC fit, sFC features of sFC fit (R2 > 0.5). Perturbation analysis revealed that the sFC fit was most impacted by coupling changes in the left paracentral gyrus ({Delta}r = 0.07). In contrast, the left pars triangularis impacted the TC fit most strongly ({Delta}r = 0.24). Our findings indicate that neurobiological characteristics are associated with individual variability in sFC and dFC, and that sFC and dFC are shaped by small sets of distinct regions. In addition, we show that brain network modelling can replicate some, but not all, properties of dFC, and model fits are strongly influenced by specific FC patterns. By modelling both sFC and dFC, we could produce new insights into neurobiological mechanisms of brain network configurations.

neuroscience↗

bioRxiv2024

AutoGaitA - Automated Gait Analysis in Python

Individual behaviours require the nervous system to execute specialised motor programs, each characterised by unique patterns of coordinated movements across body parts. Deep learning approaches for body-posture tracking have facilitated the analysis of such motor programs. However, translating the resulting time-stamped coordinate datasets into meaningful kinematic representations of motor programs remains a long-standing challenge. We developed the versatile quantitative framework AutoGaitA (Automated Gait Analysis), a Python toolbox that enables comparisons of motor programs at multiple levels of granularity and across tracking methods, species and behaviours. AutoGaitA allowed us to demonstrate that flies, mice, and humans, despite divergent biomechanics, converge on the age-dependent loss of propulsive strength, and that, in mice, locomotor programs adapt as an integrated function of both age and task difficulty. AutoGaitA represents a truly universal framework for robust analyses of motor programs and changes thereof in health and disease, and across species and behaviours.

animal behavior and cognition↗

bioRxiv2024

Earlier finish of motor planning in the premotor cortex predicts faster motor command in the primary motor cortex: human intracranial EEG evidence

The human motor system has a hierarchical control during finger movements. The non-primary motor cortex (premotor cortex, PM, and supplementary motor area, SMA) organizes motor planning, while the primary motor cortex (M1) is responsible for motor execution. We utilized the human intracranial EEGs high temporal and spatial resolution to investigate how the temporal dynamics of the high-gamma neural oscillations in the hierarchically organized motor sub-regions, during both pre-movement planning and motor execution, correlated with reaction times (RTs) in a cued finger movement task. Our results showed that the high-gamma power of PM, SMA, and M1 activated sequentially. More importantly, the sustained high-gamma power activation in the non-primary motor cortex and the peak latency of high-gamma power in M1 had a significant predictive relationship with the RTs. In particular, the faster the activation of the non-primary motor cortex returned to baseline, the faster the motor command in M1, and accordingly the shorter the RTs. Further, pairwise phase coherence between the motor areas showed that the more sustained the connection between the motor areas, the longer the RTs would be. The current findings illustrate the relationship between the temporal profiles of high-gamma power in human motor areas and response performance.

neuroscience↗

bioRxiv2024

Autophagy regulator ATG5 preserves cerebellar function by safeguarding its glycolytic activity

Dysfunctions in autophagy, a highly conserved cellular mechanism responsible for the degradation of intracellular components within lysosomes, often result in neurodegeneration. The neuroprotective effect of autophagy varies across neuronal subtypes, and the mechanisms of selective vulnerability of neurons to autophagy dysfunction are currently unknown. Utilizing a mouse model of ATG5 deficiency in inhibitory neurons and a comprehensive approach, including PET imaging, metabolomics, stable-isotope labeling studies, and live cell imaging, we establish that autophagy contributes to the survival of cerebellar Purkinje cells (PCs) by safeguarding their glycolytic activity. We show that the core autophagy protein ATG5 downregulates the levels of the glucose transporter 2 (GLUT2) during brain maturation. Autophagy-deficient PCs exhibit increased glucose uptake, along with elevated levels of glycolytic intermediates and methylglyoxal-modified proteins. We propose lysophosphatidic acid and serine as glycolytic intermediates inducing PC death and demonstrate that deletion of GLUT2 in ATG5-deficient mice mitigates PC neurodegeneration and restores their ataxic gait. Taken together, this study reveals a novel neuroprotective role of autophagy in preventing excessive glycolytic metabolism in the brain.

neuroscience↗

bioRxiv2023

Previous motor task performance impacts phase-based EEG resting-state connectivity states

The resting human brain cycles through distinct states that can be analyzed using microstate analysis and electroencephalography (EEG) data. This approach classifies, multichannel EEG data into spontaneously interchanging microstates based on topographic features. These microstates may be valuable biomarkers in neurodegenerative diseases since they reflect the resting brains state. However, microstates do not provide information about the active neural networks during the resting-state. This article presents an alternative and complementary method for analyzing resting-state EEG data and demonstrates its reproducibility and reliability. This method considers cerebral connectivity states defined by phase synchronization and measured using the corrected imaginary phase-locking value (ciPLV) based on source-reconstructed EEG recordings. We analyzed resting-state EEG data from young, healthy participants acquired on five consecutive days before and after a motor task. We show that our data reproduce microstates previously reported. Further, we reveal four stable topographic patterns over the multiple recording sessions in the source connectivity space. While the classical microstates were unaffected by a preceding motor task, the connectivity states were altered, reflecting the suppression of frontal activity in the post-movement resting-state.

neuroscience↗

bioRxiv2020

Robustness of individualized inferences from longitudinal resting state dynamics

Tracking how individual human brains change over extended timescales is crucial in scenarios ranging from healthy aging to stroke recovery. Tracking these neuroplastic changes with resting state (RS) activity is a promising but poorly understood possibility. It remains unresolved whether a persons RS activity over time can be reliably decoded to distinguish neurophysiological changes from confounding differences in cognitive state during rest. Here, we assessed whether this confounding can be minimized by tracking the configuration of an individuals RS activity that is shaped by their distinctive neurophysiology rather than cognitive state. Using EEG, individual RS activity was acquired over five consecutive days along with activity in tasks that were devised to simulate the confounding effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify a distinctive configuration in each individuals RS activity. Using this configuration as a classifier-rule, an individual could be re-identified with high accuracy from 2-second samples of the instantaneous oscillatory power acquired on a different day both from RS and confounded-RS. Importantly, the high accuracy of cross-day classification was achieved only with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). These findings support the suitability of longitudinal RS to support robust individualized inferences about neurophysiological change in health and disease.

neuroscience↗

bioRxiv2020

Dynamic graph analysis reveals aging effects on motor network communication

Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging-associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase-locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually-cued finger-tapping task. The network analysis yielded specific age-related network structures varying in time in the low frequencies (2-7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were - temporally arranged in this order - heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre-motor areas, associated with a loss of hemispheric lateralization in older subjects.

neuroscience↗

bioRxiv2018

Unraveling intra- and intersegmental neuronal connectivity between central pattern generating networks in a multi-legged locomotor system

Animal walking results from a complex interplay of central pattern generating networks (CPGs), local sensory signals expressing position, velocity and forces generated in the legs, and coordinating signals between neighboring ones. In the stick insect, in particular, intra- and intersegmental coordination is conveyed by these sensory signals. The rhythmic activity of the CPGs, hence of the legs, can be modified by the aforementioned sensory signals. However, the precise nature of the interaction between the CPGs and these sensory signals has remained largely unknown. Experimental methods aiming at finding out details of these interactions, often apply the muscarinic acetylcholine receptor agonist, pilocarpine in order to induce rhythmic activity in the CPGs, hence in the motoneurons of the segmental ganglia. Using this general approach, we removed the influence of sensory signals and investigated the putative connections between CPGs associated with the coxa-trochanter (CTr)-joint in the different segments (legs) in more detail. The experimental data underwent phase-difference analysis and Dynamic Causal Modelling (DCM). These methods can uncover the underlying coupling structure and strength between pairs of segmental ganglia (CPGs). We set up different coupling schemes (models) for DCM and compared them using Bayesian Model Selection (BMS). Models with contralateral connections in each segment and ipsilateral connections on both sides, as well as the coupling from the meta- to the ipsilateral prothoracic ganglion were preferred by BMS to all other types of models tested. Moreover, the intrasegmental coupling strength in the mesothoracic ganglion was the strongest and most stable in all three ganglia.

neuroscience↗