Biology subjects

Kollmorgen, S.

Publications and source records attributed to Kollmorgen, S..

10 recordsLinked to original sources

bioRxiv2025

Enhanced ex vivo 3D whole-brain mapping and automated analysis of Parkinson's disease pathologies in mice

The spatiotemporal relationships among -synuclein inclusion formation, dopaminergic degeneration, and white matter microstructural changes remain poorly defined. To address this, we unilaterally injected preformed fibrils (PFFs) or monomeric -synuclein into the substantia nigra pars compacta (SNc) of wild-type mice. At 12 and 20 weeks post-injection (wpi), we performed ex vivo magnetic resonance imaging (MRI) at 9.4T to assess microstructural, volumetric and paramagnetic changes, along with light-sheet microscopy (LSM) to map -synuclein aggregates, and dopaminergic neuron at single-cell resolution across the whole brain. We developed a Python-based, automated registration pipeline that achieves <40 {micro}m alignment error between ex vivo MRI and LSM and computes clearing-induced distortion, enabling scalable deformation analysis and multimodal multiscale data integration. Unilateral SNc injection of -syn PFFs induced dense phospho--syn pathology in the ipsilateral SNc at 12 wpi, which decreased by 20 wpi in parallel with the loss of tyrosine hydroxylase-positive dopaminergic neuron. Pathogenic -synuclein spreads along with dopaminergic denervation in the nigrostriatal pathway to the ipsilateral striatum and contralateral SNc. Our ex vivo MRI-LSM platform provides a scalable, open-source framework for understanding circuit-level and whole-brain propagation of pathology in an -synucleinopathy model and beyond. HighlightsO_LIOpen-source, automated pipeline aligns ex vivo 9.4T MRI with light-sheet microscopy (LSM) at <40 {micro}m error, quantifies clearing-induced tissue distortion, and enables multiscale, whole-brain multimodal analysis. C_LIO_LIWhole-brain, single-cell-resolution mapping of -synuclein pathology and dopaminergic neuron loss in an -synucleinopathy mouse model using an optimized iDISCO+ protocol that achieves uniform antibody penetration even in dense regions like the striatum. C_LIO_LIUnilateral SNc injection of -syn PFFs drives spread of p--syn pathology along the nigrostriatal pathway to the ipsilateral striatum, BST, CEA, and contralateral SNc, along with dopaminergic degeneration. C_LIO_LIIpsilateral SNc shows high pS129 pathology at 12 weeks post-injection but significant reduction by 20 wpi, concurrent with loss of tyrosine hydroxylase-positive neurons. C_LIO_LIMultiparametric MRI reveals microstructural and iron-related alterations in striatum, SNc, and VTA, with cross-modal correlations linking pS129 burden, TH loss, and magnetic susceptibility changes. C_LI

neuroscience↗

bioRxiv2025

War Trauma Impairs Prenatal Brain Development: Evidence from Genetically-Informed Brain Imaging

The global rise in armed conflicts has raised concerns about their long-term consequences. While the immediate mental and physical impacts of war are well documented, the effects on unborn children of expecting mothers remain poorly understood. Here, we leverage a large dataset (N=37,856) combining historical World War II bombing records with brain imaging and genetic data to examine whether in-utero exposure to air raids is associated with lasting differences in adult brain structure, cognition, and physical health. Using a pre-registered quasi-experimental design controlling for genetic and environmental factors, we find that exposure during the last trimester within 2 km of bombings is associated with lower gray matter volume, which partially mediates adverse effects on intelligence and obesity. These findings suggest a plausible pathway linking prenatal trauma to long-term differences in neurodevelopmental and functional outcomes, underscoring the vulnerability of the fetal brain to acute stress and highlighting the importance of protecting pregnant women during armed conflicts.

neuroscience↗

bioRxiv2025

Atlas-independent brain connectome analysis at voxel-level granularity: graph convolutional networks for etiology classification in newborns

Early identification of altered brain networks in neonates at risk for neurodevelopmental impairments is critical for timely intervention and improving outcomes. This study explores the potential of graph convolutional networks (GCNs) applied to structural brain connectomes at the voxel level granularity to classify neonatal connectomes by their underlying etiology: 51 children with congenital heart disease (CHD), 100 children born very preterm (PB), and 43 children with spina bifida aperta (SBA). Leveraging the flexibility of voxel-level parcellation, we captured fine-grained connectomic differences that improved classification performance (F1 = 0.78) compared to both atlas-based methods (F1 = 0.62) and a multilayer perceptron baseline model (F1 = 0.69). This approach enables subject-specific parcellation without the need for predefined anatomical templates, facilitating the analysis of diverse brain morphologies and age ranges. Attribution analysis using integrated gradients provided interpretable insights into etiology-specific connectomic patterns, highlighting regions of potential neurodevelopmental importance, such as the Rolandic operculum, inferior parietal lobule, and inferior frontal gyrus. Lateralized attribution patterns in PB reflected known neurodevelopmental alterations, underscoring the value of interpretable graph learning for understanding etiology-specific connectomic features. This work represents an important step toward atlas-independent connectome analysis, offering a novel framework for studying diverse neonatal populations and advancing our understanding of early brain development.

neuroscience↗

bioRxiv2024

Shared and unique consequences of Joubert Syndrome gene dysfunction on the zebrafish central nervous system

Joubert Syndrome (JBTS) is a neurodevelopmental ciliopathy defined by a highly specific midbrain-hindbrain malformation, variably associated with additional neurological features. JBTS displays prominent genetic heterogeneity with >40 causative genes that encode proteins localising to the primary cilium, a sensory organelle that is essential for transduction of signalling pathways during neurodevelopment, among other vital functions. JBTS proteins localise to distinct ciliary subcompartments, suggesting diverse functions in cilium biology. Currently, there is no unifying pathomechanism to explain how dysfunction of such diverse primary cilia-related proteins results in such a highly specific brain abnormality. In order to identify the shared consequence of JBTS gene dysfunction, we carried out transcriptomic analysis using zebrafish mutants for the JBTS-causative genes cc2d2auw38, cep290fh297, inpp5ezh506, talpid3i264 and togaram1zh510and the Bardet-Biedl syndrome-causative gene bbs1k742. We identified no commonly dysregulated signalling pathways in these mutants and yet all mutants displayed an enrichment of altered gene sets related to central nervous system function. We found that JBTS mutants have altered primary cilia throughout the brain, however do not display abnormal brain morphology. Nonetheless, behavioural analyses revealed reduced locomotion and loss of postural control which, together with the transcriptomic results, hint at underlying abnormalities in neuronal activity and/or neuronal circuit function. These zebrafish models therefore offer the unique opportunity to study the role of primary cilia in neuronal function beyond early patterning, proliferation and differentiation. Summary StatementJoubert Syndrome gene dysfunction in zebrafish leads to abnormal brain cilia, altered transcription of neuron-associated genes and abnormal swimming behaviour despite normal brain morphology.

neuroscience↗

bioRxiv2022

Primate pre-arcuate cortex actively maintains persistent representations of saccades from plans to outcomes

Dorso-lateral prefrontal cortex (dlPFC) in primates plays a key role in the acquisition and execution of flexible, goal-directed behaviors. Recordings in monkey dlPFC have revealed possible neural correlates of the underlying cognitive processes like attention, planning, or decision-making, both at the single-neuron and population levels. Integrating these observations into a coherent picture of dlPFC function is challenging, as these studies typically focused on neural activity in relation to a few, specific events within a single, fully learned behavioral task. Here we obtain a more comprehensive description of dlPFC activity from a large dataset of population recordings in monkeys across a variety of behavioral contexts. We characterized neural activity in relation to saccades that monkeys made freely, or at different stages of training in multiple tasks involving instructed saccades, perceptual discriminations, and reward-based decisions. Across all contexts, we observed reliable and strong modulations of neural activity in relation to a retrospective representation of the most recent saccadic movement. Prospective, planning-like activity was instead limited to task-related, delayed saccades that were directly eligible for a reward. The link between prospective and retrospective representations was highly structured, potentially reflecting a hard-wired feature of saccade responses in these areas. Only prospective representations were modulated by the recent behavioral history, but neither representations were modulated by learning occurring over days, despite obvious concurrent behavioral changes. Dorso-lateral PFC thus combines tightly linked flexible and rigid representations, with a dominant contribution from retrospective signals maintaining the memory of past actions.

neuroscience↗

bioRxiv2022

Operative dimensions in unconstrained connectivity of recurrent neural networks

Recurrent Neural Networks (RNNs) are commonly used models to study neural computation. However, a comprehensive understanding of how dynamics in RNNs emerge from the underlying connectivity is largely lacking. Previous work derived such an understanding for RNNs fulfilling very specific constraints on their connectivity, but it is unclear whether the resulting insights apply more generally. Here we study how network dynamics are related to network connectivity in RNNs trained without any specific constraints on several tasks previously employed in neuroscience. Despite the apparent high-dimensional connectivity of these RNNs, we show that a low-dimensional, functionally relevant subspace of the weight matrix can be found through the identification of operative dimensions, which we define as components of the connectivity whose removal has a large influence on local RNN dynamics. We find that a weight matrix built from only a few operative dimensions is sufficient for the RNNs to operate with the original performance, implying that much of the high-dimensional structure of the trained connectivity is functionally irrelevant. The existence of a low-dimensional, operative subspace in the weight matrix simplifies the challenge of linking connectivity to network dynamics and suggests that independent network functions may be placed in specific, separate subspaces of the weight matrix to avoid catastrophic forgetting in continual learning.

neuroscience↗

bioRxiv2021

Dendritic integration of sensory and reward information facilitates learning

Learning goal-directed behavior requires association of pertinent sensory stimuli with behaviorally relevant outcomes. In the mammalian neocortex, dendrites of pyramidal neurons are suitable association sites1-3 but how their activities adapt during learning remains elusive. Here, we track calcium signals in apical dendrites of layer 5 (L5) pyramidal neurons in mouse barrel cortex during texture discrimination learning4. We observe diverse task-related responses, either localized to branches or widespread throughout the apical tuft. However, even in expert mice, the tufts capability to discriminate go/no-go stimuli remains poor. Yet, we identify two prevailing response types in dendritic branches: 1) responses to unexpected outcome (reward) in naive mice that decrease with growing task proficiency, and 2) responses associated with salient sensory stimuli, especially the outcome-predicting texture touch, that strengthen upon learning. We demonstrate that these response types match distinct unsigned components of the temporal difference error5 by replicating our results with a reinforcement learning model. Moreover, optogenetic apical inhibition of L5 neurons during the outcome time window prevents naive animals from learning the task, consistent with the effect of equivalent model perturbation. Our findings suggest that salience signals in L5 apical dendrites facilitate the recruitment of task-relevant neurons via dendritic gain modulation.

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bioRxiv2020

SIPEC: the deep-learning Swiss knife for behavioral data analysis

The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

neuroscience↗

bioRxiv2019

Neighborhood-statistics reveal complex dynamics of song acquisition in the zebra finch

Motor behaviors are continually shaped by a variety of processes such as environmental influences, development, and learning1,2. The resulting behavioral changes are commonly quantified based on hand-picked features3-10 (e.g. syllable pitch11) and assuming discrete classes of behaviors (e.g. distinct syllables)3-5,9,10,12-17. Such methods may generalize poorly across behaviors and species and are necessarily biased. Here we present an account of behavioral change based on nearest-neighbor statistics18-23 that avoids such biases and apply it to song development in the juvenile zebra finch3. First, we introduce the concept of repertoire dating, whereby each syllable rendition is dated with a \"pseudo\" production-day corresponding to the day when similar renditions were typical in the behavioral repertoire. Differences in pseudo production-day across renditions isolate the components of vocal variability congruent with the long-term changes due to vocal learning and development. This variability is large, as about 10% of renditions have pseudo production-days falling more than 10 days into the future (anticipations) or into the past (regressions) relative to their actual production time. Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a behavioral trajectory, which reproduces the pairwise similarities between renditions grouped by production time and pseudo production-day24. The behavioral trajectory reveals multiple, previously unrecognized components of behavioral change operating at distinct time-scales. These components interact differently across the behavioral repertoire--diurnal change in regressions undergoes only weak overnight consolidation4,5, whereas anticipations and typical renditions consolidate fully2,6,25. Our nearest-neighbor methods yield model-free descriptions of how behavior evolves relative to itself, rather than relative to a potentially arbitrary, experimenter-defined, goal3-5,11. Because of their generality, our methods appear well-suited to comparing learning across behaviors and species1,26-32, and between biological and artificial systems.

neuroscience↗

bioRxiv2019

Spatial and temporal structure of choice representations in primate prefrontal cortex

Divergent accounts of how choices are represented by neural populations have led to conflicting explanations of the underlying mechanisms of decision-making, ranging from persistent, attractor-based dynamics to transient, sequence-based dynamics. To evaluate these mechanisms, we characterize the spatial and temporal structure of choice representations in large neural populations in prefrontal cortex. We find that the pronounced diversity of choice responses across neurons reflects only a few, mostly persistent population patterns recruited at progressively later times before and after a choice. Brief sequential activity occurs during a saccadic choice, but is entirely absent in a delay preceding it. The diversity of choice responses, which could result from almost-random connectivity in the underlying circuits, instead largely reflects the topographical arrangement of response-field properties across the cortical surface. This spatial organization appears to form a fixed scaffold upon which the context-dependent representations of task-specific variables often observed in prefrontal cortex can be learned.

neuroscience↗