Biology subjects

Kampa, B. M.

Publications and source records attributed to Kampa, B. M..

4 recordsLinked to original sources

bioRxiv2024

Comprehensive Analysis of Human Dendritic Spine Morphology and Density: Exploring Diversity of Human Dendritic Spines

Dendritic spines, small protrusions on neuronal dendrites, play a crucial role in brain function by changing shape and size in response to neural activity. So far, in depth analysis of dendritic spines in human brain tissue is lacking. This study presents a comprehensive analysis of human dendritic spine morphology and density using a unique dataset from human brain tissue from 27 patients (8 females, 19 males, aged 18-71) undergoing tumor or epilepsy surgery at three neurosurgery sites. We used acute slices and organotypic brain slice cultures to examine dendritic spines, classifying them into the three main morphological subtypes: Mushroom, Thin, and Stubby, via 3D reconstruction using ZEISS arivis Pro software. A deep learning model, trained on 39 diverse datasets, automated spine segmentation and 3D reconstruction, achieving a 74% F1-score and reducing processing time by over 50%. We show significant differences in spine density by sex, dendrite type, and tissue condition. Females had higher spine densities than males, and apical dendrites were denser in spines than basal ones. Acute tissue showed higher spine densities compared to cultured human brain tissue. With time in culture, Mushroom spines decreased, while Stubby and Thin spine percentages increased, particularly from 7-9 to 14 days in vitro, reflecting potential synaptic plasticity changes. Our study underscores the importance of using human brain tissue to understand unique synaptic properties and shows that integrating deep learning with traditional methods enables efficient large-scale analysis, revealing key insights into sex- and tissue-specific dendritic spine dynamics relevant to neurological diseases. New and NoteworthyThis study presents a dataset of nearly 4,000 morphologically reconstructed human dendritic spines across different ages, gender, and tissue conditions. The dataset was further used to evaluate a deep learning algorithm for three-dimensional spine reconstruction, offering a scalable method for semi-automated spine analysis across various tissues and microscopy setups. The findings enhance understanding of human neurology, indicating potential connections between spine morphology, brain function, and the mechanisms of neurological and psychiatric diseases.

neuroscience↗

bioRxiv2022

Immediate reuse of patch-clamp pipettes with ultrasonic cleaning

The patch-clamp technique has revolutionized neurophysiology by allowing to study single neuronal excitability, synaptic connectivity, morphology, and the transcriptomic profile. However, the throughput in recordings is limited because of the manual replacement of patch-pipettes after each attempt which are often also unsuccessful. This has been overcome by automated cleaning the tips in detergent solutions, allowing to reuse the pipette for further recordings. Here, we developed a novel method of automated cleaning by sonicating the tips within the bath solution wherein the cells are placed, reducing the risk of contaminating the bath solution or internal solution of the recording pipette by any detergent and avoiding the necessity of a separate chamber for cleaning. We showed that the patch-pipettes can be used consecutively at least 10 times and that the cleaning process does not negatively impact neither the brain slices nor other patched neurons. This method, combined with automated patch-clamp, highly improves the throughput for single and especially multiple recordings.

neuroscience↗

bioRxiv2020

MazeMaster: an open-source Python-based software package for controlling virtual reality experiments

AO_SCPLOWBSTRACTC_SCPLOWIn the last 15 years, virtual realities have revolutionized behavior experiments in particular for rodents. In combination with treadmills, running wheels, or air-floating balls, the implementation of a virtual reality (VR) provides not only the opportunity to simultaneously explore behavior and neuronal activity in head-fixed animals under nearly natural conditions, but also allows full control over the visual sensory input presented to the animal. Furthermore, VRs can be combined with other sensory modalities such as auditory, tactile or olfactory stimuli. Despite the power of using VRs in animal experiments, available software packages are very limited, expensive and lack the required flexibility to design appropriate behavior and neurophysiology experiments. For this reason, we have developed the versatile, adaptable and easy to use VR environment MazeMaster, an open-source, Python-based software package for controlling virtual reality setups and behavior experiments. The software package includes a graphical user interface (GUI) and can be integrated into standard electrophysiology and imaging setups even by non-programmers. Ready-made behavioral experiments such as multisensory discrimination in T-mazes are already implemented including full control for reward supply and bias correction. For more individual setup designs, the modularity of MazeMaster allows more programming-affine users to extend the software with potentially missing features. With MazeMaster, we offer a free and easy-to-use VR controller that will facilitate the implementation of VR setups in scientific laboratories. In addition, MazeMaster allows the design and control of common head-fixed rodent behavior paradigms with extensive acquisition of meta-data required for reproducible VR experiments. The MazeMaster VR package, therefore, offers a collaboration tool for reproducible research within and across neuroscience laboratories according to the FAIR principles.

neuroscience↗

bioRxiv2018

Pupillary dilations of mice performing a vibrotactile discrimination task reflect task engagement and response confidence.

Pupillometry, the measure of pupil size and reactivity, has been widely used to assess cognitive processes. As such, changes in pupil size have been shown to correlate with arousal, locomotion, cortical state and decision-making processes. In addition, pupillary responses have been linked to the activity of neuromodulatory systems that modulate attention and perception as the noradrenergic and cholinergic systems. Due to the extent of processes reflected by the pupil, we aimed at resolving pupillary responses in context of behavioral state and task performance while recording pupillary transients of mice performing a vibrotactile two-alternative forced choice task (2-AFC). We show that pre-stimulus pupil size differentiates between states of disengagement from task performance versus active engagement. In addition, when actively engaged, post-stimulus, pupillary dilations for correct responses are larger than for error responses with this difference reflecting response confidence. Importantly, in a delayed 2-AFC task version, we show that even though pupillary transients mainly reflect motor output or reward anticipation following the response of the animal, they also reflect animal decision confidence prior to its response. Finally, in a condition of passive engagement, when stimulus has no task relevance with reward provided automatically, pupillary dilations reflect stimulation and reward being reduced relative to a state of active engagement explained by shifts of attention from task variables. Our results provide further evidence for how pupillary dilations reflect cognitive processes in a task relevant context, showing that the pupil reflects response confidence and baseline pupil size encodes attentiveness rather than general arousal.\n\nSignificance StatementFor the last 60 years, pupillometry has been used to study various cognitive processes. Among which are mental load, arousal and various decision related components, linking pupil dilations to underlying neuromodulatory systems. Our results provide extensive evidence that in addition to reflecting attentiveness under task performance, pupil dilations also reflect the confidence of the subject in his ensuing response. This confidence coding is overlaid within a more pronounced pupil dilation that reflects motor output or other post-decision components such that are related to the response itself but not to the decision. Our results also provide evidence how different behavioral states, imposed by task demands, modulate what the pupil is reflecting, presumably showing what the underlying cognitive network is coding for.

neuroscience↗