Biology subjects

Nemri, A.

Publications and source records attributed to Nemri, A..

4 recordsLinked to original sources

bioRxiv2026

Neural and Experiential Dynamics of High-Ventilation Breathwork Mirror Psychedelic States

High-ventilation breathwork (HVB) is a simple breathing technique that nevertheless frequently triggers experiences resembling psychedelic states. While recent studies have begun to pinpoint physiological and neural changes involved in altered states of consciousness (ASCs) evoked by HVB, the interplay between neural and experiential dynamics throughout an HVB session remains unclear. Here we tracked neural activity (via 64-channel EEG recordings) as well as subjective experience depth and content in 25 experienced HVB practitioners across resting-state recordings interleaved throughout a naturalistic HVB session. We find that neural dynamics evoked by HVB emulate the most robust neural signatures of classical psychedelic states, specifically (1) loosening of temporal structure, including broadband cortical desynchronization, increased signal complexity, and decreased oscillatory power in the Alpha (8 - 13 Hz) range, and (2) reorganization of spatial structure, including a flattening of cortical processing hierarchy. These neural effects directly correlated with participants subjective depth with minute resolution, but were not explained by isolated features of HVB such as music listening or active interoception. Together, these results identify unifying neural features of psychedelic-like ASCs regardless of induction method, demonstrating that cortical states triggered by psychedelics can also be accessed by non-pharmacological routes, and breathwork is a powerful way to do so.

neuroscience↗

bioRxiv2026

A partition-based spatial entropy for co-occurrence analysis with broad application.

Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy (RCE), that detects when categorical co-occurrences happen preferentially in specific environments. We demonstrate its use over a broad range of application fields. As examples, we study brain cell dynamics in Alzheimers Disease, identifying both known and likely novel interactions between immune cells around beta-amyloid plaques. We also investigate the diversity of buildings across a town neighborhoods, to detect potential drivers of social mixing at local scale. Finally, we dissect bird species distribution across a natural reserve, identifying potential vegetation-driven changes in community composition. Altogether, the proposed RCE enables rapid insights into interactions with an environmental component, making it a useful addition to the spatial data science toolbox. Significance StatementSpatial data is rapidly accumulating across fields as diverse as spatial biology, geography, ecology or astrophysics and promises to allow major scientific advances. For example, spatial biology is transforming our ability to study complex tissue organization and disease mechanisms. A key challenge remains the quantification of spatial relationships between individual points, such as cell types, in a statistically rigorous way. We propose a spatial entropy-based measure to quantify context-dependent interactions between categories, such as cell types across tissue subregions. Our novel method deriving from Information Science provides an efficient and versatile way to extract information from spatial datasets, with broad applicability across research fields.

bioinformatics↗

bioRxiv2024

The DREAM implant: A Lightweight, Modular and Cost-Effective Implant System for Chronic Electrophysiology in Head-fixed and Freely Behaving Mice

Chronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for chronically implanting probes present steep trade-offs between cost, ease of use, size, adaptability and long-term stability. SUMMARYIntroducing a lightweight, cost-effective probe implant system for chronic electrophysiology in rodents, optimized for ease of use, probe recovery, experimental versatility and compatibility with behavior. This protocol introduces a novel chronic probe implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps and explanted safely for recovery and multiple re-use of probes, significantly reducing experimental costs. The DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7mm; (2) a 3D-printable, open-source design for a wearable Faraday cage covered in copper mesh for electrical shielding, impact protection and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 hours), probe safety and animal welfare. The resulting implants had minimal impact on animals behavioral repertoire, were easily applicable in freely moving and head-fixed contexts and delivered clearly identifiable spike waveforms and healthy neuronal responses for weeks of data collection post-implant. Infections and other surgery complications were extremely rare. As such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being, and enabling more ethologically sound experiments. Its design simplifies experimental procedures across various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.

neuroscience↗

bioRxiv2021

Increasing calling accuracy, coverage, and read depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing technologies when resources are limited. In this work, we are proposing a new imputation pipeline ("HBimpute") that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and merge their reads locally. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced with 0.5X read-depth. Overall imputing error rates are cut in half compared to the state-of-the-art software BEAGLE, while the average read-depth is increased to 83X, thus enabling the calling of structural variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance in common breeding applications to that of genomic data from a 600k array. In particular for genome-wide association studies, the sequence data is shown to be performing slightly better. Furthermore, genomic prediction based on the overlapping markers from the array and sequence is leading to a slightly higher predictive ability for the imputed sequence data, thereby indicating that the data quality obtained from low read-depth sequencing is on par or even slightly higher than high-density array data. When including all markers for the sequence data, the predictive ability is slightly reduced indicating overall lower data quality in non-array markers. Author summaryHigh-throughput genotyping of large numbers of lines remains a key challenge in plant genetics and breeding. Cost, precision, and throughput must be balanced to achieve optimal efficiencies given available technologies and finite resources. Although genotyping arrays are still considered the gold standard in high-throughput quantitative genetics, recent advances in sequencing provide new opportunities for this. Both the quality and cost of genomic data generated based on sequencing are highly dependent on the used read depth. In this work, we are proposing a new imputation pipeline ("HBimpute") that uses haplotype blocks to detect individuals of the same genetic origin and subsequently uses all reads of those individuals in the variant calling. Thus, the obtained virtual read depth is artificially increased, leading to higher calling accuracy, coverage, and the ability to all copy number variation based on relatively cheap low-read depth sequencing data. Thus, our approach makes sequencing a cost-competitive alternative to genotyping arrays with the additional benefit of the potential use of structural variation.

genetics↗