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Dalal, T.

Publications and source records attributed to Dalal, T..

2 recordsLinked to original sources

Activity-dependent lateral inhibition enables ensemble synchronization of odor-activated neurons in the olfactory bulb

Information in the brain is represented by the activity of neuronal ensembles. These ensembles are adaptive and dynamic, formed and truncated based on the animals experience. One mechanism by which spatially distributed neurons form an ensemble is by synchronizing their spike times in response to a sensory event. In the olfactory bulb, odor stimulation evokes rhythmic gamma activity in spatially distributed mitral and tufted cells (MTCs). This rhythmic activity is thought to enhance the relay of odor information to the downstream olfactory targets. However, how specifically the odor-activated MTCs are synchronized is unknown. Here, we demonstrate that optogenetic activation of one set of MTCs can gamma-entrain the spiking activity of another set. This lateral synchronization was particularly effective when the recorded MTC fired at the gamma rhythm, facilitating the synchronization of only the substantially active MTCs. Furthermore, we show that lateral synchronization did not depend on the distance between the MTCs and is mediated by granule-cell layer neurons. In contrast, lateral inhibition between MTCs that reduced their firing rates was spatially restricted to adjacent MTCs and was not mediated by granule-cell layer neurons. This dissociation between these two interaction types suggests that they are mediated by different neural circuits. Our findings propose a simple yet robust mechanism by which spatially distributed neurons entrain each other spiking activity to form an ensemble. HighlightsO_LIMTC activation entrains the spike timing of other MTCs in an activity-dependent and distance-independent manner. C_LIO_LIMTC to MTC suppression is activity- and distance-dependent C_LIO_LISpatially distributed Granule cell layer neurons control MTCs spike timing, yet do not substantially affect their odor-evoked firing rate. C_LI

neuroscience↗

PYPE: A Python pipeline for phenome-wide association (PheWAS) and mendelian randomization in investigator-driven phenotypes and genotypes of biobank data

MotivationPhenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover new and unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian Randomization (MR) can be harnessed to make causal statements about a pair of phenotypes (e.g., does one phenotype cause the other?) by comparing the genetic architecture of the phenotypes in question. Thus, approaches that automate both PheWAS and MR can enhance biobank scale analyses, circumventing the need for multiple bespoke tools for each task by providing a comprehensive, end-to-end pipeline to drive scientific discovery. ResultsWe present PYPE, a Python pipeline for running, visualizing, and interpreting PheWAS. Our pipeline allows the researcher to input genotype or phenotype files from the UK Biobank (UKBB) and automatically estimate associations between the chosen independent variables and the phenotypes. PYPE also provides a variety of visualization options including Manhattan and volcano plots and can be used to identify nearby genes and functional consequences of the significant associations. PYPE additionally provides the user with the ability to run Mendelian Randomization (MR) under a variety of causal effect modeling scenarios (e.g., Inverse Variance Weighted Regression, Egger Regression, and Weighted Median Estimation) to identify possible causal relationships between phenotypes. Availability and ImplementationPYPE is a free, open-source project developed entirely in Python and can be found at https://github.com/TaykhoomDalal/pype. PYPE is published under the Apache 2.0 license and supporting documentation can be found at the aforementioned link. ContactChirag_Patel@hms.harvard.edu

bioinformatics↗