Biology subjects

Bauman, A.

Publications and source records attributed to Bauman, A..

3 recordsLinked to original sources

bioRxiv2025

Hippocampal input-driven plasticity of prefrontal interneurons reveals a circuit basis for impaired spatial working memory.

Long-range projections from ventral hippocampus (vHPC) to medial prefrontal cortex (mPFC) support cognitive functions including spatial working memory (SWM). vHPC input targets multiple prefrontal interneuron classes, yet how hippocampal input recruits these populations in vivo, whether this recruitment is plastic, and how such plasticity influences cognition remain unknown. Here, we combined optical stimulation of mouse vHPC inputs with calcium recordings from discrete mPFC interneuron populations, revealing persistent activity-induced reweighting of hippocampal recruitment across prefrontal inhibitory microcircuits. Ex vivo electrophysiology and computational modeling implicated weakened monosynaptic hippocampal drive onto vasoactive intestinal polypeptide (VIP)-expressing interneurons in this circuit reconfiguration. Prior vHPC input stimulation and the schizophrenia-associated Df(16)A+/- mutation, which also yielded reduced hippocampal input onto VIP interneurons, produced convergent alterations in task-related VIP interneuron activity associated with poorer SWM task learning. These findings implicate hippocampal input to VIP interneurons as a key locus of plasticity capable of reconfiguring inhibitory microcircuit activity linked to impaired working memory task learning. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/665987v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@e26cb0org.highwire.dtl.DTLVardef@14d9d6borg.highwire.dtl.DTLVardef@9a0fc2org.highwire.dtl.DTLVardef@80afd1_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

neuroscience↗

bioRxiv2024

An Infralimbic Cortex Neuronal Ensemble Encoded During Learning Attenuates Fear Generalization Expression

Generalization allows previous experience to adaptively guide behavior when conditions change. The infralimbic (IL) subregion of the ventral medial prefrontal cortex plays a known role in generalization processes, although mechanisms remain unclear. A basic physical unit of memory storage and expression in the brain are sparse, distributed groups of neurons known as ensembles (i.e., the engram). Here, we set out to determine whether neuronal ensembles established in the IL during learning contribute to generalized responses. Generalization was tested in male and female mice by presenting a novel, ambiguous, tone generalization stimulus following Pavlovian defensive (fear) conditioning. The first experiment was designed to test a role for IL in generalization using chemogenetic manipulations. Results show IL regulates defensive behavior in response to ambiguous stimuli. IL silencing led to a switch in defensive state, from vigilant scanning to generalized freezing, while IL stimulation reduced freezing in favor of scanning. Leveraging activity-dependent "tagging" technology (ArcCreERT2 x eYFP system), a neuronal ensemble, preferentially located in IL Layer 2/3, was associated with the generalization stimulus. Remarkably, in the identical discrete location, fewer reactivated neurons were associated with the generalization stimulus at the remote timepoint (30 days) following learning. When an IL neuronal ensemble established during learning was selectively chemogenetically silenced, generalization increased. Conversely, IL neuronal ensemble stimulation reduced generalization. Overall, these data identify a crucial role for IL in suppressing generalized responses. Further, an IL neuronal ensemble, formed during learning, functions to later attenuate the expression of generalization in the presence of ambiguous threat stimuli.

neuroscience↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗