Biology subjects

Loewinger, G.

Publications and source records attributed to Loewinger, G..

6 recordsLinked to original sources

bioRxiv2026

Altered thalamo-prefrontal synchrony dynamics during spatial working memory task performance in a SETD1A loss-of-function mouse model of schizophrenia predisposition

Schizophrenia is associated with profound working memory deficits, for which there are no approved treatments. Rare heterozygous null mutations in SETD1A, a gene encoding a key epigenetic regulatory protein, have been definitively linked to increased risk for schizophrenia and neurodevelopmental disorders. To investigate how SETD1A haploinsufficiency impacts the function of circuits supporting working memory, this study examined neural oscillatory synchrony across a network of brain regions critical for spatial working memory (SWM) in mice carrying a loss-of-function allele in the orthologous SETD1A gene. Local field potential recordings were performed in the prefrontal cortex, dorsal and ventral hippocampus, and thalamic nucleus reuniens in male and female wildtype and Setd1a+/- mice performing a delayed non-match to sample task of SWM. Setd1a+/- mice exhibited unaltered prefrontal-hippocampal neural oscillatory synchrony across frequencies and task epochs. In contrast, Setd1a+/- mice displayed reduced beta-frequency synchrony between the prefrontal cortex and nucleus reuniens during SWM maintenance and blunted bidirectional modulation of prefrontal-reuniens beta- and gamma-frequency synchrony across SWM task epochs. Collectively, this work expands our understanding of how genetic risk for schizophrenia alters functional connectivity within distributed circuits supporting SWM. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/702577v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@8107acorg.highwire.dtl.DTLVardef@11ec407org.highwire.dtl.DTLVardef@d7ce18org.highwire.dtl.DTLVardef@1bb31f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Capturing instantaneous neural signal-behavior relationships with concurrent functional mixed models

We previously proposed an analysis framework for fiber photometry data based on functional linear mixed models (FLMMs). Functional LMMs allow modeling associations between photometry traces and trial-specific scalar values like behavioral summaries and session number, while also accounting for between-animal heterogeneity. Here, we extend the method to concurrent FLMMs (cFLMMs), a method that can fit the instantaneous relationship between functional outcomes and functional covariates. Concurrent FLMMs enable testing of how the photometry signal is associated with, for example, a behavioral variable that evolves across within-trial timepoints (e.g. animal speed). cFLMMs can also model the relationship between the photometry signal and covariates in experiments with variable trial lengths (e.g., in studies where trials end when an animal responds). We illustrate the application of cFLMMs on two published studies and show the method can identify signal-behavior associations in analyses not possible with FLMMs. We find that analyzing photometry-behavior associations based on behavioral summaries (e.g., latency-to-response, average lick rate) can lead to misleading conclusions. We published our method in the fastFMM package, available as an R package through GitHub (https://github.com/awqx/fastFMM).

neuroscience↗

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↗

bioRxiv2025

The encoding of interoceptive-based predictions by the paraventricular nucleus of the thalamus D2+ neurons

Understanding how the brain integrates internal physiological states with external sensory cues to guide behavior is a fundamental question in neuroscience. This process relies on interoceptive predictions, which are internal models that anticipate changes in the bodys physiological state based on sensory inputs and prior experiences. Despite recent advances in identifying the neural substrates of interoceptive predictions, the precise neuronal circuits involved remain elusive. In our study, we demonstrate that Dopamine 2 Receptor (D2R+) expressing neurons in the paraventricular nucleus of the thalamus (PVT) play key roles in interoception and interoceptive predictions. Specifically, these neurons are engaged in behaviors leading to physiologically relevant outcomes, with their activity highly dependent on the interoceptive state of the mice and the expected outcome. Furthermore, we show that chronic inhibition of PVTD2R+ neurons impairs the long-term performance of interoceptive-guided motivated behavior. Collectively, our findings provide insights into the role of PVTD2R+ neurons in learning and updating state-dependent predictions by integrating past experiences with current physiological conditions to optimize goal-directed behavior.

neuroscience↗

bioRxiv2023

A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.

neuroscience↗

bioRxiv2021

Surprise-induced enhancements in the associability of Pavlovian cues facilitate learning across behavior systems

Surprising violations of outcome expectancies have long been known to enhance the associability of Pavlovian cues; that is, the rate at which the cue enters into further associations. The adaptive value of such enhancements resides in promoting new learning in the face of uncertainty. However, it is unclear whether associability enhancements reflect increased associative plasticity within a particular behavior system, or whether they can facilitate learning between a cue and any arbitrary outcome, as suggested by attentional models of conditioning. Here, we show evidence consistent with the latter hypothesis. Violating the outcome expectancies generated by a cue in an appetitive setting (feeding behavior system) facilitated subsequent learning about the cue in an aversive setting (defense behavior system). In addition to shedding light on the nature of associability enhancements, our findings offer the neuroscientist a behavioral tool to dissociate their neural substrates from those of other, behavior system- or valence-specific changes. Moreover, our results present an opportunity to utilize associability enhancements to the advantage of counterconditioning procedures in therapeutic contexts.

animal behavior and cognition↗