Biology subjects

Hochbaum, D. R.

Publications and source records attributed to Hochbaum, D. R..

5 recordsLinked to original sources

bioRxiv2026

Striatal activity maintains a short-term action-outcome memory to guide future choice

Adaptive behavior requires animals to use the outcomes of recent actions (action-outcomes) to guide future decisions. While the striatum is critical for decision-making, it is currently unclear how it is involved in the storage and retrieval of short-term associative memories. Here, we implemented a head-fixed memory-guided decision-making task in which mice use the outcome of a previous choice to determine whether to repeat or switch their next action. We show that dopamine fluctuations in the ventrolateral striatum are modulated by reward receipt or omission and recent outcome history, while the activity of direct and indirect pathway striatal projection neurons encodes recent action-outcome associations and predicts future switch/repeat choices. Closed-loop optogenetic activation and inhibition of direct and indirect pathway neurons during either the action-outcome association period or the delay preceding the next choice bidirectionally biased future actions away from those favored by reward history. Together, these findings suggest that striatal activity maintains a short-term action-outcome associative memory that links completed actions to future motor plans during adaptive decision-making.

neuroscience↗

bioRxiv2023

Thyroid hormone rewires cortical circuits to coordinate body-wide metabolism and exploratory drive

Animals adapt to varying environmental conditions by modifying the function of their internal organs, including the brain. To be adaptive, alterations in behavior must be coordinated with the functional state of organs throughout the body. Here we find that thyroid hormone-- a prominent regulator of metabolism in many peripheral organs-- activates cell-type specific transcriptional programs in anterior regions of cortex of adult mice via direct activation of thyroid hormone receptors. These programs are enriched for axon-guidance genes in glutamatergic projection neurons, synaptic regulators across both astrocytes and neurons, and pro-myelination factors in oligodendrocytes, suggesting widespread remodeling of cortical circuits. Indeed, whole-cell electrophysiology recordings revealed that thyroid hormone induces local transcriptional programs that rewire cortical neural circuits via pre-synaptic mechanisms, resulting in increased excitatory drive with a concomitant sensitization of recruited inhibition. We find that thyroid hormone bidirectionally regulates innate exploratory behaviors and that the transcriptionally mediated circuit changes in anterior cortex causally promote exploratory decision-making. Thus, thyroid hormone acts directly on adult cerebral cortex to coordinate exploratory behaviors with whole-body metabolic state.

neuroscience↗

bioRxiv2023

Sub-clinical triiodothyronine levels predict health, demographic, and socioeconomic outcomes

The Hypothalamic-Pituitary-Thyroid (HPT) axis is fundamental to human biology, exerting central control over energy expenditure, metabolic rate, and body temperature. However, the consequences of "normal" physiologic HPT-axis variation in non-clinical populations are poorly understood. Using nationally-representative data from the 2007-2012 NHANES, we explore relationships with demographics, mortality, and socio-economic factors. We find much larger variation across age in free T3 than other HPT-axis hormones. T3 and T4 have opposite effects on mortality: free T3 is inversely related and free T4 is positively related with likelihood of death. Free T3 and household income are negatively related, particularly at lower incomes. Finally, free T3 among older adults is associated with labor both on the extensive margin (unemployment) and intensive margin (hours worked). Physiologic TSH/T4 explain only 1% of T3 variation, and neither are appreciably correlated to socio-economic outcomes. Taken together, our data suggest an unappreciated complexity and non-linearity of the HPT-axis signaling cascade broadly such that TSH and T4 may not be accurate surrogates of free T3. Furthermore, we find that sub-clinical variation in the HPT-axis effector hormone T3 is an important and overlooked factor linking socio-economic forces, human biology, and aging.

physiology↗

bioRxiv2019

Riffle Shuffle: A Supervised, Symmetry-Driven, GUI Toolkit for Mouse Brain Stack Registration and Plane Assignment

Immunostaining of brain slices is a ubiquitous technique used throughout neuroscience for the purposes of understanding the anatomical and molecular characteristics of brain circuits. Yet the variety of distortions introduced, and the manual nature of the preparation, hinder the use of the generated images from being rigorously quantified; instead most registration of brain slices is done laboriously by hand. Existing automated registration methods rarely make use of geometric shape information. When registering anterior-posterior brain slices, for example, small errors between consecutive planes accumulate, causing the symmetry axis of a plane to drift away from its starting position as depth increases. Furthermore, planes with imaging artifacts - e.g. one half of the slice is missing - can cause large errors, which are difficult to fix by changing global parameters. In this work we describe a method in which we register a set of consecutive brain slices enforcing all slices to have a vertical axis of symmetry, and then pair these slices optimally to planes from the Allen Mouse Brain Atlas via Dynamic Programming. The pipeline offers multiple human-in-the-loop opportunities, allowing users to fix algorithmic errors in various stages, including symmetry detection and pairwise assignment, via custom graphical interfaces. This pipeline enables large-scale analysis of brain slices, allowing this common technique to be used to generate quantitative datasets.

bioinformatics↗

bioRxiv2017

Bots for Software-Assisted Analysis of Image-Based Transcriptomics

We introduce software assistants - bots - for the task of analyzing image-based transcriptomic data. The key steps in this process are detecting nuclei, and counting associated puncta corresponding to labeled RNA. Our main release offers two algorithms for nuclei segmentation, and two for spot detection, to handle data of different complexities. For challenging nuclei segmentation cases, we enable the user to train a stacked Random Forest, which includes novel circularity features that leverage prior knowledge regarding nuclei shape for better instance segmentation. This machine learning model can be trained on a modern CPU-only computer, yet performs comparably with respect to a more hardware-demanding state-of-the-art deep learning approach, as demonstrated through experiments. While the primary motivation for the bots was image-based transcriptomics, we also demonstrate their applicability to the more general problem of scoring \"spots\" in nuclei.

bioinformatics↗