Biology subjects

Brainard, M. S.

Publications and source records attributed to Brainard, M. S..

8 recordsLinked to original sources

bioRxiv2026

Gene regulatory co-option drives birdsong neural circuit specialization

As animals evolve complex motor skills, they acquire more diverse supporting motor circuits in their nervous systems. Yet the molecular mechanisms driving motor circuit evolution remain poorly understood. Birdsong, a learned complex motor skill with parallels to human speech, is controlled by a dedicated neural circuit - the song system - that is distinguished from nearby sensorimotor regions by molecular, physiological, and connectivity specializations. By profiling gene expression and chromatin accessibility in the songbird brain, we found that each projection neuron type in the song system has a molecularly similar sister neuron type in adjacent non-song regions; these sister neurons lack specialized gene expression and are transcriptionally similar to neurons in the chicken brain. The gene regulatory networks (GRNs) controlled by transcription factors MAFB and EMX2, typically active in fast-spiking interneurons and astrocytes, are specifically active in song-dedicated extratelencephalic projection neurons. Furthermore, the heterologous expression of MAFB or EMX2 in chicken projection neurons was sufficient to drive expression programs characteristic of song neurons. These results support a model in which song-dedicated neurons emerged from ancestral neural types in part through the co-option of GRNs active in other cellular contexts, providing a genetic mechanism underlying the evolution of birdsong.

neuroscience↗

bioRxiv2025

Repeated evolutionary turnover of vertebrate skeletal muscle myosins

Myosin heavy chain proteins are essential for muscle contraction and nearly every physiological function in animals, but their diversity and evolution outside mammals is largely unknown. We comprehensively model the evolutionary history of over 1100 heavy-chain myosins. We find that skeletal muscle myosins are located in a conserved tandem gene array in all vertebrate species, but repeated gene duplication-loss turnover has surprisingly led to an independently evolved set of core skeletal muscle myosins in each major vertebrate group. Despite these separate derivations of these myosin subfamilies, each major vertebrate group exhibits consistent tissue-specific patterns of subfamily expression and specialized myosin subfamily expression in extreme muscles. Our results show that muscle evolution across vertebrates is not based in conserved orthologous motor myosins, as might be expected for such a core structural protein family. Instead, we find that skeletal muscle myosins have evolved as a shifting cluster of genes that is constantly changing and diversifying to balance the need to maintain core physiology, while innovating new physiological possibilities.

genomics↗

bioRxiv2023

Lesions in a songbird vocal circuit increase variability in song syntax

Complex motor skills like speech and dance are composed of ordered sequences of simpler elements, but the neuronal basis for syntactic ordering of individual actions into sequences is poorly understood. Birdsong is a learned vocal behavior composed of syntactically ordered sequences of individual syllables. Activity in song premotor nucleus HVC (proper name) has been linked to the control of syllable sequencing, but sequencing may also be affected by its recurrent inputs. We here test the contribution of one of HVCs inputs, mMAN (medial magnocellular nucleus of the anterior nidopallium), to the variable songs of adult male Bengalese finches (Lonchura striata domestica). The syntax of Bengalese song includes several patterns: 1) chunks, where syllables follow stereotypical order 2) branch points, where a given syllable can be followed by two or more different syllables in a probabilistic manner and 3) repeat phrases, where an individual syllable is repeated a variable number of times. We found that after bilateral lesions of mMAN, the acoustic structure of syllables remained largely intact, but sequencing became more variable for each of these patterns, seen by breaks in previously stereotyped chunks, increased uncertainty at branch points and increased variability of repeat numbers. This increase in sequencing variability highlights the potential importance of regions projecting to HVC in the ordering of vocal elements. Previous studies on zebra finches found little effect of mMAN lesions on their relatively stereotyped adult song. In contrast, our results suggest that mMAN contributes to sequencing the variable songs of Bengalese finches and highlight the utility of species with more complex song syntax in investigating neuronal control of ordered motor sequences.

neuroscience↗

bioRxiv2022

Neural circuit-wide analysis of gene expression during deafening-induced destabilization ofbirdsong

Sensory feedback is required for stable execution of learned motor skills, and its loss can severely disrupt motor performance. The neural mechanisms that mediate sensorimotor stability have been extensively studied at systems and physiological levels, yet relatively little is known about how disruptions to sensory input alter the molecular properties of associated motor systems. Songbird courtship song, a model for skilled behavior, is a learned and highly structured vocalization that is destabilized following deafening. Here, we sought to determine how the loss of auditory feedback modifies gene expression and its coordination across the birdsong sensorimotor circuit. To facilitate this system-wide analysis of transcriptional responses, we developed a gene expression profiling approach that enables the construction of hundreds of spatially-defined RNA-sequencing libraries. Using this method, we found that deafening preferentially alters gene expression across birdsong neural circuitry relative to surrounding areas, particularly in premotor and striatal regions. Genes with altered expression are associated with synaptic transmission, neuronal spines, and neuromodulation and show a bias toward expression in glutamatergic neurons and Pvalb/Sst-class GABAergic interneurons. We also found that connected song regions exhibit correlations in gene expression that were reduced in deafened birds relative to hearing birds, suggesting that song destabilization alters the inter-region coordination of transcriptional state. Finally, lesioning LMAN, a forebrain afferent of RA required for deafening-induced song plasticity, had the largest effect on groups of genes that were also most affected by deafening. Combined, this integrated transcriptomics analysis demonstrates that the loss of peripheral sensory input drives a distributed gene expression response throughout associated sensorimotor neural circuitry and identifies specific candidate molecular and cellular mechanisms that support stability and plasticity of learned motor skills.

neuroscience↗

bioRxiv2022

Dynamic top-down biasing implements rapid adaptive changes to individual movements

Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN, in shaping moment-by-moment output from a primary motor area, RA. In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation between these areas. During learning, this cofluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.

neuroscience↗

bioRxiv2021

Heritable differences in synaptic zinc-transporter levels drive variation in learned birdsong

The authors have withdrawn this manuscript. During revision of our manuscript, we carried out several lines of additional experiments and analyses that failed to fully support and reproduce the results previously presented in Figure 4 (regarding potential mechanisms whereby zinc and SLC39all contribute to song tempo). We therefore do not want this result to be cited and are withdrawing our paper while we carry out additional experiments to clarify potential contributions of SLC39all to individual variation in song tempo. We apologize for any inconvenience caused to our readers. Any questions can be addressed to DGM (first/corresponding author) and/or MSB (last/senior author).

neuroscience↗

bioRxiv2018

A polymer probe-based system for high density, long-lasting electrophysiological recordings across distributed neuronal circuits

The brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

neuroscience↗

bioRxiv2017

An Automated Approach to the Quantitation of Vocalizations and Vocal Learning in the Songbird

Studies of learning mechanisms critically depend on the ability to accurately assess learning outcomes. This assessment can be impeded by the often complex, multidimensional nature of behavior. We present a novel, automated approach to evaluating imitative learning that is founded in information theory. Conceptually, our approach estimates the amount of information present in a reference behavior that is absent from the learned behavior. We validate our approach through examination of songbird vocalizations, complex learned behaviors the study of which has provided many insights into sensory-motor learning in general and vocal learning in particular. Historically, learning has been holistically assessed by human inspection or through comparison of specific song features selected by experimenters (e.g. fundamental frequency, spectral entropy). In contrast, our approach relies on statistical models that broadly capture the structure of each song, and then uses these models to estimate the amount of information in the reference song but absent from the learned song. We show that our information theoretic measure of song learning (contrast entropy) is well correlated with human evaluation of song learning. We then expand the analysis beyond song learning and show that contrast entropy also detects the typical song deterioration that occurs following deafening. More broadly, this approach potentially provides a framework for assessing learning across a broad range of similarly structured behaviors.\n\nAuthor SummaryMeasuring learning outcomes is a critical objective of research into the neural, molecular, and behavioral mechanisms that support learning. Demonstration that a given manipulation results in better or worse learning outcomes requires an accurate and consistent measurement of learning quality. However, many behaviors (e.g. speech, walking, and reading) are complex and multidimensional, confounding the assessment of learning. One behavior subject to such confounds, vocal learning in Estrildid finches, has emerged as a vital model for sensory motor learning broadly and human speech learning in particular. Here, we demonstrate a new approach, founded in information theory, to the assessment of learning for complex high dimensional behaviors. Conceptually, we determine the amount of information (across many dimensions) present in a reference behavior and then assess how much of that information is present in the resultant learned behavior. We show that this measure provides an accurate, holistic, and automated assessment of vocal learning in Estrildid finches. Potentially, this same approach could be deployed to assess shared content in any multidimensional data, behavioral or otherwise.

animal behavior and cognition↗