Biology subjects

Duffy, A.

Publications and source records attributed to Duffy, A..

8 recordsLinked to original sources

bioRxiv2026

Flexible neuronal participation within a reliable motor sequence

The reliable execution of learned motor sequences poses a challenge: they require reproducible neuron activity patterns for behavioral consistency yet must retain flexibility for modulation depending on internal states and adaptation to external contingencies. The zebra finch, a songbird, provides an ideal system to examine this problem because their vocalizations are highly stereotyped and are associated with precisely time-locked bursts in HVC, a key nucleus involved in song production. However, their songs are not immutable or totally fixed, as they can be modulated during social interactions and can recover after brain lesions. Here, we used calcium imaging of HVC projection neurons during freely produced songs to track activity across repeated renditions of the same song from seconds to weeks. We first confirmed that song-locked calcium-event timing was stable across days. Surprisingly, not every neuron showed a calcium event on every rendition of the song. We referred to the presence of such an event as participation. This participation was a stable, neuron-specific property that varied with the past and future renditions of the songs within an utterance and depended on social context. Song recovery after brain lesion and refinement during learning further revealed that neural participation could change while sequence timing remained stable. These results suggest that stable motor sequences need not rely on rigid repeated activation of the same cellular ensemble but can preserve temporal order while flexibly selecting which neurons contribute to each expression of the behavior. This provides a cellular solution to the reliability-flexibility tradeoff in learned motor control.

neuroscience↗

bioRxiv2024

Discovering plasticity rules that organize and maintain neural circuits

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Ojas rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

neuroscience↗

bioRxiv2023

Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons

Maintaining motor behaviors throughout life is crucial for an individuals survival and reproductive success. The neuronal mechanisms that preserve behavior are poorly understood. To address this question, we focused on the zebra finch, a bird that produces a highly stereotypical song after learning it as a juvenile. Using cell-specific viral vectors, we chronically silenced inhibitory neurons in the pre-motor song nucleus called the high vocal center (HVC), which caused drastic song degradation. However, after producing severely degraded vocalizations for around 2 months, the song rapidly improved, and animals could sing songs that highly resembled the original. In adult birds, single-cell RNA sequencing of HVC revealed that silencing interneurons elevated markers for microglia and increased expression of the Major Histocompatibility Complex I (MHC I), mirroring changes observed in juveniles during song learning. Interestingly, adults could restore their songs despite lesioning the lateral magnocellular nucleus of the anterior neostriatum (LMAN), a brain nucleus crucial for juvenile song learning. This suggests that while molecular mechanisms may overlap, adults utilize different neuronal mechanisms for song recovery. Chronic and acute electrophysiological recordings within HVC and its downstream target, the robust nucleus of the archistriatum (RA), revealed that neuronal activity in the circuit permanently altered with higher spontaneous firing in RA and lower in HVC compared to control even after the song had fully recovered. Together, our findings show that a complex learned behavior can recover despite extended periods of perturbed behavior and permanently altered neuronal dynamics. These results show that loss of inhibitory tone can be compensated for by recovery mechanisms partly local to the perturbed nucleus and do not require circuits necessary for learning.

neuroscience↗

bioRxiv2022

Unsupervised Restoration of a Complex Learned Behavior After Large-Scale Neuronal Perturbation

Reliable execution of behaviors requires that brain circuits correct for variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in a brain region involved in song production in adult songbirds with stereotypical songs triggered severe degradation of their songs. The song fully recovered within two weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic inputs to unmanipulated neurons in the same region. A model inspired by the behavioral and electrophysiological findings suggests that a combination of unsupervised single-cell and population-level homeostatic plasticity rules can support the observed functional restoration after large-scale disruption of networks implementing sequential dynamics. In the model the sequence is restored through a parallel homeostatic process, rather than regrown serially, and predicts that sequences should recover in a saltatory fashion. Correspondingly, we observed such recovery in the songs of manipulated animals, with syllables that rapidly alternate between abnormal and normal durations from rendition to rendition until eventually they permanently settled into their original length. These observations indicate the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.

neuroscience↗

bioRxiv2022

Precision motor timing via scalar input fluctuations

Complex motor skills like playing piano require precise timing over long periods, without errors accumulating between subprocesses like the left and right hand movements. While biological models can produce motor-like sequences, how the brain quenches timing errors is not well understood. Motivated by songbirds, where the left and right brain nuclei governing song sequences do not connect but may receive low-dimensional thalamic input, we present a model where timing errors in an autonomous sequence generator are continually corrected by one-dimensional input fluctuations. We show in a spiking neural network model how such input can rapidly correct temporal offsets in a propagating spike pulse, recapitulating the precise timing seen in songbird brains. In a reduced, more general model, we show that such timing correction emerges when the spatial profile of the input over the sequence sufficiently reflects its temporal fluctuations, yielding time-locking attractors that slow advanced sequences and hasten lagging ones, up to the input timescale. Unlike models without fluctuating input, our model predicts anti-correlated durations of adjacent segments of the output sequence, which we verify in recorded zebra finch songs. This work provides a bioplausible picture of how temporal precision could arise in extended motor sequences and generally how low-dimensional input could continuously coordinate time-varying output signals. SignificanceComplex motor skills like playing piano require precision timing over long periods, often among multiple components like left and right muscle groups. Although brain-like network models can produce motor-like outputs, timing regulation is not well understood. We introduce a model, inspired by songbird brains, where imprecise timing in a cortical-like system is corrected by a single thalamic input regulating the sequential propagation, or tempo, of cortical activity. This model illuminates a relation between the inputs spatial structure and temporal variation that lets lagging activity hasten and advanced activity slow, which makes a prediction about output timing that we verify in real birdsong. This work reveals a simple, neuroplausible mechanism that may play a role in precision cortical or motor timing.

neuroscience↗

bioRxiv2021

Dopamine Neurons Evaluate Natural Fluctuations in Performance Quality

Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown if dopamine also signals the quality of natural performance fluctuations. Here we recorded dopamine neurons in singing birds and examined how spontaneous dopamine spiking activity correlated with natural fluctuations in ongoing song. Antidromically identified basal ganglia-projecting dopamine neurons correlated with recent, and not future, song variations, consistent with a role in evaluation, not production. Furthermore, dopamine spiking was suppressed following the production of outlying vocal variations, consistent with a role for active song maintenance. These data show for the first time that spontaneous dopamine spiking can evaluate natural behavioral fluctuations unperturbed by experimental events such as cues or rewards.

neuroscience↗

bioRxiv2021

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

BackgroundPhenome-wide association studies conducted in electronic health record (EHR)-linked biobanks have uncovered a large number of genomic loci associated with traits and diseases. However, interpretation of the complex relationships of associated genes and phenotypes is challenging. ResultsWe constructed a tissue-level phenome-wide network map of colocalized genes and phenotypes. First, we generated colocalized expression quantitative trait loci from 48 tissues of the Genotype-Tissue Expression project and from publicly available genome-wide association study summary statistics from the UK Biobank. We identified 9,151 colocalized genes for 1,411 phenotypes across 48 tissues. Then, we constructed a bipartite network using the colocalized signals to establish links between genes and phenotypes in each tissue. The majority of links are observed in a single tissue whereas only a few are present in all tissues. Finally, we applied the biLouvain clustering algorithm in each tissue-specific bipartite network to identify co-clusters of non-overlapping genes and phenotypes. The majority of co-clusters contains a small number of genes and phenotypes, and 88.6% of co-clusters are found in only one tissue. To demonstrate functionality of the phenome-wide map, we tested if these co-clusters were enriched with known biological and functional gene classes and observed several significant enrichments. Furthermore, we observed that tissue-specific co-clusters are enriched with reported drug side effects for the corresponding drug target genes in clinical trial data. ConclusionsThe phenome-wide map provides links between genes, phenotypes and tissues across a wide spectrum of biological classes and can yield biological and clinical discoveries. The phenome-wide map is publicly available at https://rstudio-connect.hpc.mssm.edu/biPheMap/.

genomics↗

bioRxiv2018

Unsupervised Machine learning to subtype Sepsis-Associated Acute Kidney Injury

ObjectiveAcute kidney injury (AKI) is highly prevalent in critically ill patients with sepsis. Sepsis-associated AKI is a heterogeneous clinical entity, and, like many complex syndromes, is composed of distinct subtypes. We aimed to agnostically identify AKI subphenotypes using machine learning techniques and routinely collected data in electronic health records (EHRs).\n\nDesignCohort study utilizing the MIMIC-III Database.\n\nSettingICUs from tertiary care hospital in the U.S.\n\nPatientsPatients older than 18 years with sepsis and who developed AKI within 48 hours of ICU admission.\n\nInterventionsUnsupervised machine learning utilizing all available vital signs and laboratory measurements.\n\nMeasurements and Main ResultsWe identified 1,865 patients with sepsis-associated AKI. Ten vital signs and 691 unique laboratory results were identified. After data processing and feature selection, 59 features, of which 28 were measures of intra-patient variability, remained for inclusion into an unsupervised machine-learning algorithm. We utilized k-means clustering with k ranging from 2 - 10; k=2 had the highest silhouette score (0.62). Cluster 1 had 1,358 patients while Cluster 2 had 507 patients. There were no significant differences between clusters on age, race or gender. We found significant differences in comorbidities and small but significant differences in several laboratory variables (hematocrit, bicarbonate, albumin) and vital signs (systolic blood pressure and heart rate). In-hospital mortality was higher in cluster 2 patients, 25% vs. 20%, p=0.008. Features with the largest differences between clusters included variability in basophil and eosinophil counts, alanine aminotransferase levels and creatine kinase values.\n\nConclusionsUtilizing routinely collected laboratory variables and vital signs in the EHR, we were able to identify two distinct subphenotypes of sepsis-associated AKI with different outcomes. Variability in laboratory variables, as opposed to their actual value, was more important for determination of subphenotypes. Our findings show the potential utility of unsupervised machine learning to better subtype AKI.

bioinformatics↗