Biology subjects

Branson, K.

Publications and source records attributed to Branson, K..

11 recordsLinked to original sources

bioRxiv2026

Mus musculus mice self-organize existing behaviors into structured bouts as they become expert hunters.

In the wild, a mouse must flexibly perform multiple computations at once, rapidly navigating, sampling its environment, forming and using memories, and balancing internal needs. Capturing the self-paced, low-repetition, complex nature of decision making in the wild while preserving the experimental control needed to interpret its process remains a challenge. Here we show that lab mice, without food restriction, learn to forage for hidden resources cued by ambiguous sounds in a dynamic environment by spatiotemporally reorganizing their existing behavioral repertoire. Using a novel closed-loop assay, we required mice to sit still to receive informative but hard-to-localize sound cues emanating from one correct location they must find, out of 157 possible locations in a large arena. With experience, mice become efficient hunters. Efficient hunting cannot be explained by an increase in a particular behavioral module, but instead, mice reorganize existing behaviors into clustered bouts of high-quality sampling and site-checking. Looking forward, this work establishes an approach for studying the neural, molecular, and evolutionary basis of naturalistic decision-making in mice.

animal behavior and cognition↗

bioRxiv2025

Networks of sexually dimorphic neurons that regulate social behaviors in Drosophila

Neural mechanisms underlying sexually dimorphic social behaviors remain enigmatic in most species. In Drosophila, sexually dimorphic P1/pC1x neurons have been described as a site of sensory integration that regulates mating and aggressive behaviors. We show that the male P1/pC1x population forms a highly intertwined network with male-specific mAL and aSP-a neurons that is poised to regulate male behavior. The 48 P1/pC1x cell types exhibit heterogeneous synaptic connections with a subset receiving strong input from identified sensory pathways. We also describe circuit motifs by which P1 and sexually dimorphic aIPg neurons co-regulate social behaviors. Genetic driver lines for these cell types were generated and used to discover distinct roles for P1/pC1x cell types in promoting social acoustic signaling and male-male interactions. Our results reveal unexpected diversity in the connectivity and behavioral roles of the P1/pC1x cell types and provide essential genetic tools for interrogating their neurophysiological and behavioral functions.

neuroscience↗

bioRxiv2025

Cortical control of innate behavior from subcortical demonstration

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1-3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5-7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Naive mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.

neuroscience↗

bioRxiv2024

The Fly Disco: Hardware and software for optogenetics and fine-grained fly behavior analysis

In the fruit fly, Drosophila melanogaster, connectome data and genetic tools provide a unique opportunity to study complex behaviors including navigation, mating, aggression, and grooming in an organism with a tractable nervous system of 140,000 neurons. Here we present the Fly Disco, a flexible system for high quality video collection, optogenetic manipulation, and fine-grained behavioral analysis of freely walking and socializing fruit fly groups. The data collection hardware and software automates the collection of videos synced to programmable optogenetic stimuli. Key pipeline features include behavioral analysis based on trajectories of 21 keypoints and optogenetic-specific summary statistics and data visualization. We created the multifly dataset for pose estimation that includes 9701 examples enriched in complex behaviors. All hardware designs, software, and the multifly dataset are freely available.

neuroscience↗

bioRxiv2024

Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological images

BackgroundRecent advancements in computational pathology have introduced deep learning methods to predict genomic, transcriptomic and molecular biomarkers from routine histology whole slide images (WSIs) for cancer diagnosis, prognosis, and treatment. However, existing methods often overlook the critical role of co-dependencies among biomarker statuses during training and inference. We hypothesize that this oversight results in models that predict the combined effect of multiple interdependent biomarkers rather than individual statuses independently, akin to attributing the quality of an orchestral symphony to a single instrument, highlighting limitations of current predictors. MethodsUsing large datasets (n = 8,221 patients), we conducted statistical co-dependence testing to demonstrate significant interdependencies among biomarker statuses in training datasets. Following standard protocols, we trained two machine learning models to predict biomarkers from WSIs achieving or matching state-of-the-art predictive performance. We then employed permutation testing and stratification analysis to evaluate their predictive quality based on the principle of conditional independence, i.e., if a model accurately captures the phenotypic influence of a specific biomarker independent of other biomarkers, its performance should remain consistent across subgroups of patients stratified by other biomarkers, aligning with its overall performance on the entire dataset. FindingsOur statistical analysis reveals significant interdependencies among biomarkers, reflecting expected co-occurrence and mutual exclusivity patterns influenced by pathological and biological processes that are consistent across datasets, as well as sampling artefacts that can be different across datasets. Our results indicate that the predictive quality of an image-based predictor for a biomarker is contingent on the status of other biomarkers, revealing that models capture aggregated influences rather than predicting individual statuses independently. For example, mutation predictions are confounded by the overall tumour mutation burden. We also show that, due to the presence of such correlations, deep learning models may not offer significant advantages in predicting certain biomarkers in comparison to simply using pathologist-assigned grades for their prediction. InterpretationWe show that current deep learning models in computational pathology fall short in isolating individual biomarker effects, leading to confounded and less precise predictions. Our findings suggest revisiting model training protocols to recognize and adjust for biomarker interdependencies at all development stages--from problem definition to usage guidelines. This involves selecting diverse datasets to reflect clinical heterogeneity, defining prediction variables or grouping patients based on co-dependencies, designing models to disentangle complex relationships, and stringent stratification testing. Clinically, failure to account for interdependencies may lead to suboptimal decisions, necessitating appropriate usage guidelines for predictive models.

cancer biology↗

bioRxiv2023

Small-field visual projection neurons detect translational optic flow and support walking control

Animals rely on visual motion for navigating the world, and research in flies has clarified how neural circuits extract information from moving visual scenes. However, the major pathways connecting these patterns of optic flow to behavior remain poorly understood. Using a high-throughput quantitative assay of visually guided behaviors and genetic neuronal silencing, we discovered a region in Drosophilas protocerebrum critical for visual motion following. We used neuronal silencing, calcium imaging, and optogenetics to identify a single cell type, LPC1, that innervates this region, detects translational optic flow, and plays a key role in regulating forward walking. Moreover, the population of LPC1s can estimate the travelling direction, such as when gaze direction diverges from body heading. By linking specific cell types and their visual computations to specific behaviors, our findings establish a foundation for understanding how the nervous system uses vision to guide navigation.

neuroscience↗

bioRxiv2023

Cancer drug sensitivity prediction from routine histology images

Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomized controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression based mapping of drug effects on cancer cell lines to train a deep learning model that predicts sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients. HighlightsO_LIPredicting drug sensitivity from routine histology images and cell lines C_LIO_LIDiscovery of histology image patterns linked to drug sensitivity C_LIO_LIA novel deep learning pipeline for analysing drug sensitivity profiles C_LI

cancer biology↗

bioRxiv2023

Data-Driven Modelling of Gene Expression States in Breast Cancer and their Prediction from Routine Whole Slide Images

Identification of gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from (WSIs) using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinically and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining significant biological insights from routine pathology imaging directly. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/536756v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@74d0dcorg.highwire.dtl.DTLVardef@13c2708org.highwire.dtl.DTLVardef@26a6dborg.highwire.dtl.DTLVardef@194b076_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIData-driven discovery of co-expressing gene groups in breast caner C_LIO_LIHistological imaging based prediction of gene groups via deep learning C_LIO_LIIdentification of phenotypic correlates of gene-expression in histological imaging C_LIO_LIClinical and therapeutic impact of gene groups and their visual patterns identified C_LI

pathology↗

bioRxiv2019

The Pontine Nuclei are an Integrative Cortico-Cerebellar Link Critical for Dexterity

Skillful control of movement requires coordination between brain areas that are reciprocally connected through polysynaptic pathways, forming closed loops. A prominent loop in mammals runs between cerebral cortex and cerebellum, which individually contribute to skilled arm control. But how and why do these regions interact? Here, we studied the mouse cortico-cerebellar loop by optogenetically perturbing the pontine nuclei (PN), which receive direct cortical inputs and project only to cerebellum. PN stimulation during rest propagated into cerebellar cortex, but the effect of stimulation was transformed downstream into a wide range of patterns in the deep cerebellar nuclei (DCN) and reduced to transient excitation in motor cortex. PN stimulation in a cued reaching task altered arm kinematics and impaired performance. Cerebellar and cortical dynamics during movement were not dominated by PN stimulation, but altered in line with behavioral changes. These results suggest that the cortico-cerebellar loop fine-tunes motor commands during skilled reaching.

neuroscience↗

bioRxiv2018

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online.

biophysics↗

bioRxiv2018

The cortical dynamics orchestrating skilled prehension

Skillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

neuroscience↗