About

The de Bivort lab at Harvard University seeks to understand how genetics and brain circuits affect unique behaviors in the context of evolution. Individual differences in behavior are a substrate for animal evolution, the essence of personality and individuality, and part of the mystery of who is afflicted by a psychological disorder and who is not. Our research aims to discover the neural circuitry and genetic and evolutionary mechanisms underlying behavioral variation using novel behavioral instruments and computational ethology. We are particularly curious about behavioral differences that exist even when genes and environment are matched, as well as ecologically important behaviors in natural contexts.

Research topics

• Computer Science
• Biology
• Artificial Intelligence
• Psychology
• Neuroscience
• Social psychology
• Evolutionary biology
• Computer Security
• Genetics
• Cognitive science
• Demography
• Cognitive psychology

Selected publications

• Family-based selection: an efficient method for increasing phenotypic variability

  G3 Genes Genomes Genetics · 2025-07-18

  articleOpen accessSenior author

  Persistent idiosyncrasies in behavioral phenotypes have been documented across animal taxa. These individual differences among organisms from the same genotype and reared in identical environments can result in phenotypic variability in the absence of genetic variation. While there is strong evidence to suggest that variability of traits can be heritable and determined by the genotype of an organism, little is known about how selection can specifically shape this heritable variance. Here, we describe a Python-based model of directional artificial selection for increasing the variability of a polygenic trait of interest. Specifically, our model focuses on variability in left-vs-right turn bias in Drosophila melanogaster. While the mean value of turn bias for a genotype is non-heritable and constant across genotypes, the variability of turn bias is a heritable and polygenic trait, varying dramatically among different genetic lines. Using our model, we compare different selection regimes and predict selection dynamics at population and genetic levels. We find that introducing population structure via a family-based selection regime can significantly affect selection response. When selection for increased variability is implemented on the basis of independently measured traits of individuals, the response is slower, but leads to a population with a greater genetic diversity. In contrast, when selection is implemented by measuring traits of families with half or full siblings, the response is faster, albeit with a reduced final genetic diversity in the population. Our model provides a useful starting point to study the effect of different selection regimes on any polygenic trait of interest. We can use this model to predict responses of laboratory-based selection experiments and implement feasible experiments for selection of complex polygenic traits in the laboratory.

  PublisherOA PDFDOI

• The Developmental Origins of Behavioral Individuality

  Annual Review of Cell and Developmental Biology · 2025-06-06 · 5 citations

  reviewOpen access1st authorCorresponding

  Every individual animal behaves differently, even if they have the same genome and have been raised in the same environment. This diversity in behavior challenges the notion that biological variation derives solely from differences in genetics and environment, and poses the question of what biological processes generate individuality. At very small scales, the dynamics of biological matter are essentially impossible to predict with certainty, and these stochastic fluctuations can ripple out to alter the metabolism, physiology, and behavior of cells and organisms. I review major findings related to the developmental origins of stochastic individuality. These include the multivariate, dynamic organization of individual behavioral differences; control of the extent of individuality by genes, neural activity, and neuromodulation; nanoscale features of neural circuits that predict behavioral biases at the individual level; and experimental and theoretical evidence that behavioral variability may reflect an adaptive bet-hedging strategy. I conclude with a brief discussion of how large datasets like connectomes and long-term behavioral recordings will inform our understanding of the mechanisms underpinning behavioral individuality.

  PublisherDOI

• Permeabilization with fenchone enhances cryopreservation of <i>Drosophila</i> embryos

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-08

  preprintOpen accessSenior author

  Abstract The difficulty of cryopreservation has long been a limitation of Drosophila melanogaster as a genetic model organism. Here we report a statistically significant improvement in the efficiency of Drosophila cryopreservation by substituting limonene with the monoterpenoid fenchone in the embryo permeabilization step of a previously published method. We found that fenchone-permeabilized embryos exhibit greater uptake of cryoprotectant compared with those permeabilized by limonene, and a ~6-fold increase in the rate of egg-to-adult survival for wild-type flies. Using this improved protocol, we successfully cryopreserved and revived precious strains after 12 months of storage in liquid nitrogen. These results suggest that fenchone is a superior permeabilizing agent for fly embryo cryopreservation, expanding possibilities for the long-term maintenance of Drosophila and other insect species. Further refinement of this approach may enable cryopreservation to replace continuous culture as the method of choice for routine maintenance of fly stocks.

  PublisherOA PDFDOI

• Author response: A neural correlate of individual odor preference in Drosophila

  2025-01-21

  peer-reviewOpen accessSenior author

  Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3,062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.

  PublisherOA PDFDOI

• A neural correlate of individual odor preference in Drosophila

  eLife · 2025-03-11 · 6 citations

  articleOpen accessSenior author

  Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.

  PublisherDOI

• The larval <i>Drosophila</i> mushroom body balances lateralized sensing and interhemispheric integration

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-02

  preprintOpen access

  larva, olfactory receptor neurons project largely ipsilaterally, providing a tractable system for asking where and how interhemispheric integration arises downstream. We combined volumetric calcium imaging with unilateral sensory perturbations, connectomic analysis, and optogenetic manipulations to trace the propagation of left-right olfactory information across successive layers of the olfactory system. This approach implicates the mushroom body (MB) as a key substrate for interhemispheric integration of odor representations. Kenyon cell (KC) odor responses were almost entirely ipsilateral, indicating minimal functional coupling between the two MBs at the input level. In contrast, modulatory neurons (MBINs) exhibited highly symmetric responses to unilateral stimulation, suggesting that reinforcement signals are broadly shared across hemispheres. Nevertheless, odor responses in some MB output neurons (MBONs), up to 5 synapses downstream from the sensory periphery, preserve information about stimulus laterality. Moreover, we show that asymmetric activation of these MBONs can modulate the animal's turning behavior in a side-biased manner. Finally, we provide direct evidence that larvae can exploit instantaneous spatial comparisons for navigation in certain sensory contexts. These findings suggest that the deeply lateralized architecture of the larval olfactory system balances the need for interhemispheric integration with the advantages of parallel sensory processing.

  PublisherOA PDFDOI

• A neural correlate of individual odor preference in Drosophila

  eLife · 2025-01-21 · 1 citations

  preprintOpen accessSenior author

  Abstract Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3,062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.

  PublisherDOI

• Distributed control circuits across a brain-and-cord connectome

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-01 · 22 citations

  preprintOpen access

  Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms 1-3 and sea squirts4 (10 3 -10 4 synapses). By contrast, the fruit fly is more complex (10 8 synaptic connections), with a brain that supports learning and spatial memory 5,6 and an intricate ventral nerve cord analogous to the vertebrate spinal cord 7-11 . Here we report the first densely reconstructed adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector neurons (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by sensory neurons in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with the endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied, reminiscent of distributed control architectures in engineered systems 12,13 .

  PublisherOA PDFDOI

• Multiple weak biases support adaptive choices without prior experience: a self-supervised strategy

  2025-06-24

  preprintOpen accessSenior author

  The fitness of newborn animals – from approaching social partners to foraging – depends on their ability to make adaptive decisions in the absence of prior experience. Without prior experience, decisions can be guided by biases shaped through evolution. However, innate biases expose individuals to the risk of errors. For instance, a newborn with an innate preference for maternal colours may mistakenly approach irrelevant misleading objects that share the features of target stimuli. Which strategies newborn animals use to minimise the costs of biases remains unknown. To address this issue, we modelled a self-supervised strategy where inexperienced animals leverage their innate internal biases and the information present in the environment to maximise adaptive choices. Our model provides a set of testable predictions. First, innate biases tend to focus on cues that are rare in the background but frequent in the target stimuli (e.g., red colour), thus reducing false positives. Second, the evolution of multiple biases enables animals to benefit from the presence of co-occurring cues (e.g., the co-occurrence of red colour, movement against gravity and a face-like pattern present in mother hen) for more robust identification of relevant stimuli. This combination supports the emergence of weak biases, whose weakness reduces the risk of wrong choices for single stimuli that partially resemble target objects. The presence of multiple weak biases is particularly advantageous in complex environments where multiple stimuli are present. Overall, a strategy to requires the simultaneous co-occurrence of independent and rare stimuli can explain the occurrence of multiple weak biases observed in newborn animals. This simple self-supervised strategy can support effective choices in both biological and artificial minds, with applications from animal cognition to developmental psychology and artificial intelligence.

  PublisherOA PDFDOI

• Family-based Selection: An Efficient Method for Increasing Phenotypic Variability

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-06

  preprintOpen accessSenior authorCorresponding

  Abstract Persistent idiosyncrasies in behavioral phenotypes have been documented across animal taxa. These individual differences among organisms from the same genotype and reared in identical environments can result in phenotypic variability in the absence of genetic variation. While there is strong evidence to suggest that variability of traits can be heritable and determined by the genotype of an organism, little is known about how selection can specifically shape this heritable variance. Here, we describe a Python-based model of directional artificial selection for increasing the variability of a polygenic trait of interest. Specifically, our model focuses on variability in left-vs-right turn bias in D. melanogaster . While the mean value of turn bias for a genotype is non-heritable and constant across genotypes, the variability of turn bias is a heritable and polygenic trait, varying dramatically among different genetic lines. Using our model, we compare different selection regimes and predict selection dynamics at population and genetic levels. We find that introducing population structure via a family-based selection regime can significantly affect selection response. When selection for increased variability is implemented on the basis of independently measured traits of individuals, the response is slower, but leads to a population with a greater genetic diversity. In contrast, when selection is implemented by measuring traits of families with half or full siblings, the response is faster, albeit with a reduced final genetic diversity in the population. Our model provides a useful starting point to study the effect of different selection regimes on any polygenic trait of interest. We can use this model to predict responses of laboratory-based selection experiments and implement feasible experiments for selection of complex polygenic traits in the lab.

  PublisherDOI

Recent grants

• Discovering the Mechanistic Basis of Individual Differences in Sensory Representation and their Effects on Preference Behavior

  NSF · $675k · 2016–2019

Frequent coauthors

• M. A. Smith

  70 shared

• Ruixuan Gao

  University of Illinois Urbana-Champaign

  47 shared

• Jamilla Akhund‐Zade

  43 shared

• Edward S. Boyden

  McGovern Institute for Brain Research

  36 shared

• Matthew A. Churgin

  Harvard University

  33 shared

• Danylo Lavrentovich

  Center for Pain and the Brain

  32 shared

• Glenn Turner

  Howard Hughes Medical Institute

  27 shared

• Erika Gajda

  Harvard University

  24 shared

Labs

• de Bivort LabPI

  The de Bivort lab is made up of a group of talented scientists, postdoctoral fellows, and students seeking to understand the evolution of behavior variability.