motion

Increasing Lung Cancer Screening Uptake Among High-Risk Emergency Department Patients

PROJECT SUMMARY/ABSTRACT Lung cancer is the leading cause of cancer death in the US. Although lung cancer screening (LCS), using low- dose CT scan, decreases lung cancer mortality through early disease identification, fewer than 1 in 6 eligible individuals get screened, with significant differences based on demographic and socio-economic factors. LCS is a process, not just a test. The critical first steps in this process are (1) identification of high-risk individuals who are eligible for LCS, and (2) recruitment of these individuals into an LCS program. The Emergency Department (ED) setting is optimal for an intervention to promote LCS by accomplishing these steps. Individuals at high risk for lung cancer are over-represented in the ED population, including: individuals that smoke, non-White individuals, patients with lower education levels, and the under-insured. In fact, over 2.3 million high-risk people pass through EDs every year who are eligible for LCS but have never been screened. The investigators’ long-term goal is to develop a low-cost, scalable intervention that increases LCS uptake among ED patients and is deployable in any ED with a regionally referrable LCS program. The objective of the proposed randomized clinical trial is to test the efficacies of text messaging and a facilitated referral strategy to promote uptake of LCS in order to achieve this goal. Step 1 of the approach is to identify participants that are eligible for LCS. Step 2 is to randomize eligible participants, using a 2x2 design, among four study arms: (1) basic referral for LCS (i.e. verbal referral with written materials; comprising an enhanced control arm), (2) basic referral plus a subsequent series of text messages, grounded in behavioral change theory, aimed at generating intention and motivation to get screened, (3) facilitated referral for LCS (i.e. submission of a requisition to LCS program by staff), and (4) facilitated referral plus text messages. The investigators’ pilot work demonstrated the feasibility and efficacy of the proposed approach. A total of 1036 individuals eligible for LCS will be recruited from a high-volume urban ED and a low-volume rural ED, randomized among study arms, and followed-up at 120 days to assess interval LCS uptake. The Specific Aims of the proposed project are, (1) Compare LCS program uptake among study arms that receive text messages to study arms that do not, (2) Compare LCS program uptake among study arms with basic referral to study arms with facilitated referral, (3) Investigate the interaction between receipt of text messages (yes/no) and referral type (basic/facilitated), and (4) Evaluate participant feedback on (a) differential barriers to LCS across sub-groups and (b) acceptability and appropriateness of ED-based promotion of LCS. The study team is at the forefront of developing ED-based interventions to promote cancer screening. This project leverages the universal access setting of the ED to identify individuals at greatest risk for lung cancer and get them screened. A scalable ED-based intervention that increases LCS uptake would save lives.

Factors Driving Wear and Implant Failure in Total Shoulder Arthroplasty

Polyethylene (PE) wear and implant-related failure remain leading causes of revision in total shoulder arthroplasty (TSA), a procedure which now surpasses the growth rate of hip and knee arthroplasty. Both anatomic (aTSA) and reverse (rTSA) TSA outcomes are heavily influenced by complex interactions between rotator cuff function, scapular motion, implant design, and patient-specific loading—factors not adequately captured in current preclinical implant testing standards. Emerging evidence suggests that PE wear progression in TSA is highly dependent on shoulder kinematics, joint loading, implant positioning, and individual patient factors. Nonetheless, data on in vivo motion and load profiles remain sparse, and few tools exist to link these profiles to clinically relevant wear patterns or associated periprosthetic inflammatory tissue responses. Accordingly, the primary objective of this project is to develop validated, patient-specific models that predict PE wear in TSA and identify modifiable surgical, design, and rehabilitation targets to improve implant longevity and restore patient mobility. Additionally, we will establish histopathological hallmarks that indicate TSA failure caused by PE wear debris. Our central hypothesis is that specific shoulder kinematics and joint loading drive distinct PE wear patterns in TSA associated with mechanical failure or inflammatory-mediated osteolysis, depending on implant design and positioning. To achieve the overall objective of this work, shoulder motions and muscle excitations across 25 activities of daily living will be collected at pre-op and post-op (>6 months) in both aTSA and rTSA patients, with long-term follow-up of patient-reported outcomes via validated surveys (5 years). Unsupervised machine learning will categorize patients into movement-based phenotypes, which will then inform a multi-scale modeling framework to estimate in vivo shoulder joint loads and implant wear across the varying movement strategies. Predicted wear patterns will be validated using state-of-the-art preclinical wear simulators. Simultaneously, we will quantify how patient, surgical, and implant factors contribute to wear in retrieved TSA components (>400 samples), correlating imaging-based wear patterns with clinical outcomes, patient-reported function, inflammatory tissue responses, and radiographic indications of loosening. For that purpose, we will establish benchmarks of TSA wear rates and introduce a new histopathological approach augmented by infrared spectroscopic imaging. This work is innovative because we are linking patient-specific movement patterns following TSA with multi-scale computational models to predict PE wear, breaking the current approaches of using generic motions and loads in existing testing standards. This work will produce the first integrated, publicly available database of TSA kinematics, joint loading, and PE wear patterns and rates, along with validated computational tools to inform implant design, surgical planning, rehabilitation strategies, and personalized risk assessment. Ultimately, these advances will improve functional outcomes and long-term success for TSA patients and enable better preclinical testing methods and standards.

Impact of environmental toxicants on frontal cortical circuits

Abstract: Human mercury (Hg) exposure has been known for many decades to produce cognitive impairment and mood disorder symptoms. Hg is a global pollutant that poses widespread potential for neurotoxic exposure, earning it a position on the WHO’s list of the top 10 chemicals of major public health concern. However, little is known about the neural mechanisms that lead to neuropsychiatric symptoms from Hg exposure. The objective of this application is to identify specific mechanisms, within the neocortical circuits that control emotion and cognition, that are disrupted by the neurotoxicant, methylmercury (MeHg). The neocortex exhibits especially strong bioaccumulation of Hg, magnifying the risk to these circuits. Therefore, we hypothesize that chronic MeHg exposure leads to persistent circuit dysfunction in prefrontal and insular cortices (mPFC and aIC) – two brain regions critical in control of emotion and cognition. Our recent work showed that mPFC neurons in brain slices are negatively affected by acute MeHg exposure, resulting in hyperexcitability and altered synaptic transmission. Currently, it unknown how these acute effects on synaptic transmission translate to altered neuronal function in vivo. This proposal applies an integrative approach to determine the in vivo effects of MeHg on mPFC and aIC circuits, at the systems neurophysiology, synaptic and molecular levels. We will compare the effects of MeHg exposure on in vivo spiking activity patterns in brain regions of the mPFC-aIC circuit, using multiunit electrophysiological recordings in awake animals. Action potentials will be recorded simultaneously from multiple neurons, distributed across cortical layers, to evaluate effects on spike frequency, temporal patterning and correlation. Using acute brain slices derived from animals chronically treated with MeHg in vivo, electrophysiologically recorded synaptic estimates will be made to compare the effects of MeHg exposure on synaptic transmission and EI-balance within brain regions of the mPFC-aIC circuit. Based on previous evidence, we hypothesize that TDP-43 hyper-phosphorylation and aggregation link MeHg exposure to mPFC and aIC dysfunction. Therefore, immunohistochemistry will be used to measure TDP-43 hyper-phosphorylation and nuclear redistribution from animals treated in vivo +/- MeHg. In addition, tissue will be co-labeled with antibodies for nPAS4, a well-stablished molecular marker of activity, to determine whether TDP-43 hallmarks correlate with MeHg-induced hyper-excitability. The results of our study will substantively improve our mechanistic understanding of how Hg disrupts frontal cortical function and contribute to our understanding of the biological basis of emotional and cognitive sympoms. Identifying specific actions of MeHg at the functional microcircuitry level and cellular/molecular level will help significantly in finding novel targets for therapeutic interventions. If our hypothesis is correct, this will also raise the question of the extent to which chronic low-level environmental mercury exposure contributes to the etiology of fronto-cortical disorders with symptoms that overlap mercury exposure but do not have definitive genetic origins. This is particularly important because fronto-cortical disorders are predominantly sporadic in nature.

RECONJOINT: A Preference Elicitation Tool to Improve Shared Decision Making for Breast Reconstruction Surgery

PROJECT SUMMARY/ABSTRACT Breast reconstruction is a critical component of comprehensive breast cancer care, offering physical and emotional restoration after mastectomy. However, 40% of women undergoing breast reconstruction report dissatisfaction and decisional regret due to low involvement with treatment decisions and poor alignment between treatment preferences and the chosen reconstructive technique. Current approaches to shared decision-making (SDM) often fail to elicit and integrate individual-level preferences into treatment planning. This serves as a barrier to effective SDM and patient-centered care. To address this gap, we developed a web- based decision tool that uses adaptive choice–based conjoint (ACBC) analysis to elicit patient-level preferences for breast reconstruction. Preliminary studies indicate that the decision tool is acceptable and usable; patients wanted to view their results and use the tool in clinic, which we could not accommodate at the time because the decision tool currently lacks a structured method for clinical integration. We propose to develop an implementation toolkit for the decision tool to facilitate clinical integration and then test the feasibility, acceptability, and implementation of the intervention, RECONJOINT (decision tool and toolkit). In Aim 1, we will design an implementation toolkit informed by focus groups and developed with input from key partners, including patients, providers, and patient advocates. Candidate elements for the implementation toolkit include components developed for site-level implementation: treatment preferences report, video introducing the tool and existing evidence, and recommendations for patients and providers to incorporate preferences into SDM. In Aim 2, we will evaluate the feasibility, acceptability, and preliminary efficacy of the intervention in a pilot cluster-randomized hybrid type 1 trial conducted at two cancer centers (Memorial Sloan Kettering and Duke University). Our primary outcome of interest is the feasibility of the intervention. Secondary outcomes include acceptability and preliminary efficacy. Using a hybrid design, we will simultaneously evaluate facilitators, barriers, and strategies to implementation and how these factors influence the feasibility and acceptability of the intervention. The Consolidated Framework for Implementation Research and the Theoretical Domains Framework will serve as conceptual frameworks. This study is innovative as it leverages ACBC analysis to elicit patient preferences, designs an intervention with multilevel input from clinical and community partners, and uses a hybrid trial design to simultaneously evaluate feasibility, acceptability, preliminary efficacy, and implementation. By addressing critical barriers to SDM and enhancing patient–provider communication, this research aligns with the goals of PA-25-253 and the National Cancer Plan to deliver high quality, patient-centered cancer care. Findings from this study will inform a full-scale multi-site trial to evaluate the efficacy of the intervention and implementation outcomes (e.g., reach).

Stability in disrupted maternal representations over the perinatal period: Contributors and consequences

Abstract High-quality mother-infant relationships promote social, emotional, and cognitive development while protecting against poor child behavioral, health, and psychological adaptation that create risk for long- term negative outcomes. As mothers transition to parenthood, their own experiences of being cared for influence their emerging views of parenting and representations of their developing child. Evidence suggests that ‘disrupted’ maternal representations of the child, i.e., representations characterized by mixed communication, role merging, extreme withdrawal, and other unusual psychological processes, are tied to both poor child socioemotional adjustment and both insecure and disorganized attachment. However, it is unclear whether disrupted representations that emerge during pregnancy remain stable across the first several years of the child’s life. In addition, to date, research has not examined how change/stability in these representations may affect maternal caregiving and subsequent child adaptation. Using data from a longitudinal, multi-method study, this proposed project will examine the stability of maternal representations of the child for 99 women living in high risk contexts using the Working Model of the Child Interview during the third trimester and again when the child is two years of age. Mothers’ demographic characteristics (i.e. SES and relationship status), interpersonal violence experiences (i.e. child maltreatment or intimate violence exposure), psychological health (i.e. depressive, anxious, and PTSD symptoms), and parenting stress (i.e. perceptions of the child as difficult and parent-child interactions as dysfunctional) are measured as well to examine influences on representation stability. Finally, the observed quality of maternal caregiving and child adaptation are measured and examined in relation to stability in maternal representations of the child. Findings from this study have the potential to identify which mother-child dyads are at greatest risk for poor adaptation across the perinatal period and to delineate the contributors and consequences of maternal representational stability. These findings will serve as an important step towards informing the development or modification of existing prevention/intervention approaches that are targeted specifically towards mother-child dyads who are most at need.

Organization of thalamic networks and mechanisms of dysfunction in schizophrenia and autism

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

Go with the visual flow: circuit mechanisms for gaze control during locomotion

OpenNeuro FitLins GLM: An Accessible, Semi-Automated Pipeline for OpenNeuro Task fMRI Analysis

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Neural circuits underlying sleep structure and functions

Sleep is an active state critical for processing emotional memories encoded during waking in both humans and animals. There is a remarkable overlap between the brain structures and circuits active during sleep, particularly rapid eye-movement (REM) sleep, and the those encoding emotions. Accordingly, disruptions in sleep quality or quantity, including REM sleep, are often associated with, and precede the onset of, nearly all affective psychiatric and mood disorders. In this context, a major biomedical challenge is to better understand the underlying mechanisms of the relationship between (REM) sleep and emotion encoding to improve treatments for mental health. This lecture will summarize our investigation of the cellular and circuit mechanisms underlying sleep architecture, sleep oscillations, and local brain dynamics across sleep-wake states using electrophysiological recordings combined with single-cell calcium imaging or optogenetics. The presentation will detail the discovery of a 'somato-dendritic decoupling'in prefrontal cortex pyramidal neurons underlying REM sleep-dependent stabilization of optimal emotional memory traces. This decoupling reflects a tonic inhibition at the somas of pyramidal cells, occurring simultaneously with a selective disinhibition of their dendritic arbors selectively during REM sleep. Recent findings on REM sleep-dependent subcortical inputs and neuromodulation of this decoupling will be discussed in the context of synaptic plasticity and the optimization of emotional responses in the maintenance of mental health.

From heterogeneous wiring to degenerative function in motion-detection circuits

Neural mechanisms of rhythmic motor control in Drosophila

All animal locomotion is rhythmic,whether it is achieved through undulatory movement of the whole body or the coordination of articulated limbs. Neurobiologists have long studied locomotor circuits that produce rhythmic activity with non-rhythmic input, also called central pattern generators (CPGs). However, the cellular and microcircuit implementation of a walking CPG has not been described for any limbed animal. New comprehensive connectomes of the fruit fly ventral nerve cord (VNC) provide an opportunity to study rhythmogenic walking circuits at a synaptic scale.We use a data-driven network modeling approach to identify and characterize a putative walking CPG in the Drosophila leg motor system.

Digital Minds: Brain Development in the Age of Technology

Digital Minds: Brain Development in the Age of Technology examines how our increasingly connected world shapes mental and cognitive health. From screen time and social media to virtual interactions, this seminar delves into the latest research on how technology influences brain development, relationships, and emotional well-being. Join us to explore strategies for harnessing technology's benefits while mitigating its potential challenges, empowering you to thrive in a digital age.

Circuit Mechanisms of Remote Memory

Memories of emotionally-salient events are long-lasting, guiding behavior from minutes to years after learning. The prelimbic cortex (PL) is required for fear memory retrieval across time and is densely interconnected with many subcortical and cortical areas involved in recent and remote memory recall, including the temporal association area (TeA). While the behavioral expression of a memory may remain constant over time, the neural activity mediating memory-guided behavior is dynamic. In PL, different neurons underlie recent and remote memory retrieval and remote memory-encoding neurons have preferential functional connectivity with cortical association areas, including TeA. TeA plays a preferential role in remote compared to recent memory retrieval, yet how TeA circuits drive remote memory retrieval remains poorly understood. Here we used a combination of activity-dependent neuronal tagging, viral circuit mapping and miniscope imaging to investigate the role of the PL-TeA circuit in fear memory retrieval across time in mice. We show that PL memory ensembles recruit PL-TeA neurons across time, and that PL-TeA neurons have enhanced encoding of salient cues and behaviors at remote timepoints. This recruitment depends upon ongoing synaptic activity in the learning-activated PL ensemble. Our results reveal a novel circuit encoding remote memory and provide insight into the principles of memory circuit reorganization across time.

Sensory cognition

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.

Understanding the complex behaviors of the ‘simple’ cerebellar circuit

Every movement we make requires us to precisely coordinate muscle activity across our body in space and time. In this talk I will describe our efforts to understand how the brain generates flexible, coordinated movement. We have taken a behavior-centric approach to this problem, starting with the development of quantitative frameworks for mouse locomotion (LocoMouse; Machado et al., eLife 2015, 2020) and locomotor learning, in which mice adapt their locomotor symmetry in response to environmental perturbations (Darmohray et al., Neuron 2019). Combined with genetic circuit dissection, these studies reveal specific, cerebellum-dependent features of these complex, whole-body behaviors. This provides a key entry point for understanding how neural computations within the highly stereotyped cerebellar circuit support the precise coordination of muscle activity in space and time. Finally, I will present recent unpublished data that provide surprising insights into how cerebellar circuits flexibly coordinate whole-body movements in dynamic environments.

Neural mechanisms governing the learning and execution of avoidance behavior

The nervous system orchestrates adaptive behaviors by intricately coordinating responses to internal cues and environmental stimuli. This involves integrating sensory input, managing competing motivational states, and drawing on past experiences to anticipate future outcomes. While traditional models attribute this complexity to interactions between the mesocorticolimbic system and hypothalamic centers, the specific nodes of integration have remained elusive. Recent research, including our own, sheds light on the midline thalamus's overlooked role in this process. We propose that the midline thalamus integrates internal states with memory and emotional signals to guide adaptive behaviors. Our investigations into midline thalamic neuronal circuits have provided crucial insights into the neural mechanisms behind flexibility and adaptability. Understanding these processes is essential for deciphering human behavior and conditions marked by impaired motivation and emotional processing. Our research aims to contribute to this understanding, paving the way for targeted interventions and therapies to address such impairments.

Visuomotor learning of location, action, and prediction

Modelling the fruit fly brain and body

Through recent advances in microscopy, we now have an unprecedented view of the brain and body of the fruit fly Drosophila melanogaster. We now know the connectivity at single neuron resolution across the whole brain. How do we translate these new measurements into a deeper understanding of how the brain processes sensory information and produces behavior? I will describe two computational efforts to model the brain and the body of the fruit fly. First, I will describe a new modeling method which makes highly accurate predictions of neural activity in the fly visual system as measured in the living brain, using only measurements of its connectivity from a dead brain [1], joint work with Jakob Macke. Second, I will describe a whole body physics simulation of the fruit fly which can accurately reproduce its locomotion behaviors, both flight and walking [2], joint work with Google DeepMind.

Stability of visual processing in passive and active vision

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

Modeling idiosyncratic evaluation of faces

Incorporating visual evidence and counter-evidence to estimate self-movement

Piecing together the puzzle of emotional consciousness

Conscious emotional experiences are very rich in their nature, and can encompass anything ranging from the most intense panic when facing immediate threat, to the overwhelming love felt when meeting your newborn. It is then no surprise that capturing all aspects of emotional consciousness, such as intensity, valence, and bodily responses, into one theory has become the topic of much debate. Key questions in the field concern how we can actually measure emotions and which type of experiments can help us distill the neural correlates of emotional consciousness. In this talk I will give a brief overview of theories of emotional consciousness and where they disagree, after which I will dive into the evidence proposed to support these theories. Along the way I will discuss to what extent studying emotional consciousness is ‘special’ and will suggest several tools and experimental contrasts we have at our disposal to further our understanding on this intriguing topic.

Sensory Consequences of Visual Actions

We use rapid eye, head, and body movements to extract information from a new part of the visual scene upon each new gaze fixation. But the consequences of such visual actions go beyond their intended sensory outcomes. On the one hand, intrinsic consequences accompany movement preparation as covert internal processes (e.g., predictive changes in the deployment of visual attention). On the other hand, visual actions have incidental consequences, side effects of moving the sensory surface to its intended goal (e.g., global motion of the retinal image during saccades). In this talk, I will present studies in which we investigated intrinsic and incidental sensory consequences of visual actions and their sensorimotor functions. Our results provide insights into continuously interacting top-down and bottom-up sensory processes, and they reify the necessity to study perception in connection to motor behavior that shapes its fundamental processes.

Modeling the Navigational Circuitry of the Fly

Navigation requires orienting oneself relative to landmarks in the environment, evaluating relevant sensory data, remembering goals, and convert all this information into motor commands that direct locomotion. I will present models, highly constrained by connectomic, physiological and behavioral data, for how these functions are accomplished in the fly brain.

Vocal emotion perception at millisecond speed

The human voice is possibly the most important sound category in the social landscape. Compared to other non-verbal emotion signals, the voice is particularly effective in communicating emotions: it can carry information over large distances and independent of sight. However, the study of vocal emotion expression and perception is surprisingly far less developed than the study of emotion in faces. Thereby, its neural and functional correlates remain elusive. As the voice represents a dynamically changing auditory stimulus, temporally sensitive techniques such as the EEG are particularly informative. In this talk, the dynamic neurocognitive operations that take place when we listen to vocal emotions will be specified, with a focus on the effects of stimulus type, task demands, and speaker and listener characteristics (e.g., age). These studies suggest that emotional voice perception is not only a matter of how one speaks but also of who speaks and who listens. Implications of these findings for the understanding of psychiatric disorders such as schizophrenia will be discussed.

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

From the guts to the brain through adaptive immunity in the prevention of Alzheimer’ disease

Dr. Pasinetti is the Saunders Family Chair and Professor of Neurology at Icahn School of medicine at Mount Sinai, New York. His studies allowed him to develop novel therapeutic approaches through investigation of preventable risk factors including mood disorders in the promotion of resilience against neurodegenerative disorder. In his presentation Dr. Pasinetti will discuss novel concepts about the gut-brain axis in mechanisms associated to peripheral adaptive immunity as therapeutic targets to mitigate the onset and the progression of Alzheimer’s disease and other form of dementia.

Freeze or flee ? New insights from rodent models of autism

Individuals afflicted with certain types of autism spectrum disorder often exhibit impaired cognitive function alongside enhanced emotional symptoms and mood lability. However, current understanding of the pathogenesis of autism and intellectual disabilities is based primarily on studies in the hippocampus and cortex, brain areas involved in cognitive function. But, these disorders are also associated with strong emotional symptoms, which are likely to involve changes in the amygdala and other brain areas. In this talk I will highlight these issues by presenting analyses in rat models of ASD/ID lacking Nlgn3 and Frm1 (causing Fragile X Syndrome). In addition to identifying new circuit and cellular alterations underlying divergent patterns of fear expression, these findings also suggest novel therapeutic strategies.

From pecking order to ketamine - neural mechanism of social and emotional behavior

Emotions and social interactions color our lives and shape our behaviors. Using animal models and engineered manipulations, we aim to understand how social and emotional behaviors are encoded in the brain, focusing on the neural circuits underlying dominance hierarchy and depression. This lecture will highlight our recent discoveries on how downward social mobility leads to depression; how ketamine tames depression by blocking burst firing in the brain’s antireward center; and, how glia-neuron interaction plays a surprising role in this process. I will also present our recent work on the mechanism underlying the sustained antidepressant activity of ketamine and its brain region specificity. With these results, we hope to illuminate on a more unified theory on ketamine’s mode of action and inspire new treatment strategies for depression.

From pecking order to ketamine - neural mechanism of social and emotional behavior

Emotions and social interactions color our lives and shape our behaviors. Using animal models and engineered manipulations, we aim to understand how social and emotional behaviors are encoded in the brain, focusing on the neural circuits underlying dominance hierarchy and depression. This lecture will highlight our recent discoveries on how downward social mobility leads to depression; how ketamine tames depression by blocking burst firing in the brain’s antireward center; and, how glia-neuron interaction plays a surprising role in this process. I will also present our recent work on the mechanism underlying the sustained antidepressant activity of ketamine and its brain region specificity. With these results, we hope to illuminate on a more unified theory on ketamine’s mode of action and inspire new treatment strategies for depression.

Studies on the role of relevance appraisal in affect elicitation

A fundamental question in affective sciences is how the human mind decides if, and in what intensity, to elicit an affective response. Appraisal theories assume that preceding the affective response, there is an evaluation stage in which dimensions of an event are being appraised. Common to most appraisal theories is the assumption that the evaluation phase involves the assessment of the stimulus’ relevance to the perceiver’s well-being. In this talk, I first discuss conceptual and methodological challenges in investigating relevance appraisal. Next, I present two lines of experiments that ask how the human mind uses information about objective and subjective probabilities in the decision about the intensity of the emotional response and how these are affected by the valence of the event. The potential contribution of the results to appraisal theory is discussed.

Computational models of spinal locomotor circuitry

To effectively move in complex and changing environments, animals must control locomotor speed and gait, while precisely coordinating and adapting limb movements to the terrain. The underlying neuronal control is facilitated by circuits in the spinal cord, which integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. I will present a series of computational models investigating dynamics of central neuronal interactions as well as a neuromechanical model that integrates neuronal circuits with a model of the musculoskeletal system. These models closely reproduce speed-dependent gait expression and experimentally observed changes following manipulation of multiple classes of genetically-identified neuronal populations. I will discuss the utility of these models in providing experimentally testable predictions for future studies.

The Geometry of Decision-Making

Running, swimming, or flying through the world, animals are constantly making decisions while on the move—decisions that allow them to choose where to eat, where to hide, and with whom to associate. Despite this most studies have considered only on the outcome of, and time taken to make, decisions. Motion is, however, crucial in terms of how space is represented by organisms during spatial decision-making. Employing a range of new technologies, including automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) for animals, experiments with fruit flies, locusts and zebrafish (representing aerial, terrestrial and aquatic locomotion, respectively), I will demonstrate that this time-varying representation results in the emergence of new and fundamental geometric principles that considerably impact decision-making. Specifically, we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (‘critical’) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. Due to the critical nature of these transitions (and the corresponding increase in ‘susceptibility’) even noisy brains are extremely sensitive to very small differences between remaining options (e.g., a very small difference in neuronal activity being in “favor” of one option) near these locations in space-time. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

Targeting Maladaptive Emotional Memories to Treat Mental Health Disorders: Insights from Rodent Models

Maladaptive emotional memories contribute to the persistence of numerous mental health disorders, including post-traumatic stress disorder (PTSD), drug addiction and obsessive-compulsive disorder (OCD). Using rodent behavioural models of the psychological processes relevant to these disorders, it is possible to identify potential treatment targets for the development of new therapies, including those based upon disrupting the reconsolidation of maladaptive emotional memories. Using examples from rodent models relevant to multiple mental health disorders, this talk will consider some of the opportunities and challenges that this approach provides.

Establishment and aging of the neuronal DNA methylation landscape in the hippocampus

The hippocampus is a brain region with key roles in memory formation, cognitive flexibility and emotional control. Yet hippocampal function is impaired severely during aging and in neurodegenerative diseases, and impairments in hippocampal function underlie age-related cognitive decline. Accumulating evidence suggests that the deterioration of the neuron-specific epigenetic landscape during aging contributes to their progressive, age-related dysfunction. For instance, we have recently shown that aging is associated with pronounced alterations of neuronal DNA methylation patterns in the hippocampus. Because neurons are generated mostly during development with limited replacement in the adult brain, they are particularly long-lived cells and have to maintain their cell-type specific gene expression programs life-long in order to preserve brain function. Understanding the epigenetic mechanisms that underlie the establishment and long-term maintenance of neuron-specific gene expression programs, will help us to comprehend the sources and consequences of their age-related deterioration. In this talk, I will present our recent work that investigated the role of DNA methylation in the establishment of neuronal gene expression programs and neuronal function, using adult neurogenesis in the hippocampus as a model. I will then describe the effects of aging on the DNA methylation landscape in the hippocampus and discuss the malleability of the aging neuronal methylome to lifestyle and environmental stimulation.

Motion processing across visual field locations in zebrafish

Neural mechanisms underlying visual and vestibular self-motion perception

Integrative Neuromodulation: from biomarker identification to optimizing neuromodulation

Why do we make decisions impulsively blinded in an emotionally rash moment? Or caught in the same repetitive suboptimal loop, avoiding fears or rushing headlong towards illusory rewards? These cognitive constructs underlying self-control and compulsive behaviours and their influence by emotion or incentives are relevant dimensionally across healthy individuals and hijacked across disorders of addiction, compulsivity and mood. My lab focuses on identifying theory-driven modifiable biomarkers focusing on these cognitive constructs with the ultimate goal to optimize and develop novel means of neuromodulation. Here I will provide a few examples of my group’s recent work to illustrate this approach. I describe a series of recent studies on intracranial physiology and acute stimulation focusing on risk taking and emotional processing. This talk highlights the subthalamic nucleus, a common target for deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder. I further describe recent translational work in non-invasive neuromodulation. Together these examples illustrate the approach of the lab highlighting modifiable biomarkers and optimizing neuromodulation.

Targeting thalamic circuits rescues motor and mood deficits in PD mice

Although bradykinesia, tremor, and rigidity are hallmark motor defects in Parkinson’s disease (PD) patients, they also experience motor learning impairments and non-motor symptoms such as depression. The neural basis for these different PD symptoms are not well understood. While current treatments are effective for locomotion deficits in PD, therapeutic strategies targeting motor learning deficits and non-motor symptoms are lacking. We found that distinct parafascicular (PF) thalamic subpopulations project to caudate putamen (CPu), subthalamic nucleus (STN), and nucleus accumbens (NAc). While PF-->CPu and PF-->STN circuits are critical for locomotion and motor learning respectively, inhibition of the PF-->NAc circuit induced a depression-like state. While chemogenetically manipulating CPu-projecting PF neurons led to a long-term restoration of locomotion, optogenetic long-term potentiation at PF-->STN synapses restored motor learning behavior in PD model mice. Furthermore, activation of NAc-projecting PF neurons rescued depression-like PD phenotypes. Importantly, we identified nicotinic acetylcholine receptors capable of modulating PF circuits to rescue different PD phenotypes. Thus, targeting PF thalamic circuits may be an effective strategy for treating motor and non-motor deficits in PD.

Minute-scale periodic sequences in medial entorhinal cortex

The medial entorhinal cortex (MEC) hosts many of the brain’s circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience. While location is known to be encoded by a plethora of spatially tuned cell types in this brain region, little is known about how the activity of entorhinal cells is tied together over time. Among the brain’s most powerful mechanisms for neural coordination are network oscillations, which dynamically synchronize neural activity across circuit elements. In MEC, theta and gamma oscillations provide temporal structure to the neural population activity at subsecond time scales. It remains an open question, however, whether similarly coordination occurs in MEC at behavioural time scales, in the second-to-minute regime. In this talk I will show that MEC activity can be organized into a minute-scale oscillation that entrains nearly the entire cell population, with periods ranging from 10 to 100 seconds. Throughout this ultraslow oscillation, neural activity progresses in periodic and stereotyped sequences. The oscillation sometimes advances uninterruptedly for tens of minutes, transcending epochs of locomotion and immobility. Similar oscillatory sequences were not observed in neighboring parasubiculum or in visual cortex. The ultraslow periodic sequences in MEC may have the potential to couple its neurons and circuits across extended time scales and to serve as a scaffold for processes that unfold at behavioural time scales.

Does subjective time interact with the heart rate?

Decades of research have investigated the relationship between perception of time and heart rate with often mixed results. In search of such a relationship, I will present my far journey between two projects: from time perception in the realistic VR experience of crowded subway trips in the order of minutes (project 1); to the perceived duration of sub-second white noise tones (project 2). Heart rate had multiple concurrent relationships with subjective temporal distortions for the sub-second tones, while the effects were lacking or weak for the supra-minute subway trips. What does the heart have to do with sub-second time perception? We addressed this question with a cardiac drift-diffusion model, demonstrating the sensory accumulation of temporal evidence as a function of heart rate.

Direction-selective ganglion cells in primate retina: a subcortical substrate for reflexive gaze stabilization?

To maintain a stable and clear image of the world, our eyes reflexively follow the direction in which a visual scene is moving. Such gaze stabilization mechanisms reduce image blur as we move in the environment. In non-primate mammals, this behavior is initiated by ON-type direction-selective ganglion cells (ON-DSGCs), which detect the direction of image motion and transmit signals to brainstem nuclei that drive compensatory eye movements. However, ON-DSGCs have not yet been functionally identified in primates, raising the possibility that the visual inputs that drive this behavior instead arise in the cortex. In this talk, I will present molecular, morphological and functional evidence for identification of an ON-DSGC in macaque retina. The presence of ON-DSGCs highlights the need to examine the contribution of subcortical retinal mechanisms to normal and aberrant gaze stabilization in the developing and mature visual system. More generally, our findings demonstrate the power of a multimodal approach to study sparsely represented primate RGC types.

Social attention & emotion: invasive neurophysiology & white matter pathway studies

Two sides of emotion expressions: Readouts and Regulators

Versatile treadmill system for measuring locomotion and neural activity in head-fixed mice

Here, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. https://dx.doi.org/10.1016/j.xpro.2022.101701

Modern Approaches to Behavioural Analysis

The goal of neuroscience is to understand how the nervous system controls behaviour, not only in the simplified environments of the lab, but also in the natural environments for which nervous systems evolved. In pursuing this goal, neuroscience research is supported by an ever-larger toolbox, ranging from optogenetics to connectomics. However, often these tools are coupled with reductionist approaches for linking nervous systems and behaviour. This course will introduce advanced techniques for measuring and analysing behaviour, as well as three fundamental principles as necessary to understanding biological behaviour: (1) morphology and environment; (2) action-perception closed loops and purpose; and (3) individuality and historical contingencies [1]. [1] Gomez-Marin, A., & Ghazanfar, A. A. (2019). The life of behavior. Neuron, 104(1), 25-36

It’s All About Motion: Functional organization of the multisensory motion system at 7T

The human middle temporal complex (hMT+) has a crucial biological relevance for the processing and detection of direction and speed of motion in visual stimuli. In both humans and monkeys, it has been extensively investigated in terms of its retinotopic properties and selectivity for direction of moving stimuli; however, only in recent years there has been an increasing interest in how neurons in MT encode the speed of motion. In this talk, I will explore the proposed mechanism of speed encoding questioning whether hMT+ neuronal populations encode the stimulus speed directly, or whether they separate motion into its spatial and temporal components. I will characterize how neuronal populations in hMT+ encode the speed of moving visual stimuli using electrocorticography ECoG and 7T fMRI. I will illustrate that the neuronal populations measured in hMT+ are not directly tuned to stimulus speed, but instead encode speed through separate and independent spatial and temporal frequency tuning. Finally, I will suggest that this mechanism may play a role in evaluating multisensory responses for visual, tactile and auditory stimuli in hMT+.

Exploring emotion in the expression of ape gesture

Language appears to be the most complex system of animal communication described to date. However, its precursors were present in the communication of our evolutionary ancestors and are likely shared by our modern ape cousins.  All great apes, including humans, employ a rich repertoire of vocalizations, facial expressions, and gestures. Great ape gestural repertoires are particularly elaborate, with ape species employing over 80 different gesture types intentionally: that is towards a recipient with a specific goal in mind. Intentional usage allows us to ask not only what information is encoded in ape gestures, but what do apes mean when they use them. I will discuss recent research on ape gesture, on how we approach the question of decoding meaning, and how with new methods we are starting to integrate long overlooked aspects of ape gesture such as group and individual variation, and expression and emotion into our study of these signals.

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Our results obtained in the fruit fly Drosophila demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

CNStalk: Involvement of the cerebellum in motor and emotional learning

Learning static and dynamic mappings with local self-supervised plasticity

Animals exhibit remarkable learning capabilities with little direct supervision. Likewise, self-supervised learning is an emergent paradigm in artificial intelligence, closing the performance gap to supervised learning. In the context of biology, self-supervised learning corresponds to a setting where one sense or specific stimulus may serve as a supervisory signal for another. After learning, the latter can be used to predict the former. On the implementation level, it has been demonstrated that such predictive learning can occur at the single neuron level, in compartmentalized neurons that separate and associate information from different streams. We demonstrate the power such self-supervised learning over unsupervised (Hebb-like) learning rules, which depend heavily on stimulus statistics, in two examples: First, in the context of animal navigation where predictive learning can associate internal self-motion information always available to the animal with external visual landmark information, leading to accurate path-integration in the dark. We focus on the well-characterized fly head direction system and show that our setting learns a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Second, we show that incorporating global gating by reward prediction errors allows the same setting to learn conditioning at the neuronal level with mixed selectivity. At its core, conditioning entails associating a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Solving the generic problem of pattern-to-pattern associations naturally leads to emergent cognitive phenomena like blocking, overshadowing, saliency effects, extinction, interstimulus interval effects etc. Surprisingly, we find that the same network offers a reductionist mechanism for causal inference by resolving the post hoc, ergo propter hoc fallacy.

Ebselen: a lithium-mimetic without lithium side-effects?

Development of new medications for mental health conditions is a pressing need given the high proportion of people not responding to available treatments. We hope that presenting ebselen to a wider audience will inspire further studies on this promising agent with a benign side-effects profile. Laboratory research, animal research and human studies suggest that ebselen shares many features with the mood stabilising drug lithium, creating a promise of a drug that would have a similar clinical effect but without lithium’s troublesome side-effect profile and toxicity. Both drugs have a common biological target, inositol monophosphatase, whose inhibition is thought key to lithium’s therapeutic effect. Both drugs have neuroprotective action and reduce oxidative stress. In animal studies, ebselen affected neurotransmitters involved in the development of mental health symptoms, and in particular, produced effects of serotonin function very similar to lithium. Both ebselen and lithium share behavioural effects: antidepressant-like effects in rodent models of depression and decrease in behavioural impulsivity, a property associated with lithium's anti-suicidal action. Human neuropsychological studies support an antidepressant profile for ebselen based on its positive impact on emotional processing and reward seeking. Our group currently is exploring ebselen’s effects in patients with mood disorders. A completed ‘add-on’ clinical trial in mania showed ebselen’s superiority over placebo after three weeks of treatment. Our ongoing experimental research explores ebselen’s antidepressant profile in patients with treatment resistant depression. If successful, this will lead to a clinical trial of ebselen as an antidepressant augmentation agent, similar to lithium.

Pynapple: a light-weight python package for neural data analysis - webinar + tutorial

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

Pynapple: a light-weight python package for neural data analysis - webinar + tutorial

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

Perception during visual disruptions

Visual perception is perceived as continuous despite frequent disruptions in our visual environment. For example, internal events, such as saccadic eye-movements, and external events, such as object occlusion temporarily prevent visual information from reaching the brain. Combining evidence from these two models of visual disruption (occlusion and saccades), we will describe what information is maintained and how it is updated across the sensory interruption. Lina Teichmann will focus on dynamic occlusion and demonstrate how object motion is processed through perceptual gaps. Grace Edwards will then describe what pre-saccadic information is maintained across a saccade and how it interacts with post-saccadic processing in retinotopically relevant areas of the early visual cortex. Both occlusion and saccades provide a window into how the brain bridges perceptual disruptions. Our evidence thus far suggests a role for extrapolation, integration, and potentially suppression in both models. Combining evidence from these typically separate fields enables us to determine if there is a set of mechanisms which support visual processing during visual disruptions in general.

3D Movement Analysis of the Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories

Bernstein Conference 2024

Spatial integration properties in MT neurons affect spatiotemporal motion discrimination

Bernstein Conference 2024

Spatial scale and coordinates of motion representation in the mouse Nucleus of the Optic Tract

Bernstein Conference 2024

Utilizing Random Forest for Multivariate Analysis: Exploring the Influence of Dopaminergic Neurons on Drosophila Larvae Locomotion

Bernstein Conference 2024

Biological learning of local motion detectors

COSYNE 2022

Causal inference can explain hierarchical motion perception and is reflected in neural responses in MT

COSYNE 2022

Correlation-based motion detectors in olfaction enable turbulent plume navigation

COSYNE 2022

Faithful encoding of interlimb coordination by individual Purkinje cells during locomotion

COSYNE 2022

Learning static and motion cues to material by predicting moving surfaces

COSYNE 2022

Learning static and motion cues to material by predicting moving surfaces

COSYNE 2022

Organization of local directionally selective neurons informs global motion vision encoding

COSYNE 2022

Organization of local directionally selective neurons informs global motion vision encoding

COSYNE 2022

Predicting connectivity of motion-processing neurons with recurrent neural networks

COSYNE 2022

Predicting connectivity of motion-processing neurons with recurrent neural networks

COSYNE 2022

Structure in motion: visual motion perception as online hierarchical inference

COSYNE 2022

Structure in motion: visual motion perception as online hierarchical inference

COSYNE 2022

Cerebellar interneurons encode single steps in locomotion

COSYNE 2023

Data-driven discovery of long timescale behavioral strategies during sensory evoked locomotion

COSYNE 2023

Directly comparing fly and mouse visual systems reveals algorithmic similarities for motion detection

COSYNE 2023

Divisive normalization as a mechanism for hierarchical causal inference in motion perception

COSYNE 2023

Drosophila detects negative visual evidence against self-motion

COSYNE 2023

Leveraging computational and animal models of vision to probe atypical emotion recognition in autism

COSYNE 2023

Locomotion is associated with straighter neural trajectories for natural movies in mouse visual cortex

COSYNE 2023

Self-generated vestibular prosthetic input updates forward internal model of self-motion

COSYNE 2023

Broken time reversal symmetry in visual motion detection

COSYNE 2025

A data-driven biomechanical modeling and optimization pipeline for studying salamander locomotions

COSYNE 2025

Information-preserving modulation as a principle of sensory coding during locomotion across species

COSYNE 2025

Neural circuit architectural priors for quadruped locomotion

COSYNE 2025

Predictive and Invariant Representations via Motion and Form Factorization in Natural Scenes

COSYNE 2025

Probing Motion-Form Interactions in the Macaque Inferior Temporal Cortex and Artificial Neural Networks for Complex Scene Understanding

COSYNE 2025

Recurrent connectivity supports motion detection in connectome-constrained models of fly vision

COSYNE 2025

A spiking neuromechanical model of the zebrafish to investigate the role of axial proprioceptive sensory feedback during locomotion

COSYNE 2025

Acute D3 Antagonist GSK598809 Modulates Neural Response During Negative Emotional Processing in Substance Dependence

Alterations of locomotion in a model of incomplete cervical spinal cord injury (SCI) in pigs

An alternative cortico-subcortical loop involving the thalamus for locomotion

Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress

Brain-body interactions in emotions: perspective matters

Brainstem somatostatin-expressing cells control the emotional regulation of pain behavior

Are children with “Limited Prosocial Emotions” emotionally blind? Emotional processing and facial emotional expressions in response to three intervention programs

Investigating the role of recurrent connectivity in connectome-constrained and task-optimized models of the fruit fly’s motion pathway

Bernstein Conference 2024