utility

Utilizing integrin-targeted PET imaging and therapeutics to predict and treat radiation-induced pulmonary fibrosis

Project Summary/Abstract. Lung cancer is the leading cause of cancer death in the US, with over 125,000 deaths annually. Radiation therapy (RT) is a critical component of curative lung cancer treatment for many patients. However, radiationinduced pulmonary fibrosis (RIPF) is a common side effect that carries a poor prognosis with limited treatment options. Up to 40% of patients with lung cancer who receive RT may experience RIPF. RIPF is a late effect of RT, typically occurring 3 or more months after treatment. The symptoms of RIPF can include shortness of breath, pleural effusions, decreased lung function, and respiratory failure. Cell surface integrin heterodimers play a key role in the pathogenesis of RIPF. In particular, the integrin αvβ6, which is expressed at a low level in the alveolar epithelium at baseline, is significantly upregulated upon RT damage. The key role of integrin αvβ6 in RIPF is illustrated by studies in which mice lacking integrin αvβ6, or treated with an αvβ6-blocking antibody, do not develop RIPF. Here, we propose to translate this mechanistic understanding of RIPF into novel approaches for monitoring and treating RIPF. We hypothesize that non-invasive αvβ6 PET imaging will be safe and can specifically bind to αvβ6 in patients with RIPF. Additionally, we hypothesize that a novel small-molecule integrin antagonist, IDL2965, can mitigate and treat RIPF in mice. In this project, we are utilizing mice to model RIPF, as mice develop RIPF that mimics human disease. In addition, cellular and in vitro models do not approximate the complex biology leading to the development of RIPF. Our data using [64Cu]Cu-DOTA-αvβ6-BP to detect early RIPF in mice are compelling in both single-fraction high-dose RT and lower dose-larger volume RT models (Lo et. al, IJROBP 2025). However, to progress to clinical trials in patients with cancer, we will obtain data to submit an Investigational New Drug (IND) application to the FDA. Importantly, we propose translating [64Cu]Cu-DOTA-αvβ6-BP PET imaging into patients with lung cancer, allowing us to better identify RIPF and develop a tool to determine the efficacy of IDL-2965 in future clinical studies. The specific aims of the proposal are: (1) Characterize the utility of [64Cu]Cu-DOTA-αvβ6-BP in mice with conventionally fractionated RT and identify circulating biomarkers of RIPF, and determine the in vivo toxicology of [64Cu]Cu-DOTA-αvβ6-BP to prepare and submit an exploratory Investigational New Drug (eIND) application to the FDA, (2) Conduct a first-in-human clinical trial of [64Cu]Cu-DOTA-αvβ6-BP to determine its safety and human dosimetry in patients with evidence of RIPF from computed tomography or in healthy controls, and (3) Determine the effect of integrin antagonism using IDL-2965 on mitigating RIPF in preclinical mouse models. The goals of this proposal are two-fold: (1) demonstrate safety and target specificity for [64Cu]Cu-DOTA-αvβ6-BP so that it can be used in future studies to identify RIPF and evaluate the efficacy of anti-fibrotic therapies, and 2) determine the ability of IDL-2965 to prevent RIPF in preclinical mouse models.

BKCa Channel Contributions to Cerebellar Regulated TSC-Associated Neuropsychiatric Disorders

Project Summary TSC is associated with neurodevelopmental disability including cognitive disability and autism spectrum disorders (ASD) that make up part of TSC associated neuropsychiatric disorders (TAND). The mechanisms for TAND remain poorly understood but studies have increasingly implicated cerebellar dysfunction in the pathogenesis of cognitive and behavioral deficits in both TSC and other neurodevelopmental disorders. A shared feature is cerebellar Purkinje cell (PC) dysfunction. Changes in intrinsic properties of PCs results in both motor and cognitive/ behavioral changes in disease models and in individuals afflicted by these disorders. Mechanistic underpinnings of these altered properties remain unknown, but a significant emerging body of data implicate ion channel dysfunction as the primary etiology of these deficits. The current proposal seeks to delineate the ion channel contribution to PC dysfunction and to TAND-relevant behaviors. In doing so, these studies will produce significant both short- and long-term impact. Short-term: These proposed studies will provide a mechanistic understanding of the contribution of ion channels to the neuronal dysfunction in the cerebellum that has been demonstrated to be causally linked to abnormal TAND-relevant behaviors. In addition, we will target specific ion channels both genetically and pharmacologically to evaluate the benefits of ion channel restoration on both electrophysiological abnormalities but also the TAND-relevant behaviors observed in the model. Long-term: These studies, thus, provide a framework for subsequent clinically-relevant therapeutic development for TAND. First, these studies will uncover the ability for TAND-relevant behaviors to be improved upon targeting ion channel alterations in TSC. These studies will also define molecular targets on which therapeutic development can be targeted, thereby potentially providing a molecular-informed pipeline for therapeutic development. In addition, these studies will utilize clinically-available, FDA-approved pharmacological agents to target ion channel function and investigate the potential therapeutic benefits for these agents for TAND-relevant behaviors. Thus, these studies will address a core gap in knowledge to achieve a better mechanistic understanding of TAND and to develop therapeutic opportunities to address TAND. These studies will not only reveal previously understudied and novel mechanistic underpinnings for these behaviors but will provide pre-clinical insights into the therapeutic utility of clinically-utilized agents for the treatment of TAND-related behaviors, thus potentially providing both immediate and long-term opportunities for the treatment of TAND. Moreover, although these studies focus on TSC, these mechanisms may prove generalizable beyond TSC and provide a shared basis and therapeutic opportunity for other neuropsychiatric/developmental conditions.

Engineering of a temperate Burkholderia cepacia complex phage to improve efficacy as a potential therapeutic

Project Summary Bacteria in the Burkholderia cepacia complex (Bcc) cause difficult to treat infections in patients with compromised respiratory systems, such as those with cystic fibrosis (CF). Alternative treatment options are needed, since antibiotics often fail these patients. Bacteriophage (phage) therapy is a promising strategy, yet therapeutically ideal phages are difficult to find and narrow in their range of use due to host specificity. In the proposed study, we continue development of a potential phage therapeutic sourced from Burkholderia itself. We have isolated a phage, called BCC02, that was present within the genome of a Burkholderia bacteria (a prophage) and have shown that it can kill other bacteria within the same genus. However, this phage still has the potential to integrate into other bacterial genomes, which is an undesirable trait for phage therapy. By engineering changes to the BCC02 genome using synthetic biology techniques, we hypothesize that we can increase its range of therapeutic potential by disabling its ability to integrate into the bacterial genome, and that this change will increase the number of bacteria that it can lyse. The specific aims of this project are to (1) engineer this phage to lose the ability to lysogenize (integrate into bacterial genomes) then test the effects of these modifications on bacterial host range and (2) test activity of our originally isolated phage, BCC02 as well as our engineered variant on a clinically relevant panel of patho-adapted isolates from patients with CF. We propose to use transformation-associated recombination (TAR) cloning methods to target the lysogeny control region of the BCC02 genome for removal. We hypothesize that loss of integration ability will force this phage into an obligately lytic lifestyle, where it will lyse all bacteria it is able to infect. Successful completion of this project will determine the feasibility of engineering obligately lytic Burkholderia-targeting phages from Burkholderia spp. prophages, shed light on the effects of lytic lifestyle on host range, and establish the utility of these phages for tackling particularly problematic clinical infections. In addition, this study may produce a Bcc- targeting phage that is primed for development to be used for phage therapy.

Magnetic resonance true temperature imaging with high spatial and temporal resolution

ABSTRACT The knowledge of temperature and temperature distribution within the brain can be critical to understanding the healthy and diseased brain, its response to acute injury, and in monitoring critically important thermal interventions. There are several temperature sensitive properties such relaxation rates and the proton resonance frequency shift (PRFS) that can be measured with magnetic resonance imaging (MRI) methods but these methods can only measure temperature change. The PRFS method, which provides the most accurate measurement of temperature change can only measure true tissue temperature if the starting true temperature distribution is known. Fortunately, MR spectroscopy (MRS) methods have been developed that show great promise in the measurement of true temperature. These methods rely on the detection of a temperature independent spectral peak of protons bound to carbon atoms in high concentration metabolites, such as N- acetylaspartate (NAA), creatine (Cr) and choline (Cho) which can be used as a reference for the temperature dependent spectral peak of water protons. Both single voxel spectroscopy (SVS) methods and MRS imaging (MRSI) methods have been described but are slow because of the long readout time needed to achieve adequate spectral resolution and the need to perform multiple averages due to the low signal being measured. Echo-planar spectroscopic imaging (EPSI) speeds up MRSI by interleaving an oscillating imaging gradient to spatially encode one of the imaging dimensions simultaneously with spectral readout. Unfortunately, SVS, MRSI, and even EPSI are unsuitable for clinical applications because of the low spatial resolution (voxel size 1 cm3) and temporal resolution (multiple minutes). The goal of this project is to develop an MRI technique that can measure true temperature in the whole brain at spatial and temporal resolutions that enable clinical utility for acutely assessing and longitudinally monitoring healthy and diseased brain tissue, and real time monitoring of thermal interventional therapies. This innovative true temperature measurement technique combines EPSI, for low resolution background field measurements, with PRFS for high spatial and temporal resolution water proton measurements. While conventional EPSI methods interleave volumetric acquisitions with and without water suppression, we propose an innovative modification to take advantage of the very strong water signal to obtain a very high resolution, dynamic method for true temperature measurements. The MRI pulse sequence will be refined, validated (Aim 1), applied to healthy subjects and post-surgery patients at risk for infections (Aim 2), and applied to essential tremor (ET) patients during the required delay between repeated focused ultrasound sonications (Aim 3). Successful completion of the aims of this study will result in a clinically practical method to obtain true temperature measurements in the brain with a spatial and temporal resolution sufficiently high to meet the needs of monitoring focal thermal therapy treatments as well as to provide true temperature measurements over the entire brain for assessment of the state of the brain with disease, infection, and injury.

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

Neuronal population interactions between brain areas

Most brain functions involve interactions among multiple, distinct areas or nuclei. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Using a population approach, we found that interactions between early visual cortical areas (V1 and V2) occur through a low-dimensional bottleneck, termed a communication subspace. In this talk, I will focus on the statistical methods we have developed for studying interactions between brain areas. First, I will describe Delayed Latents Across Groups (DLAG), designed to disentangle concurrent, bi-directional (i.e., feedforward and feedback) interactions between areas. Second, I will describe an extension of DLAG applicable to three or more areas, and demonstrate its utility for studying simultaneous Neuropixels recordings in areas V1, V2, and V3. Our results provide a framework for understanding how neuronal population activity is gated and selectively routed across brain areas.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

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.

Implications of Vector-space models of Relational Concepts

Vector-space models are used frequently to compare similarity and dimensionality among entity concepts. What happens when we apply these models to relational concepts? What is the evidence that such models do apply to relational concepts? If we use such a model, then one implication is that maximizing surface feature variation should improve relational concept learning. For example, in STEM instruction, the effectiveness of teaching by analogy is often limited by students’ focus on superficial features of the source and target exemplars. However, in contrast to the prediction of the vector-space computational model, the strategy of progressive alignment (moving from perceptually similar to different targets) has been suggested to address this issue (Gentner & Hoyos, 2017), and human behavioral evidence has shown benefits from progressive alignment. Here I will present some preliminary data that supports the computational approach. Participants were explicitly instructed to match stimuli based on relations while perceptual similarity of stimuli varied parametrically. We found that lower perceptual similarity reduced accurate relational matching. This finding demonstrates that perceptual similarity may interfere with relational judgements, but also hints at why progressive alignment maybe effective. These are preliminary, exploratory data and I to hope receive feedback on the framework and to start a discussion in a group on the utility of vector-space models for relational concepts in general.

How People Form Beliefs

In this talk I will present our recent behavioural and neuroscience research on how the brain motivates itself to form particular beliefs and why it does so. I will propose that the utility of a belief is derived from the potential outcomes associated with holding it. Outcomes can be internal (e.g., positive/negative feelings) or external (e.g., material gain/loss), and only some are dependent on belief accuracy. We show that belief change occurs when the potential outcomes of holding it alters, for example when moving from a safe environment to a threatening environment. Our findings yield predictions about how belief formation alters as a function of mental health. We test these predictions using a linguistic analysis of participants’ web searches ‘in the wild’ to quantify the affective properties of information they consume and relate those to reported psychiatric symptoms. Finally, I will present a study in which we used our framework to alter the incentive structure of social media platforms to reduce the spread of misinformation and improve belief accuracy.

Multimodal imaging in Dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a synucleinopathy but more than half of patients with DLB also have varying degrees of tau and amyloid-β co-pathology. Identifying and tracking the pathologic heterogeneity of DLB with multi-modal biomarkers is critical for the design of clinical trials that target each pathology early in the disease at a time when prevention or delaying the transition to dementia is possible. Furthermore, longitudinal evaluation of multi-modal biomarkers contributes to our understanding of the type and extent of the pathologic progression and serves to characterize the temporal emergence of the associated phenotypic expression. This talk will focus on the utility of multi-modal imaging in DLB.

Thurstonian measurement of risk preferences: contemporary economic outlook

Recent economics literature has seen a revival of interest to psychologically-grounded theories of decision under risk. We review the recent proposals in this direction, compare it to classical estimations based on utility functions, and discuss their appropriateness using some original experimental data.

Neural mechanisms of active vision in the marmoset monkey

Human vision relies on rapid eye movements (saccades) 2-3 times every second to bring peripheral targets to central foveal vision for high resolution inspection. This rapid sampling of the world defines the perception-action cycle of natural vision and profoundly impacts our perception. Marmosets have similar visual processing and eye movements as humans, including a fovea that supports high-acuity central vision. Here, I present a novel approach developed in my laboratory for investigating the neural mechanisms of visual processing using naturalistic free viewing and simple target foraging paradigms. First, we establish that it is possible to map receptive fields in the marmoset with high precision in visual areas V1 and MT without constraints on fixation of the eyes. Instead, we use an off-line correction for eye position during foraging combined with high resolution eye tracking. This approach allows us to simultaneously map receptive fields, even at the precision of foveal V1 neurons, while also assessing the impact of eye movements on the visual information encoded. We find that the visual information encoded by neurons varies dramatically across the saccade to fixation cycle, with most information localized to brief post-saccadic transients. In a second study we examined if target selection prior to saccades can predictively influence how foveal visual information is subsequently processed in post-saccadic transients. Because every saccade brings a target to the fovea for detailed inspection, we hypothesized that predictive mechanisms might prime foveal populations to process the target. Using neural decoding from laminar arrays placed in foveal regions of area MT, we find that the direction of motion for a fixated target can be predictively read out from foveal activity even before its post-saccadic arrival. These findings highlight the dynamic and predictive nature of visual processing during eye movements and the utility of the marmoset as a model of active vision. Funding sources: NIH EY030998 to JM, Life Sciences Fellowship to JY

Application of Airy beam light sheet microscopy to examine early neurodevelopmental structures in 3D hiPSC-derived human cortical spheroids

The inability to observe relevant biological processes in vivo significantly restricts human neurodevelopmental research. Advances in appropriate in vitro model systems, including patient-specific human brain organoids and human cortical spheroids (hCSs), offer a pragmatic solution to this issue. In particular, hCSs are an accessible method for generating homogenous organoids of dorsal telencephalic fate, which recapitulate key aspects of human corticogenesis, including the formation of neural rosettes—in vitro correlates of the neural tube. These neurogenic niches give rise to neural progenitors that subsequently differentiate into neurons. Studies differentiating induced pluripotent stem cells (hiPSCs) in 2D have linked atypical formation of neural rosettes with neurodevelopmental disorders such as autism spectrum conditions. Thus far, however, conventional methods of tissue preparation in this field limit the ability to image these structures in three-dimensions within intact hCS or other 3D preparations. To overcome this limitation, we have sought to optimise a methodological approach to process hCSs to maximise the utility of a novel Airy-beam light sheet microscope (ALSM) to acquire high resolution volumetric images of internal structures within hCS representative of early developmental time points.

Towards better interoceptive biomarkers in computational psychiatry

Empirical evidence and theoretical models both increasingly emphasize the importance of interoceptive processing in mental health. Indeed, many mood and psychiatric disorders involve disturbed feelings and/or beliefs about the visceral body. However, current methods to measure interoceptive ability are limited in a number of ways, restricting the utility and interpretation of interoceptive biomarkers in psychiatry. I will present some newly developed measures and models which aim to improve our understanding of disordered brain-body interaction in psychiatric illnesses.

Lysosomal storage disorders and their unanticipated links to rare and common diseases

Lysosomal storage diseases are a group of over 70 inherited metabolic disorders, many of which have a neurodegenerative clinical course. Treatments have been developed for a subset of these disorders and are now in routine clinical use. We have found that some neurological and neurodegenerative diseases share unanticipated links to lysosomal storage diseases providing insights into disease pathogenesis. These links also suggest treatments developed for lysosomal disorders may have unanticipated utility in other rare and common diseases.

Attentional Foundations of Framing Effects

Framing effects in individual decision-making have puzzled economists for decades because they are hard, if at all, to explain with rational choice theories. Why should mere changes in the description of a choice problem affect decision-making? Here, we examine the hypothesis that changes in framing cause changes in the allocation of attention to the different options – measured via eye-tracking – and give rise to changes in decision-making. We document that the framing of a sure alternative as a gain – as opposed to a loss – in a risk-taking task increases the attentional advantage of the sure option and induces a higher choice frequency of that option – a finding that is predicted by the attentional drift-diffusion model (aDDM). The model also correctly predicts other key findings such as that the increased attentional advantage of the sure option in the gain frame should also lead quicker decisions in this frame. In addition, the data reveal that increasing risk aversion at higher stake sizes may also be driven by attentional processes because the sure option receives significantly more attention – regardless of frame – at higher stakes. We also corroborate the causal impact of framing-induced changes of attention on choice with an additional experiment that manipulates attention exogenously. Finally, to study the precise mechanisms underlying the framing effect we structurally estimate an aDDM that allows for frame and option-dependent parameters. The estimation results indicate that – in addition to the direct effects of framing-induced changes in attention on choice – the gain frame also causes (i) an increase in the attentional discount of the gamble and (ii) an increased concavity of utility. Our findings suggest that the traditional explanation of framing effects in risky choice in terms of a more concave value function in the gain domain is seriously incomplete and that attentional mechanisms as hypothesized in the aDDM play a key role.

Motor Cortex in Theory and Practice

A central question in motor physiology has been whether motor cortex activity resembles muscle activity, and if not, why not? Over fifty years, extensive observations have failed to provide a concise answer, and the topic remains much debated. To provide a different perspective, we employed a novel behavioral paradigm that affords extensive comparison between time-evolving neural and muscle activity. Single motor-cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid ’trajectory tangling’: moments where similar activity patterns led to dissimilar future patterns. Avoidance of trajectory tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Remarkably, we were able to predict motor cortex activity from muscle activity alone, by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling. Our results argue that motor cortex embeds descending commands in additional structure that ensure low tangling, and thus noise-robustness. The dominant structure in motor cortex may thus serve not a representational function (encoding specific variables) but a computational function: ensuring that outgoing commands can be generated reliably. Our results establish the utility of an emerging approach: understanding the structure of neural activity based on properties of population geometry that flow from normative principles such as noise robustness.

Human reconstruction of local image structure from natural scenes

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside examples of how the associated empirical results can be combined with targeted models to shape our understanding of the underlying perceptual mechanisms. Although the emerging view is far from complete, it challenges compartmentalized notions of bottom-up/top-down object segmentation, and suggests instead that these two modes are best viewed as an integrated perceptual mechanism.

Spanning the arc between optimality theories and data

Ideas about optimization are at the core of how we approach biological complexity. Quantitative predictions about biological systems have been successfully derived from first principles in the context of efficient coding, metabolic and transport networks, evolution, reinforcement learning, and decision making, by postulating that a system has evolved to optimize some utility function under biophysical constraints. Yet as normative theories become increasingly high-dimensional and optimal solutions stop being unique, it gets progressively hard to judge whether theoretical predictions are consistent with, or "close to", data. I will illustrate these issues using efficient coding applied to simple neuronal models as well as to a complex and realistic biochemical reaction network. As a solution, we developed a statistical framework which smoothly interpolates between ab initio optimality predictions and Bayesian parameter inference from data, while also permitting statistically rigorous tests of optimality hypotheses.

Context-dependent neural coding of utility in the frontal cortex of rats

COSYNE 2025

Characterizing the utility of novel channelrhodopsin mutants for activation of the auditory pathway

Mice regulate their attentional intensity and arousal to exploit increases in task utility

An atopic dermatitis mouse model reveals potential utility for atopic dermatitis-generated comorbid depression brain circuitry studies

FENS Forum 2024

Clinical utility of advanced neuroimaging modalities for epilepsy surgery assessment

FENS Forum 2024

Exploring the combinatorial, diagnostic utility of multimodal biomarkers in differentially diagnosing Dementia with Lewy Bodies from Alzheimer’s through predictive statistical modelling

FENS Forum 2024