arXiv subjects

Adam Kepecs

Publications and source records attributed to Adam Kepecs.

3 recordsLinked to original sources

arXiv2025

Digital Modeling of Spatial Pathway Activity from Histology Reveals Tumor Microenvironment Heterogeneity

Spatial transcriptomics (ST) enables simultaneous mapping of tissue morphology and spatially resolved gene expression, offering unique opportunities to study tumor microenvironment heterogeneity. Here, we introduce a computational framework that predicts spatial pathway activity directly from hematoxylin-and-eosin-stained histology images at microscale resolution 55 and 100 um. Using image features derived from a computational pathology foundation model, we found that TGFb signaling was the most accurately predicted pathway across three independent breast and lung cancer ST datasets. In 87-88% of reliably predicted cases, the resulting spatial TGFb activity maps reflected the expected contrast between tumor and adjacent non-tumor regions, consistent with the known role of TGFb in regulating interactions within the tumor microenvironment. Notably, linear and nonlinear predictive models performed similarly, suggesting that image features may relate to pathway activity in a predominantly linear fashion or that nonlinear structure is small relative to measurement noise. These findings demonstrate that features extracted from routine histopathology may recover spatially coherent and biologically interpretable pathway patterns, offering a scalable strategy for integrating image-based inference with ST information in tumor microenvironment studies.

q-bio.QM↗

arXiv2018

A Reconfigurable Nanophotonics Platform for Sub-Millisecond, Deep Brain Neural Stimulation

Nanophotonics provides the ability to rapidly and precisely reconfigure light beams on a compact platform. Infrared nanophotonic devices are widely used in data communications to overcome traditional bandwidth limitations of electrical interconnects. Nanophotonic devices also hold promise for use in biological applications that require visible light, but this has remained technically elusive due to the challenges of reconfiguring and guiding light at these smaller dimensions. In neuroscience, for example, there is a need for implantable optical devices to optogenetically stimulate neurons across deep brain regions with the speed and precision matching state-of-the-art recording probes. Here we demonstrate the first platform for reconfigurable nanophotonic devices in the visible wavelength range and show its application in vivo in the brain. We demonstrate an implantable probe endowed with the ability to rapidly switch and route multiple optical beams using a nanoscale switching network. Each switch consists of a silicon nitride waveguide structure that can be reconfigured by electrically tuning the phase of light and is designed for robustness to fabrication variation, enabling scalable devices. By implanting our probe in mouse visual cortex, we demonstrate in vivo the ability to stimulate identified sets of neurons across layers to produce multi-neuron spike patterns and record them simultaneously with sub-millisecond temporal precision. This nanophotonic platform can be scaled up and integrated with high-density neural recording technologies, opening the door to implantable probe technologies that are able to simultaneously record and stimulate the activity of large neural populations at distant regions of the brain with sub-millisecond precision. We expect this platform will enable researchers to gain a deeper understanding into the spatio-temporal precision of the neural code.

physics.app-ph↗

arXiv2014

Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables. The resulting diversity of neural tuning often obscures the represented information. Here we introduce a novel dimensionality reduction technique, demixed principal component analysis (dPCA), which automatically discovers and highlights the essential features in complex population activities. We reanalyze population data from the prefrontal areas of rats and monkeys performing a variety of working memory and decision-making tasks. In each case, dPCA summarizes the relevant features of the population response in a single figure. The population activity is decomposed into a few demixed components that capture most of the variance in the data and that highlight dynamic tuning of the population to various task parameters, such as stimuli, decisions, rewards, etc. Moreover, dPCA reveals strong, condition-independent components of the population activity that remain unnoticed with conventional approaches.

q-bio.NC↗