Biology subjects

Graves, A. R.

Publications and source records attributed to Graves, A. R..

8 recordsLinked to original sources

bioRxiv2026

Neuropixels 1536 Channel Quad Base probe reveals brain-wide communication underlying flexible sensorimotor sequences

The brain orchestrates billions of neurons spanning hundreds of regions to drive behavior. High-density electrophysiology has provided insights into this process, but monitoring brain-wide patterns of spiking activity demands new tools. Here we present Neuropixels 2.0 Quad Base, a 1536-channel probe with four times the simultaneous recording capacity of state-of-art Neuropixels 2.0. Using two Quad Base probes, we performed 3072-channel recordings in mice trained to produce complex sequences of goal-directed licks. We decoded behavioral kinematics from neural populations across more than 20 brain regions and identified the spatiotemporal emergence of motor decision signals. The probes high channel count further revealed expanded inter-neuronal functional subnetworks and latent dynamics across multiple areas--features significantly undersampled when channels were subsampled to mimic standard Neuropixels 2.0. Neuropixels 2.0 Quad Base offers a powerful tool to explore neural encoding of cognition and behavior at grand scale.

neuroscience↗

bioRxiv2025

Uncertainty-Gated Min-Cost Flows for In Vivo NanoScale Synaptic Plasticity Tracking

Synapses are the fundamental unit of neural connectivity, exhibiting dynamic functional and structural changes that enable the brain to learn, adapt, and form memories. Recent advances in fluorescent labeling of endogenous proteins offer an opportunity to image synaptic strength in vivo and study mechanisms underlying adaptive neural computation. Studying synaptic dynamics requires tracking signals of small, densely packed synapses over days as they change in size, position, and intensity between imaging sessions, and may even appear or disappear. Associating >50,000 dynamic, submicrometer particles across time is difficult, even for state-of-the-art algorithms. Moreover, most algorithms assign equal weight to the lateral (XY) and noisier axial (Z) dimensions, reducing performance. To address these challenges and accurately track synapses in vivo, we developed SynTrack. We formulate tracking as a Maximum A Posteriori estimation problem that identifies the K most likely disjoint paths in a Hidden Markov Model, solved using min-cost circulation optimization. An anisotropic uncertainty model accounts for poorer axial resolution, and a fully temporally connected spatio-temporal graph overcomes long-term occlusions. SynTrack achieves a mean displacement of 0.50 {micro}m with a Multiple Object Tracking Accuracy (MOTA) score of 89.8%, on par with expert annotators but with substantially increased speed and scalability. In a large-scale volume imaged over two weeks, SynTrack reconstructed 74,000 synapse trajectories detected in 4.9 out of 8 imaging sessions on average, with 18,000 synapses tracked in at least seven sessions. We present a state-of-the-art algorithm capable of high-fidelity longitudinal tracking of individual synapses in behaving mice at an unprecedented scale.

neuroscience↗

bioRxiv2025

Perinatal Exposure to Metal Mixtures Disrupts Neuronal Function and Behavior

BackgroundEnvironmental exposure to heavy metals such as lead (Pb), arsenic (As), hexavalent chromium [Cr(VI)], and cadmium (Cd), (PACC), is linked to neurodevelopmental disorders. These metals often co-occur in contaminated environments, but their combined effects on brain development remain poorly understood. ObjectiveTo test the hypothesis that perinatal exposure to a mixture of environmentally relevant levels of Pb, As, Cd, and Cr(VI), causes developmental defects in cognition, behavior, and neuronal function. MethodsFemale C57BL/6J mice were exposed to either a single metal or the PACC mixture in drinking water. Exposure began two weeks preconception and continued until weaning at postnatal day 21. Juvenile mice were tested at 4-5 weeks of age in open field (locomotion), novel object recognition (short-term memory), Y-maze (working memory), and elevated plus maze (anxiety-like behavior). A subset of animals underwent Whole-cell patch-clamp recordings in the medial prefrontal cortex (mPFC) and hippocampal CA1 neurons. ResultsPerinatal exposure to PACC metal mixture increased anxiety-like behavior and impaired short-term memory but not locomotion or working memory. Pyramidal neurons in mPFC and hippocampal CA1 displayed increased intrinsic excitability, mPFC neurons also showed elevated amplitude in spontaneous excitatory postsynaptic currents. DiscussionOur findings suggest that perinatal exposure to the PACC metal mixture impairs cognition, increases anxiety-like behavior, and alters neuronal function in specific brain regions of juvenile mice, leading to disruption in neuronal function and behavior later in life. Further studies are needed to provide mechanistic insight into how perinatal heavy metal exposure affects neuronal development. HighlightsO_LIPerinatal exposure to a metal mixture including lead (Pb), arsenic (As), hexavalent chromium (Cr(VI)), and cadmium (Cd), collectively termed PACC metal mixture--impairs cognition and increases anxiety in mice. C_LIO_LINeuronal excitability and synaptic transmission are altered in medial prefrontal cortex after PACC metal mixture exposure. C_LIO_LIPACC mixture exposure decreases short-term memory in both males and females, and increases anxiety in males C_LIO_LIPrincipal component and clustering analyses reveal that PACC mixture exposure and control mice form distinct, nonoverlapping populations in physiological-behavioral space. C_LIO_LIEnvironmentally relevant PACC metal mixtures exert stronger effects than individual metals alone. C_LI

pharmacology and toxicology↗

bioRxiv2025

SLAy-ing oversplitting errors in high-density electrophysiology spike sorting

The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed with automated methods that use waveform template matching to isolate putative single neurons. While scalable, automated methods rely on assumptions that often fail to account for biophysical changes in action potential waveforms, leading to systematic oversplitting of individual neurons into multiple putative units. Consequently, manual curation of these errors, which is both time-consuming and lacking in reproducibility, remains necessary. To improve efficiency and reproducibility in the spike-sorting pipeline, we introduce the Spike-sorting Lapse Amelioration System (SLAy), an algorithm that automatically merges oversplit spike units. SLAy employs two novel metrics: (1) a waveform similarity metric that uses a neural network to obtain spatially informed, nonlinear waveform representations, and (2) a cross-correlogram significance metric based on the earth movers distance between the observed and null cross-correlograms. To improve reproducibility and remove the need for manual tuning, we also develop an automatic parameter setting procedure for SLAy that accounts for dataset-specific characteristics. On simulated oversplitting across a diverse set of animal models, brain regions, and probe geometries, SLAy substantially outperforms an existing merging algorithm, achieving high recall without merging extraneous units. On the original datasets without simulated oversplitting, SLAy recovers [~] 95% of merges found by human curators and human curators agree with [~] 90% of merges suggested by SLAy. SLAy leverages multithreading for computational efficiency, running in less than 10 minutes for all recordings we tested. SLAy is also compatible with SpikeInterface, making it a practical and flexible solution for large-scale ephys data analysis across acquisition systems and spike sorters.

neuroscience↗

bioRxiv2025

Automatic detection of fluorescently labeled synapses in volumetric in vivo imaging data

Synapses are submicron structures that connect and enable communication between neurons. Many forms of learning are thought to be encoded by synaptic plasticity, wherein the strength of specific synapses is regulated by modulating expression of neurotransmitter receptors. For instance, regulation of AMPA-type glutamate receptors is a central mechanism controlling the formation and recollection of long-term memories. A critical step in understanding how synaptic plasticity drives behavior is thus to directly observe, i.e., image, fluorescently labeled synapses in living tissue. However, due to their small size and incredible density -- with one [~]0.5 {micro}m diameter synapse every cubic micron -- accurately detecting individual synapses and segmenting each from its closely abutting neighbors is challenging. To overcome this, we trained a convolutional neural network to simultaneously detect and separate densely labeled synapses. These tools significantly increased the accuracy and scale of synapse detection, enabling segmentation of hundreds of thousands of individual synapses imaged in living mice.

neuroscience↗

bioRxiv2022

Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

Synaptic plasticity encodes learning as changes in the strength of synapses, sub-micron structures that mediate communication between brain cells. Due to their small size and high density, synapses are extremely difficult to image in vivo, limiting our ability to directly relate synaptic plasticity with behavior. Here, we developed a combination of computational and biological methods to overcome these challenges. First, we trained a deep learning image restoration algorithm that combines the advantages of ex vivo super-resolution and in vivo imaging modalities to overcome limitations specific to each optical system. Applied to in vivo images from transgenic mice expressing fluorescently labeled synaptic proteins, this restoration algorithm super-resolved diffraction-limited synapses, enabling identification and logitudinal tracking of synaptic plasticity underlying behavior with unprecedented spatial resolution. More generally, our method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution.

neuroscience↗

bioRxiv2021

KIBRA-PKCγ signaling pathway modulates memory performance in mice and humans

Human memory is a polygenic cognitive trait that is fundamental to individual competence. Genome-wide association studies (GWAS) have identified KIBRA as a novel gene associated with human memory performance. KIBRA interacts with AMPA receptors (AMPARs) and proteins essential for synaptic plasticity. The deletion of Kibra in mice impairs synaptic plasticity and learning and memory. However, the molecular basis through which KIBRA regulates dynamic AMPAR trafficking underlying synaptic plasticity is still unknown. Here we report that KIBRA interacts with the neuronal specific kinase PKC{gamma} to modulate AMPAR trafficking upon learning, and KIBRA-PKC{gamma} signaling pathway also associates with human memory performance. We find PKC{gamma} is an essential kinase that phosphorylates AMPARs upon learning, and the loss of KIBRA in mouse brain impedes PKC{gamma}-AMPAR interaction. Activation of PKC{gamma} enables KIBRA to recruit phosphorylated AMPARs to the synapse to promote LTP and learning. We further performed transcriptomic and genetic analyses in human postmortem brain samples, and behavioral and fMRI evaluations in living human subjects, to demonstrate the genetic interactions between KIBRA and PRKCG on memory performance and memory associated physiological engagement of the hippocampal memory system. Overall, our results support that the KIBRA-PKC{gamma} signaling pathway is crucial for modulating memory performance in mice and humans.

neuroscience↗

bioRxiv2020

Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors

Elucidating how synaptic molecules such as AMPA receptors mediate neuronal communication and tracking their dynamic expression during behavior is crucial to understand cognition and disease, but current technological barriers preclude large-scale exploration of molecular dynamics in vivo. We have developed a suite of innovative methodologies that break through these barriers: a new knockin mouse line with fluorescently tagged endogenous AMPA receptors, two-photon imaging of hundreds of thousands of labeled synapses in behaving mice, and computer-vision-based automatic synapse detection. Using these tools, we can longitudinally track how the strength of synapses changes during behavior. We used this approach to generate an unprecedentedly detailed spatiotemporal map of synaptic plasticity underlying sensory experience. More generally, these tools can be used as an optical probe capable of measuring functional synapse strength across entire brain areas during any behavioral paradigm, describing complex system-wide changes with molecular precision.

neuroscience↗