Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance.

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

A patient-centered textbook outcome measure effectively discriminates contemporary elective open abdominal aortic aneurysm repair quality.

BACKGROUND: There is persistent controversy surrounding the merit of surgical volume benchmarks being used solely as a sufficient proxy for assessing the quality of open abdominal aortic aneurysm (AAA) repair. Importantly, operative volume quotas may fail to reflect a more nuanced and comprehensive depiction of surgical outcomes most relevant to patients. Accordingly, we herein propose a patient-centered textbook outcome (TO) for AAA repair that is analogous to other large magnitude extirpative operations performed in other surgical specialties, and test its feasibility to discriminate hospital performance using Society for Vascular Surgery (SVS) volume guidelines. METHODS: All elective open infrarenal AAA repairs (OAR) in the SVS-Vascular Quality Initiative were examined (2009-2022). The primary end point was a TO, defined as a composite of no in-hospital complication or reintervention/reoperation, length of stay of ≤10 days, home discharge, and 1-year survival rates. The discriminatory ability of the TO measure was assessed by comparing centers that did or did not meet the SVS annual OAR volume threshold recommendation (high volume ≥10 OARs/year; low volume <10 OARs/year). Logistic regression and multivariable models adjusted for patient and procedure-related differences. RESULTS: A total of 9657 OARs across 198 centers were analyzed (mean age, 69.5 ± 8.4 years; female, 26%; non-White, 12%). A TO was identified in 44% (n = 4293) of the overall cohort. The incidence of individual TO components included no in-hospital complication (61%), no in-hospital reintervention or reoperation (92%), length of stay of ≤10 days (78%), home discharge (76%), and 1-year survival (91%). Median annual center volume was 6 (interquartile range, 3-10) and a majority of centers did not meet the SVS volume suggested threshold (<10 OARs/year, n = 148 [74%]). However, most patients (6265 of 9657 [65%]) underwent OAR in high-volume hospitals. When comparing high- and low-volume centers, a TO was more likely to occur in high-volume institutions: ≥10 OARs/year (46%) vs <10 OARs/year (42%; P = .0006). The association of a protective effect for higher center volume remained after risk adjustment (odds ratio, 1.1; 95% confidence interval, 1.05-1.26; P = .003). CONCLUSIONS: TOs for elective OAR reflect a more nuanced and comprehensive patient centered proxy to measure care delivery, consistent with other surgical specialties. Surprisingly, a TO was achieved in <50% of elective AAA cases nationally. Although the likelihood of a TO seems to correlate with SVS center volume recommendations, it more importantly reflects elements which may be prioritized by patients and thus offers insights into further improving real-world AAA care.

Multi-day Neuron Tracking in High Density Electrophysiology Recordings using EMD.

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 84% average recovery rate.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks.

BACKGROUND: Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered. NEW METHOD: We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task. RESULTS: We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored. COMPARISON WITH EXISTING METHODS: Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data. CONCLUSIONS: Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Interventions in Carotid Artery Surgery: An Overview of Current Management and Future Implications.

Atherosclerotic carotid artery disease has been well studied over the last half-century by multiple randomized controlled trials attempting to elucidate the appropriate modality of therapy for this disease process. Surgical techniques have evolved from carotid artery endarterectomy and transfemoral carotid artery stenting to the development of hybrid techniques in transcarotid artery revascularization. In this article, the authors provide a review of the available literature regarding operative and medical management of carotid artery disease.

Revealing abrupt transitions from goal-directed to habitual behavior.

A fundamental tenet of animal behavior is that decision-making involves multiple 'controllers.' Initially, behavior is goal-directed, driven by desired outcomes, shifting later to habitual control, where cues trigger actions independent of motivational state. Clark Hull's question from 1943 still resonates today: "Is this transition abrupt, or is it gradual and progressive?"1 Despite a century-long belief in gradual transitions, this question remains unanswered2,3 as current methods cannot disambiguate goal-directed versus habitual control in real-time. Here, we introduce a novel 'volitional engagement' approach, motivating animals by palatability rather than biological need. Offering less palatable water in the home cage4,5 reduced motivation to 'work' for plain water in an auditory discrimination task when compared to water-restricted animals. Using quantitative behavior and computational modeling6, we found that palatability-driven animals learned to discriminate as quickly as water-restricted animals but exhibited state-like fluctuations when responding to the reward-predicting cue-reflecting goal-directed behavior. These fluctuations spontaneously and abruptly ceased after thousands of trials, with animals now always responding to the reward-predicting cue. In line with habitual control, post-transition behavior displayed motor automaticity, decreased error sensitivity (assessed via pupillary responses), and insensitivity to outcome devaluation. Bilateral lesions of the habit-related dorsolateral striatum7 blocked transitions to habitual behavior. Thus, 'volitional engagement' reveals spontaneous and abrupt transitions from goal-directed to habitual behavior, suggesting the involvement of a higher-level process that arbitrates between the two.

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

Learning is thought to involve changes in glutamate receptors at synapses, submicron structures that mediate communication between neurons in the central nervous system. Due to their small size and high density, synapses are difficult to resolve in vivo, limiting our ability to directly relate receptor dynamics to animal 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. When applied to in vivo images from transgenic mice expressing fluorescently labeled glutamate receptors, this restoration algorithm super-resolved synapses, enabling the tracking of behavior-associated synaptic plasticity with high spatial resolution. This method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution.

A reputational perspective on rational framing effects.

To assess whether behaviors like framing effects are rational, researchers need to consider decision makers' goals. I argue that researchers should broaden the scope of analysis to include impression management goals. Under predictable conditions, behaviors traditionally considered irrational (e.g., loss-gain framing effects on risk preferences) can be reputationally rewarding, casting doubt on strict claims of irrationality.

Review of data processing of functional optical microscopy for neuroscience.

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging.

Optical imaging of calcium signals in the brain has enabled researchers to observe the activity of hundreds-to-thousands of individual neurons simultaneously. Current methods predominantly use morphological information, typically focusing on expected shapes of cell bodies, to better identify neurons in the field-of-view. The explicit shape constraints limit the applicability of automated cell identification to other important imaging scales with more complex morphologies, e.g., dendritic or widefield imaging. Specifically, fluorescing components may be broken up, incompletely found, or merged in ways that do not accurately describe the underlying neural activity. Here we present Graph Filtered Temporal Dictionary (GraFT), a new approach that frames the problem of isolating independent fluorescing components as a dictionary learning problem. Specifically, we focus on the time-traces-the main quantity used in scientific discovery-and learn a time trace dictionary with the spatial maps acting as the presence coefficients encoding which pixels the time-traces are active in. Furthermore, we present a novel graph filtering model which redefines connectivity between pixels in terms of their shared temporal activity, rather than spatial proximity. This model greatly eases the ability of our method to handle data with complex non-local spatial structure. We demonstrate important properties of our method, such as robustness to morphology, simultaneously detecting different neuronal types, and implicitly inferring number of neurons, on both synthetic data and real data examples. Specifically, we demonstrate applications of our method to calcium imaging both at the dendritic, somatic, and widefield scales.

Detecting and correcting false transients in calcium imaging.

Population recordings of calcium activity are a major source of insight into neural function. Large datasets require automated processing, but this can introduce errors that are difficult to detect. Here we show that popular time course-estimation algorithms often contain substantial misattribution errors affecting 10-20% of transients. Misattribution, in which fluorescence is ascribed to the wrong cell, arises when overlapping cells and processes are imperfectly defined or not identified. To diagnose misattribution, we develop metrics and visualization tools for evaluating large datasets. To correct time courses, we introduce a robust estimator that explicitly accounts for contaminating signals. In one hippocampal dataset, removing contamination reduced the number of place cells by 15%, and 19% of place fields shifted by over 10 cm. Our methods are compatible with other cell-finding techniques, empowering users to diagnose and correct a potentially widespread problem that could alter scientific conclusions.

Sequential and efficient neural-population coding of complex task information.

Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural population modes as the encoding unit and suggests partial whitening of task-specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time-dependent encodings in a simple way using random sequential dynamics as a temporal scaffold.

Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods.

BACKGROUND: The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible. NEW-METHOD: We provide a new framework for evaluating two-photon microscopy methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. RESULTS: We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. COMPARISON WITH EXISTING METHODS: NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data. CONCLUSIONS: By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.

Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

An approach to induction of tolerance to pig cardiac xenografts in neonates.

There is a continuing need for donor hearts for infants with complex congenital heart defects. The transplantation of hearts from neonatal pigs would be an alternative to human organs, particularly if donor-specific immunological tolerance could be achieved. The great majority of infant humans do not make natural (preformed) antibodies against triple-knockout (TKO) pigs (that do not express any of the three known pig antigens against which humans have natural anti-pig antibodies). The transplantation of a heart from a TKO pig into an infant would therefore minimize any risk of early antibody-mediated rejection, and, with adequate immunosuppressive therapy, prolonged graft survival may well be achieved. Total host thymectomy (commonly carried out at the time of orthotopic heart transplantation in this age group) ± residual T-cell depletion and donor-specific pig thymus tissue transplantation might induce T-cell tolerance and allow immunosuppressive therapy to be discontinued (if there is in vitro evidence of T-cell and B-cell nonresponsiveness to donor-specific pig cells). Even if tolerance were not achieved, with continuing immunosuppressive therapy, the graft would likely "bridge" the patient until a suitable allograft became available or be associated with prolonged xenograft function.

International validation of the EORTC QLQ-PRT20 module for assessment of quality of life symptoms relating to radiation proctitis: a phase IV study.

BACKGROUND: Although patients experience radiation proctitis post radiotherapy no internationally tested instruments exist to measure these symptoms. This Phase IV study tested the scale structure, reliability and validity and cross-cultural applicability of the EORTC proctitis module (QLQ-PRT23) in patients who were receiving pelvic radiotherapy. METHODS: Patients (n = 358) from six countries completed the EORTC QLQ-C30, QLQ-PRT23 and EORTC Quality of Life Group debriefing questions. Clinicians completed the EORTC Radiation Therapy Oncology Group scale. Questionnaires were completed at four time-points. The module's scale structure was examined and validated using standard psychometric analysis techniques. RESULTS: Three items were dropped from the module (QLQ-PRT23 → QLQ-PRT20). Factor analysis identified five factors in the module: bowel control; bloating and gas; emotional function/lifestyle; pain; and leakage. Inter-item correlations were within r = 0.3-0.7. Test-Retest reliability was high. All multi-item scales discriminated between patients showing symptoms and those without symptomology. The module discriminated symptoms from the clinician completed scoring and for age, gender and comorbidities. CONCLUSION: The EORTC QLQ-PRT20 is designed to be used in addition to the EORTC QLQ-C30 to measure quality of life in patients who receive pelvic radiotherapy. The EORTC QLQ-PRT20 is quick to complete, acceptable to patients, has good content validity and high reliability. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ANZCTR) ACTRN12609000972224 .

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are overdispersed relative to a Poisson distribution. Recent work (Goris, Movshon, & Simoncelli, 2014 ) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sublinear to linear to quadratic. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with a constant Fano factor. We describe a computationally efficient method for fitting this model to data and demonstrate that a majority of neurons in a V1 population are better described by a model with a nonquadratic relationship between mean and variance. Finally, we demonstrate a practical use of our model via an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.

Volumetric two-photon imaging of neurons using stereoscopy (vTwINS).

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large-scale recording of neural activity in vivo. Here, we introduce volumetric two-photon imaging of neurons using stereoscopy (vTwINS), a volumetric calcium imaging method that uses an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced 'image pairs' in the resulting 2D image, and the separation distance between projections is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a modified orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrated vTwINS by imaging neural population activity in the mouse primary visual cortex and hippocampus. Our results demonstrated that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame rate.

Short-term memory capacity in networks via the restricted isometry property.

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

A common network architecture efficiently implements a variety of sparsity-based inference problems.

The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm (LCA). Among the cost functions we examine are approximate l(p) norms (0 ≤ p ≤ 2), modified l(p)-norms, block-l1 norms, and reweighted algorithms. Of particular interest is that we show significantly increased performance in reweighted l1 algorithms by inferring all parameters jointly in a dynamical system rather than using an iterative approach native to digital computational architectures.