A sensory-motor theory of the neocortex.

Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.

Posterior Uveitis in Ocular-Involving Chronic Lymphocytic Leukemia and the Utility of Negative MYD88 L265P Testing in the Diagnosis.

Introduction: The aim of this study was to investigate if a negative test result for MYD88 L265P mutation, associated with vitreoretinal lymphoma (VRL) and primary CNS lymphoma, in liquid biopsies from intraocular fluids can be a useful adjuvant test to diagnose chronic lymphocytic leukemia in clinically challenging cases. Case Presentations: We selected patients with a past medical history or examinations findings suspicious for intraocular lymphoma. We evaluated both vitreous and aqueous humor-derived (AHD) MYD88 L265P mutation from patients that had suspected intraocular lymphoma that warranted a liquid biopsy procedure. Gold-standard cytopathology, flow cytometry, and gene rearrangement studies were also performed. All 4 patients had negative AHD MYD88 L265P mutation testing. Gold-standard testing (cytology) either showed paucicellular specimens (1/4) or specimens with high background inflammation (3/4). One case showed a rare B-cell clonal population (CD5+, Kappa-restricted by flow cytometry), but this was not sufficient to make any definitive diagnosis. All patients were subsequently initiated on systemic therapy and had improvement in their disease burden. Conclusions: Negative AHD MYD88 L265P mutation testing can serve as an adjuvant molecular test to diagnose difficult cases of intraocular CLL.

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others.

Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex.

We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network's hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world.

Quiescence enables unrestricted cell fate in naive embryonic stem cells.

Quiescence in stem cells is traditionally considered as a state of inactive dormancy or with poised potential. Naive mouse embryonic stem cells (ESCs) can enter quiescence spontaneously or upon inhibition of MYC or fatty acid oxidation, mimicking embryonic diapause in vivo. The molecular underpinning and developmental potential of quiescent ESCs (qESCs) are relatively unexplored. Here we show that qESCs possess an expanded or unrestricted cell fate, capable of generating both embryonic and extraembryonic cell types (e.g., trophoblast stem cells). These cells have a divergent metabolic landscape comparing to the cycling ESCs, with a notable decrease of the one-carbon metabolite S-adenosylmethionine. The metabolic changes are accompanied by a global reduction of H3K27me3, an increase of chromatin accessibility, as well as the de-repression of endogenous retrovirus MERVL and trophoblast master regulators. Depletion of methionine adenosyltransferase Mat2a or deletion of Eed in the polycomb repressive complex 2 results in removal of the developmental constraints towards the extraembryonic lineages. Our findings suggest that quiescent ESCs are not dormant but rather undergo an active transition towards an unrestricted cell fate.

Cell-Free DNA Extraction of Vitreous and Aqueous Humor Specimens for Diagnosis and Monitoring of Vitreoretinal Lymphoma.

Vitreoretinal lymphoma (VRL) represents an aggressive lymphoma, often categorized as primary central nervous system diffuse large B-cell lymphoma. To diagnose VRL, specimens such as vitreous humor and, more recently, aqueous humor are collected. Diagnostic testing for VRL on these specimens includes cytology, flow cytometry, and molecular testing. However, both cytopathology and flow cytometry, along with molecular testing using cellular DNA, necessitate intact whole cells. The challenge lies in the fact that vitreous and aqueous humor typically have low cellularity, and many cells get destroyed during collection, storage, and processing. Moreover, these specimens pose additional difficulties for molecular testing due to the high viscosity of vitreous humor and the low volume of both vitreous and aqueous humor. This study proposes a method for extracting cell-free DNA from vitreous and aqueous specimens. This approach complements the extraction of cellular DNA or allows the cellular component of these specimens to be utilized for other diagnostic methods, including cytology and flow cytometry.

RHEGMATOGENOUS RETINAL DETACHMENT AFTER INITIATION OF PILOCARPINE HYDROCHLORIDE OPHTHALMIC SOLUTION 1.25% FOR TREATMENT OF PRESBYOPIA.

BACKGROUND/PURPOSE: Retinal detachment has previously been reported in association with topical miotic use for the treatment of glaucoma. Pilocarpine hydrochloride 1.25% was recently approved by the Food and Drug Administration for the treatment of presbyopia, with no reports of associated retinal detachments in the clinical trial data. METHODS: Case report. RESULTS: Two novel cases of unilateral retinal detachment occurring within 10 days of the initiation of pilocarpine 1.25% for the treatment of presbyopia were described. The patients were pseudophakic men in their 60s or 70s with preexisting retinal detachment risk factors, such as high myopia, lattice degeneration, and prior retinal detachment. Both affected eyes were treated with pars plana vitrectomy and gas endotamponade with an uncomplicated postoperative course. CONCLUSION: Retinal detachment may be associated with the use of pilocarpine 1.25%. Caution should be used when considering prescribing this medication in patients with preexisting retinal abnormality.

Local field potentials and single unit dynamics in motor cortex of unconstrained macaques during different behavioral states.

Different sleep stages have been shown to be vital for a variety of brain functions, including learning, memory, and skill consolidation. However, our understanding of neural dynamics during sleep and the role of prominent LFP frequency bands remain incomplete. To elucidate such dynamics and differences between behavioral states we collected multichannel LFP and spike data in primary motor cortex of unconstrained macaques for up to 24 h using a head-fixed brain-computer interface (Neurochip3). Each 8-s bin of time was classified into awake-moving (Move), awake-resting (Rest), REM sleep (REM), or non-REM sleep (NREM) by using dimensionality reduction and clustering on the average spectral density and the acceleration of the head. LFP power showed high delta during NREM, high theta during REM, and high beta when the animal was awake. Cross-frequency phase-amplitude coupling typically showed higher coupling during NREM between all pairs of frequency bands. Two notable exceptions were high delta-high gamma and theta-high gamma coupling during Move, and high theta-beta coupling during REM. Single units showed decreased firing rate during NREM, though with increased short ISIs compared to other states. Spike-LFP synchrony showed high delta synchrony during Move, and higher coupling with all other frequency bands during NREM. These results altogether reveal potential roles and functions of different LFP bands that have previously been unexplored.

Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning.

There is growing interest in predictive coding as a model of how the brain learns through predictions and prediction errors. Predictive coding models have traditionally focused on sensory coding and perception. Here we introduce active predictive coding (APC) as a unifying model for perception, action, and cognition. The APC model addresses important open problems in cognitive science and AI, including (1) how we learn compositional representations (e.g., part-whole hierarchies for equivariant vision) and (2) how we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex state dynamics and abstract actions from simpler dynamics and primitive actions. By using hypernetworks, self-supervised learning, and reinforcement learning, APC learns hierarchical world models by combining task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We illustrate the applicability of the APC model to active visual perception and hierarchical planning. Our results represent, to our knowledge, the first proof-of-concept demonstration of a unified approach to addressing the part-whole learning problem in vision, the nested reference frames learning problem in cognition, and the integrated state-action hierarchy learning problem in reinforcement learning.

Molecular Analysis of Liquid Vitreous Biopsy Reveals Occult Lymphoma Following Cytology-Negative Biopsies of the Brain and Vitreous.

PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare but deadly malignancy that principally affects adults in the fifth and sixth decades of life. Despite diagnostic advances in analyses of cerebral spinal fluid and neuroimaging, definitive diagnosis of PCNSL requires primary brain tissue biopsy. While small neurosurgical biopsy volumes are pursued to minimize removal of normal brain tissue, the spatial margins to precisely biopsy pathologic tissue are narrow and can result in missed diagnoses. Furthermore, prior steroid treatment can significantly reduce tumor burden increasing the likelihood of a non-diagnostic biopsy. METHODS: A retrospective case report from a tertiary referral center using a combination of neuroradiological studies, sterotactic tissue biopsy, and molecular testing for genome mutations. RESULTS: A 72-year-old woman with strong suspicion for PCNSL clinically and radiologically, but cerebral spinal fluid and primary brain tissue biopsy were negative for tumor. However, vitreous liquid biopsy molecular testing for a MYD88 mutation as well as B-cell clonality (IGH/IGK rearrangement) were positive, indicating the presence of secondary vitreoretinal lymphoma from PCNSL. Only after autopsy of her brain was histopathological and immunohistochemical evidence of PCNSL confirmed. CONCLUSION: This case illustrates the unique contribution of liquid biopsy neuropathology-oriented molecular testing in a challenging case with high clinical suspicion of PCNSL in which gold-standard diagnostic testing failed to yield a diagnosis.

Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars.

Human vision, thought, and planning involve parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emulate this capability using neural networks, but a generative model formulation has been lacking. Generative models that leverage compositionality, recursion, and part-whole hierarchies are thought to underlie human concept learning and the ability to construct and represent flexible mental concepts. We introduce Recursive Neural Programs (RNPs), a neural generative model that addresses the part-whole hierarchy learning problem by modeling images as hierarchical trees of probabilistic sensory-motor programs. These programs recursively reuse learned sensory-motor primitives to model an image within different spatial reference frames, enabling hierarchical composition of objects from parts and implementing a grammar for images. We show that RNPs can learn part-whole hierarchies for a variety of image datasets, allowing rich compositionality and intuitive parts-based explanations of objects. Our model also suggests a cognitive framework for understanding how human brains can potentially learn and represent concepts in terms of recursively defined primitives and their relations with each other.

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.

Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents' emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.

Expressive probabilistic sampling in recurrent neural networks.

In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neural dynamics can sample from arbitrary distributions is still lacking. We use tools from functional analysis and stochastic differential equations to explore the minimum architectural requirements for $\textit{recurrent}$ neural circuits to sample from complex distributions. We first consider the traditional sampling model consisting of a network of neurons whose outputs directly represent the samples (sampler-only network). We argue that synaptic current and firing-rate dynamics in the traditional model have limited capacity to sample from a complex probability distribution. We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution. We call such circuits reservoir-sampler networks (RSNs). We propose an efficient training procedure based on denoising score matching that finds recurrent and output weights such that the RSN implements Langevin sampling. We empirically demonstrate our model's ability to sample from several complex data distributions using the proposed neural dynamics and discuss its applicability to developing the next generation of sampling-based brain models.

The Cost of Standard and Complex Pars Plana Vitrectomy for Retinal Detachment Repair Exceeds Its Reimbursement.

OBJECTIVE: To measure the total costs and reimbursements associated with standard and complex pars plana vitrectomy using time-driven activity-based costing (TDABC). DESIGN: Economic analysis at a single academic institution. SUBJECTS: Patients who underwent standard or complex pars plana vitrectomy (PPV; Current Procedural Terminology codes 67108 and 67113) at the University of Michigan in the calendar year 2021. METHODS: Process flow mapping for standard and complex PPVs was used to determine the operative components. The internal anesthesia record system was used to calculate time estimates, and financial calculations were constructed from published literature and internal sources. A TDABC analysis was used to determine the costs of standard and complex PPVs. Average reimbursement was based on Medicare rates. MAIN OUTCOME MEASURES: The primary outcomes were the total costs for standard and complex PPVs and the resulting net margin at current Medicare reimbursement levels. The secondary outcomes were the differential in surgical times, costs, and margin for standard and complex PPV. RESULTS: Over the 2021 calendar year, a total of 270 standard and 142 complex PPVs were included in the analysis. Complex PPVs were associated with significantly increased anesthesia time (52.28 minutes; P < 0.001), operating room time (51.28 minutes; P < 0.0001), surgery time (43.64 minutes; P < 0.0001), and postoperative time (25.95 minutes; P < 0.0001). The total day-of-surgery costs were $5154.59 and $7852.38 for standard and complex PPVs, respectively. Postoperative visits incurred an additional cost of $327.84 and $353.86 for standard and complex PPV, respectively. The institution-specific facility payments were $4505.50 and $4935.14 for standard and complex PPV, respectively. Standard PPV yielded a net negative margin of -$976.93, whereas complex PPV yielded a net negative margin of -$3271.10. CONCLUSIONS: This analysis demonstrated that Medicare reimbursement is inadequate in covering the costs of PPV for retinal detachment, with a particularly large negative margin for more complex cases. These findings demonstrate that additional steps may be necessary to mitigate adverse economic incentives so that patients continue to have timely access to care to achieve optimal visual outcomes after retinal detachment. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any materials discussed in this article.

Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision.

Most detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time consuming. Developing natural language processing (NLP) methods for structuring RWD is thus essential for scaling real-world evidence generation. We propose leveraging patient-level supervision from medical registries, which are often readily available and capture key patient information, for general RWD applications. We conduct an extensive study on 135,107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states. Our deep-learning methods attain test area under the receiver operating characteristic curve (AUROC) values of 94%-99% for key tumor attributes and comparable performance on held-out data from separate health systems and states. Ablation results demonstrate the superiority of these advanced deep-learning methods. Error analysis shows that our NLP system sometimes even corrects errors in registrar labels.

Responses of Cortical Neurons to Intracortical Microstimulation in Awake Primates.

Intracortical microstimulation (ICMS) is commonly used in many experimental and clinical paradigms; however, its effects on the activation of neurons are still not completely understood. To document the responses of cortical neurons in awake nonhuman primates to stimulation, we recorded single-unit activity while delivering single-pulse stimulation via Utah arrays implanted in primary motor cortex (M1) of three macaque monkeys. Stimuli between 5 and 50 µA delivered to single channels reliably evoked spikes in neurons recorded throughout the array with delays of up to 12 ms. ICMS pulses also induced a period of inhibition lasting up to 150 ms that typically followed the initial excitatory response. Higher current amplitudes led to a greater probability of evoking a spike and extended the duration of inhibition. The likelihood of evoking a spike in a neuron was dependent on the spontaneous firing rate as well as the delay between its most recent spike time and stimulus onset. Tonic repetitive stimulation between 2 and 20 Hz often modulated both the probability of evoking spikes and the duration of inhibition; high-frequency stimulation was more likely to change both responses. On a trial-by-trial basis, whether a stimulus evoked a spike did not affect the subsequent inhibitory response; however, their changes over time were often positively or negatively correlated. Our results document the complex dynamics of cortical neural responses to electrical stimulation that need to be considered when using ICMS for scientific and clinical applications.

Neural co-processors for restoring brain function: results from a cortical model of grasping.

Objective.A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and different objectives. Traditional approaches, such as those currently used for deep brain stimulation, have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation.Approach.To achieve goal-directed closed-loop neurostimulation, we propose the use of brain co-processors, devices which exploit artificial intelligence to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. Here we investigate a specific type of co-processor called a 'neural co-processor' which uses artificial neural networks and deep learning to learn optimal closed-loop stimulation policies. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for futurein vivotests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity in preparation for futurein vivotests.Main results.Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function in the range 75%-90%.Significance.Our results provide the first proof-of-concept demonstration, using computer simulations, of a neural co-processor for adaptive activity-dependent closed-loop neurostimulation for optimizing a rehabilitation goal after injury. While a significant gap remains between simulations andin vivoapplications, our results provide insights on how such co-processors may eventually be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.