Human-derived cortical organoids (hCOs) recapitulate cell diversity and 3D structure found in the human brain and offer a promising model for discovery of new gene therapies targeting neurological disorders. Adeno-associated viruses (AAVs) are the most promising vehicles for non-invasive gene delivery to the central nervous system (CNS), but reliable and reproducible in vitro models to assess their clinical potential are lacking. hCOs can take on these issues as they are a physiologically relevant model to assess AAV transduction efficiency, cellular tropism, and biodistribution within the tissue parenchyma, all of which could significantly modulate therapeutic efficacy. Here, we examine a variety of naturally occurring AAV serotypes and measure their ability to transduce neurons and glia in hCOs from multiple donors. We demonstrate cell tropism driven by AAV serotype and hCO donor and quantify fractions of neurons and astrocytes transduced with GFP as well as overall hCO health.

Elevated expression of the complement component 4A (C4A) protein has been linked to an increased risk of schizophrenia (SCZ). However, there are few human models available to study the mechanisms by which C4A contributes to the development of SCZ. In this study, we established a C4A overexpressing neuroimmune cortical organoid (NICO) model, which includes mature neuronal cells, astrocytes, and functional microglia. The C4A NICO model recapitulated several neuroimmune endophenotypes observed in SCZ patients, including modulation of inflammatory genes and increased cytokine secretion. C4A expression also increased microglia-mediated synaptic uptake in the NICO model, supporting the hypothesis that synapse and brain volume loss in SCZ patients may be due to excessive microglial pruning. Our results highlight the role of C4A in the immunogenetic risk factors for SCZ and provide a human model for phenotypic discovery and validation of immunomodulating therapies.

Sean Escola, Saul Kato, and Pavan Ramkumar explain the importance of data science in their research. They have developed a simple non-parametric statistical method called the Rank-to-Group (RTG) score that identifies hierarchical confounder effects in raw data and machine learning-derived data embeddings. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in machine learning models.

The promise of machine learning (ML) to extract insights from high-dimensional datasets is tempered by confounding variables. It behooves scientists to determine if a model has extracted the desired information or instead fallen prey to bias. Due to features of natural phenomena and experimental design constraints, bioscience datasets are often organized in nested hierarchies that obfuscate the origins of confounding effects and render confounder amelioration methods ineffective. We propose a non-parametric statistical method called the rank-to-group (RTG) score that identifies hierarchical confounder effects in raw data and ML-derived embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders when linear methods fail. In a public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover crossmodal correlated variability in a multi-phenotypic biological dataset. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in ML models.

ABSTRACTDemultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/− forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

1 Massachusetts General Hospital 2 Harvard Medical School 3 University of Pennsylvania 4 Loyola University 5 University of Syracuse 6 Northwestern University 7 Sonat Consulting 8 Princeton University 9 NTT DATA Mathematical Systems Inc 10 605 11 Rockets of Awesome 12 Epoch Capital 13 University of Washington 14 IDLab-AIRO – Ghent University – imec 15 Clean Power Research 16 University of Trento 17 Columbia University 18 Georgia Tech 19 System1 Biosciences Inc 20 Research Institute for Agriculture, Fisheries and Food DOI: 10.21105/joss.01959

Cardiovascular disease is one of the most important fatality of death in today world. Heart disease prediction is a serious issue in the field of medical data analysis. A method is proposed that aims at identifying major features by applying machine learning techniques resulting in increasing the accuracy in the prediction of heart disease. Scientists have been utilizing a few machine learning methods to assist wellbeing with caring experts in the conclusion of coronary illness. The World Health Organization (WHO) insight gives data that the cardio vascular ailments have a tremendous arrangement of enthusiasm for clinical research attributable to its effect on individual wellbeing. This will produce an enhanced performance level with an higher accuracy level through the prediction model for heart disease using Hybrid algorithm. This module introduces with various features of combinations and many specified classification techniques. Therefore, this makes use of hybrid random forest with a linear model (HRFLM).

Neurons are often probed by presenting a set of stimuli that vary along one dimension (e.g. color) and quantifying how this stimulus property affect neural activity. An open question, in particular where higher-level areas are involved, is how much tuning measured with one stimulus set reveals about tuning to a new set. Here we ask this question by estimating tuning to hue in macaque V4 from a set of natural scenes and a set of simple color stimuli. We found that hue tuning was strong in each dataset but was not correlated across the datasets, a finding expected if neurons have strong mixed selectivity. We also show how such mixed selectivity may be useful for transmitting information about multiple dimensions of the world. Our finding suggest that tuning in higher visual areas measured with simple stimuli may thus not generalize to naturalistic stimuli. New & Noteworthy Visual cortex is often investigated by mapping neural tuning to variables selected by the researcher such as color. How much does this approach tell us a neuron’s general ‘role’ in vision? Here we show that for strongly hue-tuned neurons in V4, estimating hue tuning from artificial stimuli does not reveal the hue tuning in the context of natural scenes. We show how models of optimal information processing suggest that such mixed selectivity maximizes information transmission.

In primates, the Frontal Eye Field (FEF) controls saccades used to search the environment and foveate search targets. The receptive fields (RFs) of FEF neurons are not always statically fixed to a retinotopic location. Rather, they predictively remap to conform to the future fixation position just before a saccade begins. Two modes of perisaccadic remapping have been observed. In forward remapping, the RF jump follows a vector parallel to the upcoming saccade. In convergent remapping, the RF is pulled toward the endpoint of the upcoming saccade. Here, we ask what adaptive purpose RF remapping may serve for visual search behavior, characterized by exploratory and exploitative saccades. Exploratory saccades are high latency, low velocity saccades performed in a sequence to gather visual information, while exploitative saccades are low latency, high velocity, individual saccades performed to foveate the search target. We hypothesize that forward remapping is adaptive for exploratory saccades because it allows for a retinotopically accurate preview that will facilitate potential discovery of targets and planning of upcoming movements. Likewise, we hypothesize that convergent remapping occurs during exploitative saccades, and that this momentarily enhances visual representation around the target. To test this, we recorded from FEF neurons while monkeys searched for a target (gabor patch) embedded in a Perlin noise background. The monkeys were trained to ignore visual probes that were rapidly flashed at random locations during the search behavior. The visual responses elicited by the probes allowed us to infer RF location continuously throughout natural search behavior. We observed forward remapping around exploratory saccades and convergent remapping around exploitative saccades. This effect was strongest in cells that showed delay period activity during a memory guided saccade task. These results suggest that the dynamic reshaping of visual space in FEF facilitates different saccadic strategies. Meeting abstract presented at VSS 2018

Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.

Our bodies and the environment constrain our movements. For example, when our arm is fully outstretched, we cannot extend it further. More generally, the distribution of possible movements is conditioned on the state of our bodies in the environment, which is constantly changing. However, little is known about how the brain represents such distributions, and uses them in movement planning. Here, we record from dorsal premotor cortex (PMd) and primary motor cortex (M1) while monkeys reach to randomly placed targets. The hand’s position within the workspace creates probability distributions of possible upcoming targets, which affect movement trajectories and latencies. PMd, but not M1, neurons have increased activity when the monkey’s hand position makes it likely the upcoming movement will be in the neurons’ preferred directions. Across the population, PMd activity represents probability distributions of individual upcoming reaches, which depend on rapidly changing information about the body’s state in the environment.

Neuroscience has long focused on finding encoding models that effectively ask “what predicts neural spiking?” and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.

When a saccade is expected to result in a reward, both neural activity in oculomotor areas and the saccade itself (e.g., its vigor and latency) are altered (compared with when no reward is expected). As such, it is unclear whether the correlations of neural activity with reward indicate a representation of reward beyond a movement representation; the modulated neural activity may simply represent the differences in motor output due to expected reward. Here, to distinguish between these possibilities, we trained monkeys to perform a natural scene search task while we recorded from the frontal eye field (FEF). Indeed, when reward was expected (i.e., saccades to the target), FEF neurons showed enhanced responses. Moreover, when monkeys accidentally made eye movements to the target, firing rates were lower than when they purposively moved to the target. Thus, neurons were modulated by expected reward rather than simply the presence of the target. We then fit a model that simultaneously included components related to expected reward and saccade parameters. While expected reward led to shorter latency and higher velocity saccades, these behavioral changes could not fully explain the increased FEF firing rates. Thus, FEF neurons appear to encode motivational factors such as reward expectation, above and beyond the kinematic and behavioral consequences of imminent reward.

When we search for visual objects, the features of those objects bias our attention across the visual landscape (feature-based attention). The brain uses these top-down cues to select eye movement targets (spatial selection). The frontal eye field (FEF) is a prefrontal brain region implicated in selecting eye movements and is thought to reflect feature-based attention and spatial selection. Here, we study how FEF facilitates attention and selection in complex natural scenes. We ask whether FEF neurons facilitate feature-based attention by representing search-relevant visual features or whether they are primarily involved in selecting eye movement targets in space. We show that search-relevant visual features are weakly predictive of gaze in natural scenes and additionally have no significant influence on FEF activity. Instead, FEF activity appears to primarily correlate with the direction of the upcoming eye movement. Our result demonstrates a concrete need for better models of natural scene search and suggests that FEF activity during natural scene search is explained primarily by spatial selection.

Perceiving the visual world around us requires the brain to represent the features of stimuli and to categorize the stimulus based on these features. Incorrect categorization can result either from errors in visual representation or from errors in processes that lead to categorical choice. To understand the temporal relationship between the neural signatures of such systematic errors, we recorded whole-scalp magnetoencephalography (MEG) data from human subjects performing a rapid-scene categorization task. We built scene category decoders based on (1) spatiotemporally resolved neural activity, (2) spatial envelope (SpEn) image features, and (3) behavioral responses. Using confusion matrices, we tracked how well the pattern of errors from neural decoders could be explained by SpEn decoders and behavioral errors, over time and across cortical areas. Across the visual cortex and the medial temporal lobe, we found that both SpEn and behavioral errors explained unique variance in the errors of neural decoders. Critically, these effects were nearly simultaneous, and most prominent between 100 and 250ms after stimulus onset. Thus, during rapid-scene categorization, neural processes that ultimately result in behavioral categorization are simultaneous and co-localized with neural processes underlying visual information representation.

Every movement we make represents one of many possible actions. In reaching tasks with multiple targets, dorsal premotor cortex (PMd) appears to represent all possible actions simultaneously. However, in many situations we are not presented with explicit choices. Instead, we must estimate the best action based on noisy information and execute it while still uncertain of our choice. Here we asked how both primary motor cortex (M1) and PMd represented reach direction during a task in which a monkey made reaches based on noisy, uncertain target information. We found that with increased uncertainty, neurons in PMd actually enhanced their representation of unlikely movements throughout both planning and execution. The magnitude of this effect was highly variable across sessions, and was correlated with a measure of the monkeys' behavioral uncertainty. These effects were not present in M1. Our findings suggest that PMd represents and maintains a full distribution of potentially correct actions.

Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and above limb kinematics and dynamics. Here, we report a categorical reward signal in dorsal premotor (PMd) and primary motor (M1) neurons that corresponds to an increase in firing rates when a trial was not rewarded regardless of whether or not a reward was expected. We show that this signal is unrelated to error magnitude, reward prediction error, or other task confounds such as reward consumption, return reach plan, or kinematic differences across rewarded and unrewarded trials. The availability of reward information in motor cortex is crucial for theories of reward-based learning and motivational influences on actions.

How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and associated costs while limiting the achievable efficiency. This trade-off suggests a cost-effective learning strategy: to learn new movements we should start with many short chunks (to limit the cost of computation). As practice reduces the impediments to more complex computation, the chunking structure should evolve to allow progressively more efficient movements (to maximize efficiency). Here we show that monkeys learning a reaching sequence over an extended period of time adopt this strategy by performing movements that can be described as locally optimal trajectories. Chunking can thus be understood as a cost-effective strategy for producing and learning efficient movements.

Like humans, monkeys make saccades nearly three times a second. To understand the factors guiding this frequent decision, computational models of vision attempt to predict fixation locations using bottom-up visual features and top-down goals. How do the relative influences of these factors evolve over multiple time scales? Here we analyzed visual features at fixations using a retinal transform that provides realistic visual acuity by suitably degrading visual information in the periphery. In a task in which monkeys searched for a Gabor target in natural scenes, we characterized the relative importance of bottom-up and task-relevant influences by decoding fixated from nonfixated image patches based on visual features. At fast time scales, we found that search strategies can vary over the course of a single trial, with locations of higher saliency, target-similarity, edge–energy, and orientedness looked at later on in the trial. At slow time scales, we found that search strategies can be refined over several weeks of practice, and the influence of target orientation was significant only in the latter of two search tasks. Critically, these results were not observed without applying the retinal transform. Our results suggest that saccade-guidance strategies become apparent only when models take into account degraded visual representation in the periphery.