Search

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 reproduciblein vitromodels 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.

Demultiplexing 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.

The promise of using machine learning (ML) to extract scientific insights from high dimensional datasets is tempered by the frequent presence of confounding variables, and it behooves scientists to determine whether or not a model has extracted the desired information or instead may have fallen prey to bias. Due both to features of many natural phenomena and to practical constraints of experimental design, complex bioscience datasets tend to be organized in nested hierarchies which can obfuscate the origin of a confounding effect and obviate traditional methods of confounder amelioration. We propose a simple non-parametric statistical method called the Rank-to-Group (RTG) score that can identify hierarchical confounder effects in raw data and ML-derived data embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders in cases where linear methods such as regression fail. In a large public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover cross-modal correlated variability in a complex multi-phenotypic biological dataset. This approach should be of general use in experiment–analysis cycles and to ensure confounder robustness in ML models.

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.

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 & NoteworthyVisual 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.

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.

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.

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., Movements are continually constrained by the current body position and its relation to the surroundings. Here the authors report that the population activity of monkey dorsal premotor cortex neurons dynamically represents the probability distribution of possible reach directions.

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.

Current knowledge about the precise timing of visual input to the cortex relies largely on spike timings in monkeys and evoked-response latencies in humans. However, quantifying the activation onset does not unambiguously describe the timing of stimulus-feature-specific information processing. Here, we investigated the information content of the early human visual cortical activity by decoding low-level visual features from single-trial magnetoencephalographic (MEG) responses. MEG was measured from nine healthy subjects as they viewed annular sinusoidal gratings (spanning the visual field from 2 to 10° for a duration of 1 s), characterized by spatial frequency (0.33 cycles/degree or 1.33 cycles/degree) and orientation (45° or 135°); gratings were either static or rotated clockwise or anticlockwise from 0 to 180°. Time-resolved classifiers using a 20 ms moving window exceeded chance level at 51 ms (the later edge of the window) for spatial frequency, 65 ms for orientation, and 98 ms for rotation direction. Decoding accuracies of spatial frequency and orientation peaked at 70 and 90 ms, respectively, coinciding with the peaks of the onset evoked responses. Within-subject time-insensitive pattern classifiers decoded spatial frequency and orientation simultaneously (mean accuracy 64%, chance 25%) and rotation direction (mean 82%, chance 50%). Classifiers trained on data from other subjects decoded the spatial frequency (73%), but not the orientation, nor the rotation direction. Our results indicate that unaveraged brain responses contain decodable information about low-level visual features already at the time of the earliest cortical evoked responses, and that representations of spatial frequency are highly robust across individuals.

In the absence of external stimuli, human hemodynamic brain activity displays slow intrinsic variations. To find out whether such fluctuations would be altered by persistent pain, we asked 10 patients with unrelenting chronic pain of different etiologies and 10 sex- and age-matched control subjects to rest with eyes open during 3-T functional MRI. Independent component analysis was used to identify functionally coupled brain networks. Time courses of an independent component comprising the insular cortices of both hemispheres showed stronger spectral power at 0.12 to 0.25 Hz in patients than in control subjects, with the largest difference at 0.16 Hz. A similar but weaker effect was seen in the anterior cingulate cortex, whereas activity of the precuneus and early visual cortex, used as a control site, did not differ between the groups. In the patient group, seed point-based correlation analysis revealed altered spatial connectivity between insulae and anterior cingulate cortex. The results imply both temporally and spatially aberrant activity of the affective pain-processing areas in patients suffering from chronic pain. The accentuated 0.12- to 0.25-Hz fluctuations in the patient group might be related to altered activity of the autonomic nervous system.

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.

Independent component analysis (ICA) is increasingly used to analyze patterns of spontaneous activity in brain imaging. However, there are hardly any methods for answering the fundamental question: are the obtained components statistically significant? Most methods considering the significance of components either consider group-differences or use arbitrary thresholds with weak statistical justification. In previous work, we proposed a statistically principled method for testing if the coefficients in the mixing matrix are similar in different subjects or sessions. In many applications of ICA, however, we would like to test the reliability of the independent components themselves and not the mixing coefficients. Here, we develop a test for such an inter-subject consistency by extending our previous theory. The test is applicable, for example, to the spatial activity patterns obtained by spatial ICA in resting-state fMRI. We further improve both this and the previously proposed testing method by introducing a new way of correcting for multiple testing, new variants of the clustering method, and a computational approximation which greatly reduces the memory and computation required.

We developed a data-driven method to spatiotemporally and spectrally characterize the dynamics of brain oscillations in resting-state magnetoencephalography (MEG) data. The method, called envelope spatial Fourier independent component analysis (eSFICA), maximizes the spatial and spectral sparseness of Fourier energies of a cortically constrained source current estimate. We compared this method using a simulated data set against 5 other variants of independent component analysis and found that eSFICA performed on par with its temporal variant, eTFICA, and better than other ICA variants, in characterizing dynamics at time scales of the order of minutes. We then applied eSFICA to real MEG data obtained from 9 subjects during rest. The method identified several networks showing within- and cross-frequency inter-areal functional connectivity profiles which resemble previously reported resting-state networks, such as the bilateral sensorimotor network at ~20Hz, the lateral and medial parieto-occipital sources at ~10Hz, a subset of the default-mode network at ~8 and ~15Hz, and lateralized temporal lobe sources at ~8Hz. Finally, we interpreted the estimated networks as spatiospectral filters and applied the filters to obtain the dynamics during a natural stimulus sequence presented to the same 9 subjects. We observed occipital alpha modulation to visual stimuli, bilateral rolandic mu modulation to tactile stimuli and video clips of hands, and the temporal lobe network modulation to speech stimuli, but no modulation of the sources in the default-mode network. We conclude that (1) the proposed method robustly detects inter-areal cross-frequency networks at long time scales, (2) the functional relevance of the resting-state networks can be probed by applying the obtained spatiospectral filters to data from measurements with controlled external stimulation.

Independent component analysis (ICA) of electroencephalographic (EEG) and magnetoencephalographic (MEG) data is usually performed over the temporal dimension: each channel is one row of the data matrix, and a linear transformation maximizing the independence of component time courses is sought. In functional magnetic resonance imaging (fMRI), by contrast, most studies use spatial ICA: each time point constitutes a row of the data matrix, and independence of the spatial patterns is maximized. Here, we show the utility of spatial ICA in characterizing oscillatory neuromagnetic signals. We project the sensor data into cortical space using a standard minimum‐norm estimate and apply a sparsifying transform to focus on oscillatory signals. The resulting method, spatial Fourier‐ICA, provides a concise summary of the spatiotemporal and spectral content of spontaneous neuromagnetic oscillations in cortical source space over time scales of minutes. Spatial Fourier‐ICA applied to resting‐state and naturalistic stimulation MEG data from nine healthy subjects revealed consistent components covering the early visual, somatosensory and motor cortices with spectral peaks at ∼10 and ∼20 Hz. The proposed method seems valuable for inferring functional connectivity, stimulus‐related modulation of rhythmic activity, and their commonalities across subjects from nonaveraged MEG data. Hum Brain Mapp, 2011. © 2011 Wiley‐Liss, Inc.

Unsupervised spatiotemporal and spectral characterization of spontaneous neuromagnetic brain rhythms over long time scales (minutes) would be useful for basic and clinical neuroscience. We recently showed that after applying a sparsifying transform (the short-time Fourier transform or STFT) to MEG data, independent component analysis (ICA) identified sources of oscillatory activity [1]. STFT on MEG data results in a 3-way data structure with temporal (time points), spatial (channels or source signals) and spectral (frequency bins) dimensions. Here, we propose to treat the 3-way data by using ICA to impose sparseness in the space–frequency dimension, resulting in a “spatial Fourier-ICA” (sFICA). Results of sFICA applied to STFTs of source-level MEG data from subjects who received natural stimulation or were resting suggest that sFICA is an efficient technique to identify both stimulus-related and intrinsic dynamics of neuromagnetic brain rhythms.

Rhythmic brain activity, measured by magnetoencephalography (MEG), is modulated during stimulation and task performance. Here, we introduce an oscillatory response function (ORF) to predict the dynamic suppression–rebound modulation of brain rhythms during a stimulus sequence. We derived a class of parametric models for the ORF in a generalized convolution framework. The model parameters were estimated from MEG data acquired from 10 subjects during bilateral tactile stimulation of fingers (stimulus rates of 4 Hz and 10 Hz in blocks of 0.5, 1, 2, and 4 s). The envelopes of the 17–23 Hz rhythmic activity, computed for sensors above the rolandic region, correlated 25%–43% better with the envelopes predicted by the models than by the stimulus time course (boxcar). A linear model with separate convolution kernels for onset and offset responses gave the best prediction. We studied the generalizability of this model with data from 5 different subjects during a separate bilateral tactile sequence by first identifying neural sources of the 17–23 Hz activity using cortically constrained minimum norm estimates. Both the model and the boxcar predicted strongest modulation in the primary motor cortex. For short‐duration stimulus blocks, the model predicted the envelope of the cortical currents 20% better than the boxcar did. These results suggest that ORFs could concisely describe brain rhythms during different stimuli, tasks, and pathologies. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.

Summary • Problem: Basic independent component analysis (ICA) of spontaneous MEG data finds many artifacts and not so many brain rhythms • We want to develop a method which concentrates on sources of rhythmic activity • We show this is accomplished by applying ICA on short-time Fourier Transforms • Further, we extend the method to find regions in which oscillations are phase-locked but not necessarily at the same phase.

In this paper, we explore visually evoked potentials (VEPs) as a potential tool for biometric identification. Using a clinical stimulation paradigm, single channel pattern onset VEPs are recorded from raw EEG from 10 healthy male subjects aged between 20 and 24. Following this, two feature extraction techniques are employed to characterize the signals. Specifically, a novel, physiologically relevant peak matching algorithm is proposed and its performance is compared to features obtained from multi-resolution wavelet analysis. Once suitably characterized, the VEPs from different individuals are classified using a standard distance-measure based algorithm.

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.