PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.

Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; (2) individual word decoding in reconstructed speech achieves 92%-100% accuracy (chance level is 8%); (3) direct reconstruction from sensorimotor brain activity produces intelligible speech.Significance.These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.

Disentangling rodent behaviors to improve automated behavior recognition.

Automated observation and analysis of behavior is important to facilitate progress in many fields of science. Recent developments in deep learning have enabled progress in object detection and tracking, but rodent behavior recognition struggles to exceed 75-80% accuracy for ethologically relevant behaviors. We investigate the main reasons why and distinguish three aspects of behavior dynamics that are difficult to automate. We isolate these aspects in an artificial dataset and reproduce effects with the state-of-the-art behavior recognition models. Having an endless amount of labeled training data with minimal input noise and representative dynamics will enable research to optimize behavior recognition architectures and get closer to human-like recognition performance for behaviors with challenging dynamics.

The neuroconnectionist research programme.

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.

Towards Naturalistic Speech Decoding from Intracranial Brain Data.

Speech decoding from brain activity can enable development of brain-computer interfaces (BCIs) to restore naturalistic communication in paralyzed patients. Previous work has focused on development of decoding models from isolated speech data with a clean background and multiple repetitions of the material. In this study, we describe a novel approach to speech decoding that relies on a generative adversarial neural network (GAN) to reconstruct speech from brain data recorded during a naturalistic speech listening task (watching a movie). We compared the GAN-based approach, where reconstruction was done from the compressed latent representation of sound decoded from the brain, with several baseline models that reconstructed sound spectrogram directly. We show that the novel approach provides more accurate reconstructions compared to the baselines. These results underscore the potential of GAN models for speech decoding in naturalistic noisy environments and further advancing of BCIs for naturalistic communication. Clinical Relevance - This study presents a novel speech decoding paradigm that combines advances in deep learning, speech synthesis and neural engineering, and has the potential to advance the field of BCI for severely paralyzed individuals.

Bayesian model averaging for nonparametric discontinuity design.

Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions. In this paper, we provide a framework for discontinuity-based designs using Bayesian model averaging and Gaussian process regression, which we refer to as 'Bayesian nonparametric discontinuity design', or BNDD for short. BNDD addresses the two major shortcomings in most implementations of such designs: overconfidence due to implicit conditioning on the alleged effect, and model misspecification due to reliance on overly simplistic regression models. With the appropriate Gaussian process covariance function, our approach can detect discontinuities of any order, and in spectral features. We demonstrate the usage of BNDD in simulations, and apply the framework to determine the effect of running for political positions on longevity, of the effect of an alleged historical phantom border in the Netherlands on Dutch voting behaviour, and of Kundalini Yoga meditation on heart rate.

Phenotype based prediction of exome sequencing outcome using machine learning for neurodevelopmental disorders.

PURPOSE: Although the introduction of exome sequencing (ES) has led to the diagnosis of a significant portion of patients with neurodevelopmental disorders (NDDs), the diagnostic yield in actual clinical practice has remained stable at approximately 30%. We hypothesized that improving the selection of patients to test on the basis of their phenotypic presentation will increase diagnostic yield and therefore reduce unnecessary genetic testing. METHODS: We tested 4 machine learning methods and developed PredWES from these: a statistical model predicting the probability of a positive ES result solely on the basis of the phenotype of the patient. RESULTS: We first trained the tool on 1663 patients with NDDs and subsequently showed that diagnostic ES on the top 10% of patients with the highest probability of a positive ES result would provide a diagnostic yield of 56%, leading to a notable 114% increase. Inspection of our model revealed that for patients with NDDs, comorbid abnormal (lower) muscle tone and microcephaly positively correlated with a conclusive ES diagnosis, whereas autism was negatively associated with a molecular diagnosis. CONCLUSION: In conclusion, PredWES allows prioritizing patients with NDDs eligible for diagnostic ES on the basis of their phenotypic presentation to increase the diagnostic yield, making a more efficient use of health care resources.

No Evidence for Neural Overlap between Unconsciously Processed and Imagined Stimuli.

Visual representations can be generated via feedforward or feedback processes. The extent to which these processes result in overlapping representations remains unclear. Previous work has shown that imagined stimuli elicit similar representations as perceived stimuli throughout the visual cortex. However, while representations during imagery are indeed only caused by feedback processing, neural processing during perception is an interplay of both feedforward and feedback processing. This means that any representational overlap could be because of overlap in feedback processes. In the current study, we aimed to investigate this issue by characterizing the overlap between feedforward- and feedback-initiated category representations during imagined stimuli, conscious perception, and unconscious processing using fMRI in humans of either sex. While all three conditions elicited stimulus representations in left lateral occipital cortex (LOC), significant similarities were observed only between imagery and conscious perception in this area. Furthermore, connectivity analyses revealed stronger connectivity between frontal areas and left LOC during conscious perception and in imagery compared with unconscious processing. Together, these findings can be explained by the idea that long-range feedback modifies visual representations, thereby reducing representational overlap between purely feedforward- and feedback-initiated stimulus representations measured by fMRI. Neural representations influenced by feedback, either stimulus driven (perception) or purely internally driven (imagery), are, however, relatively similar.

Population codes of prior knowledge learned through environmental regularities.

How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.

Adaptive time scales in recurrent neural networks.

Recent experiments have revealed a hierarchy of time scales in the visual cortex, where different stages of the visual system process information at different time scales. Recurrent neural networks are ideal models to gain insight in how information is processed by such a hierarchy of time scales and have become widely used to model temporal dynamics both in machine learning and computational neuroscience. However, in the derivation of such models as discrete time approximations of the firing rate of a population of neurons, the time constants of the neuronal process are generally ignored. Learning these time constants could inform us about the time scales underlying temporal processes in the brain and enhance the expressive capacity of the network. To investigate the potential of adaptive time constants, we compare the standard approximations to a more lenient one that accounts for the time scales at which processes unfold. We show that such a model performs better on predicting simulated neural data and allows recovery of the time scales at which the underlying processes unfold. A hierarchy of time scales emerges when adapting to data with multiple underlying time scales, underscoring the importance of such a hierarchy in processing complex temporal information.

Cortical network responses map onto data-driven features that capture visual semantics of movie fragments.

Research on how the human brain extracts meaning from sensory input relies in principle on methodological reductionism. In the present study, we adopt a more holistic approach by modeling the cortical responses to semantic information that was extracted from the visual stream of a feature film, employing artificial neural network models. Advances in both computer vision and natural language processing were utilized to extract the semantic representations from the film by combining perceptual and linguistic information. We tested whether these representations were useful in studying the human brain data. To this end, we collected electrocorticography responses to a short movie from 37 subjects and fitted their cortical patterns across multiple regions using the semantic components extracted from film frames. We found that individual semantic components reflected fundamental semantic distinctions in the visual input, such as presence or absence of people, human movement, landscape scenes, human faces, etc. Moreover, each semantic component mapped onto a distinct functional cortical network involving high-level cognitive regions in occipitotemporal, frontal and parietal cortices. The present work demonstrates the potential of the data-driven methods from information processing fields to explain patterns of cortical responses, and contributes to the overall discussion about the encoding of high-level perceptual information in the human brain.

Brain-optimized extraction of complex sound features that drive continuous auditory perception.

Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match the neural representation of sound. Here, we postulate that constructing a data-driven neural model of auditory perception, with a minimum of theoretical assumptions about the relevant sound features, could provide an alternative approach and possibly a better match to the neural responses. We collected electrocorticography recordings from six patients who watched a long-duration feature film. The raw movie soundtrack was used to train an artificial neural network model to predict the associated neural responses. The model achieved high prediction accuracy and generalized well to a second dataset, where new participants watched a different film. The extracted bottom-up features captured acoustic properties that were specific to the type of sound and were associated with various response latency profiles and distinct cortical distributions. Specifically, several features encoded speech-related acoustic properties with some features exhibiting shorter latency profiles (associated with responses in posterior perisylvian cortex) and others exhibiting longer latency profiles (associated with responses in anterior perisylvian cortex). Our results support and extend the current view on speech perception by demonstrating the presence of temporal hierarchies in the perisylvian cortex and involvement of cortical sites outside of this region during audiovisual speech perception.

Validation of structural brain connectivity networks: The impact of scanning parameters.

Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1.03 mm3) are in better agreement with the tracer network, than those derived from high image resolution (0.53 mm3) or at an even lower image resolution (2.03 mm3). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60-64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70-75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.

Distinct directional couplings between slow and fast gamma power to the phase of theta oscillations in the rat hippocampus.

It is well-established that theta (~4-10 Hz) and gamma (~25-100 Hz) oscillations interact in the rat hippocampus. This cross-frequency coupling might facilitate neuronal coordination both within and between brain areas. However, it remains unclear whether the phase of theta oscillations controls the power of slow and fast gamma activity or vice versa. We here applied spectral Granger causality, phase slope index and a newly developed cross-frequency directionality (CFD) measure to investigate directional interactions between local field potentials recorded within and across hippocampal subregions of CA1 and CA3 of freely exploring rats. Given the well-known CA3 to CA1 anatomical connection, we hypothesized that interregional directional interactions were constrained by anatomical connection, and within-frequency and cross-frequency directional interactions were always from CA3 to CA1. As expected, we found that CA3 drove CA1 in the theta band, and theta phase-to-gamma power coupling was prominent both within and between CA3 and CA1 regions. The CFD measure further demonstrated that distinct directional couplings with respect to theta phase was different between slow and fast gamma activity. Importantly, CA3 slow gamma power phase-adjusted CA1 theta oscillations, suggesting that slow gamma activity in CA3 entrains theta oscillations in CA1. In contrast, CA3 theta phase controls CA1 fast gamma activity, indicating that communication at CA1 fast gamma is coordinated by CA3 theta phase. Overall, these findings demonstrate dynamic directional interactions between theta and slow/fast gamma oscillations in the hippocampal network, suggesting that anatomical connections constrain the directional interactions.

Deep learning improves automated rodent behavior recognition within a specific experimental setup.

Automated observation and analysis of rodent behavior is important to facilitate research progress in neuroscience and pharmacology. Available automated systems lack adaptivity and can benefit from advances in AI. In this work we compare a state-of-the-art conventional rat behavior recognition (RBR) system to an advanced deep learning method and evaluate its performance within and across experimental setups. We show that using a multi-fiber network (MF-Net) in conjunction with data augmentation strategies within-setup dataset performance improves over the conventional RBR system. Two new methods for video augmentation were used: video cutout and dynamic illumination change. However, we also show that improvements do not transfer to videos in different experimental setups, for which we discuss possible causes and cures.

Evidence for confounding eye movements under attempted fixation and active viewing in cognitive neuroscience.

Eye movements can have serious confounding effects in cognitive neuroscience experiments. Therefore, participants are commonly asked to fixate. Regardless, participants will make so-called fixational eye movements under attempted fixation, which are thought to be necessary to prevent perceptual fading. Neural changes related to these eye movements could potentially explain previously reported neural decoding and neuroimaging results under attempted fixation. In previous work, under attempted fixation and passive viewing, we found no evidence for systematic eye movements. Here, however, we show that participants' eye movements are systematic under attempted fixation when active viewing is demanded by the task. Since eye movements directly affect early visual cortex activity, commonly used for neural decoding, our findings imply alternative explanations for previously reported results in neural decoding.

Neuroimaging Findings on Amodal Completion: A Review.

Amodal completion is the phenomenon of perceiving completed objects even though physically they are partially occluded. In this review, we provide an extensive overview of the results obtained from a variety of neuroimaging studies on the neural correlates of amodal completion. We discuss whether low-level and high-level cortical areas are implicated in amodal completion; provide an overview of how amodal completion unfolds over time while dissociating feedforward, recurrent, and feedback processes; and discuss how amodal completion is represented at the neuronal level. The involvement of low-level visual areas such as V1 and V2 is not yet clear, while several high-level structures such as the lateral occipital complex and fusiform face area seem invariant to occlusion of objects and faces, respectively, and several motor areas seem to code for object permanence. The variety of results on the timing of amodal completion hints to a mixture of feedforward, recurrent, and feedback processes. We discuss whether the invisible parts of the occluded object are represented as if they were visible, contrary to a high-level representation. While plenty of questions on amodal completion remain, this review presents an overview of the neuroimaging findings reported to date, summarizes several insights from computational models, and connects research of other perceptual completion processes such as modal completion. In all, it is suggested that amodal completion is the solution to deal with various types of incomplete retinal information, and highly depends on stimulus complexity and saliency, and therefore also give rise to a variety of observed neural patterns.

Eye movements explain decodability during perception and cued attention in MEG.

Eye movements are an integral part of human perception, but can induce artifacts in many magneto-encephalography (MEG) and electroencephalography (EEG) studies. For this reason, investigators try to minimize eye movements and remove these artifacts from their data using different techniques. When these artifacts are not purely random, but consistent regarding certain stimuli or conditions, the possibility arises that eye movements are actually inducing effects in the MEG signal. It remains unclear how much of an influence eye movements can have on observed effects in MEG, since most MEG studies lack a control analysis to verify whether an effect found in the MEG signal is induced by eye movements. Here, we find that we can decode stimulus location from eye movements in two different stages of a working memory match-to-sample task that encompass different areas of research typically done with MEG. This means that the observed MEG effect might be (partly) due to eye movements instead of any true neural correlate. We suggest how to check for eye movement effects in the data and make suggestions on how to minimize eye movement artifacts from occurring in the first place.

Shared Neural Mechanisms of Visual Perception and Imagery.

For decades, the extent to which visual imagery relies on the same neural mechanisms as visual perception has been a topic of debate. Here, we review recent neuroimaging studies comparing these two forms of visual experience. Their results suggest that there is a large overlap in neural processing during perception and imagery: neural representations of imagined and perceived stimuli are similar in the visual, parietal, and frontal cortex. Furthermore, perception and imagery seem to rely on similar top-down connectivity. The most prominent difference is the absence of bottom-up processing during imagery. These findings fit well with the idea that imagery and perception rely on similar emulation or prediction processes.

Neural Tuning to Low-Level Features of Speech throughout the Perisylvian Cortex.

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain.SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the perisylvian cortex and potentially contribute to the emergence of "coarse" speech representations in inferior frontal gyrus typically associated with high-level language processing. These findings add to the previous work on auditory processing and underline a distinctive role of inferior frontal gyrus in natural speech comprehension.