Computational Language Modeling and the Promise of In Silico Experimentation.

Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm-in silico experimentation using deep learning-based encoding models-that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.

Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity.

The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness of deep neural networks (DNNs). The purported challenge in aggregating neural spiking data is the pervasiveness of context-dependent shifts in the neural data distributions. However, large scale unsupervised pretraining by nature spans heterogeneous data, and has proven to be a fundamental recipe for successful representation learning across deep learning. We thus develop Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer for neural spiking activity, and demonstrate that pretraining can leverage motor BCI datasets that span sessions, subjects, and experimental tasks. NDT2 enables rapid adaptation to novel contexts in downstream decoding tasks and opens the path to deployment of pretrained DNNs for iBCI control. Code: https://github.com/joel99/context_general_bci.

Stacked regressions and structured variance partitioning for interpretable brain maps.

Relating brain activity associated with a complex stimulus to different properties of that stimulus is a powerful approach for constructing functional brain maps. However, when stimuli are naturalistic, their properties are often correlated (e.g., visual and semantic features of natural images, or different layers of a convolutional neural network that are used as features of images). Correlated properties can act as confounders for each other and complicate the interpretability of brain maps, and can impact the robustness of statistical estimators. Here, we present an approach for brain mapping based on two proposed methods: stacking different encoding models and structured variance partitioning. Our stacking algorithm combines encoding models that each use as input a feature space that describes a different stimulus attribute. The algorithm learns to predict the activity of a voxel as a linear combination of the outputs of different encoding models. We show that the resulting combined model can predict held-out brain activity better or at least as well as the individual encoding models. Further, the weights of the linear combination are readily interpretable; they show the importance of each feature space for predicting a voxel. We then build on our stacking models to introduce structured variance partitioning, a new type of variance partitioning that takes into account the known relationships between features. Our approach constrains the size of the hypothesis space and allows us to ask targeted questions about the similarity between feature spaces and brain regions even in the presence of correlations between the feature spaces. We validate our approach in simulation, showcase its brain mapping potential on fMRI data, and release a Python package. Our methods can be useful for researchers interested in aligning brain activity with different layers of a neural network, or with other types of correlated feature spaces.

A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex.

Midlevel features, such as contour and texture, provide a computational link between low- and high-level visual representations. Although the nature of midlevel representations in the brain is not fully understood, past work has suggested a texture statistics model, called the P-S model (Portilla and Simoncelli, 2000), is a candidate for predicting neural responses in areas V1-V4 as well as human behavioral data. However, it is not currently known how well this model accounts for the responses of higher visual cortex to natural scene images. To examine this, we constructed single-voxel encoding models based on P-S statistics and fit the models to fMRI data from human subjects (both sexes) from the Natural Scenes Dataset (Allen et al., 2022). We demonstrate that the texture statistics encoding model can predict the held-out responses of individual voxels in early retinotopic areas and higher-level category-selective areas. The ability of the model to reliably predict signal in higher visual cortex suggests that the representation of texture statistics features is widespread throughout the brain. Furthermore, using variance partitioning analyses, we identify which features are most uniquely predictive of brain responses and show that the contributions of higher-order texture features increase from early areas to higher areas on the ventral and lateral surfaces. We also demonstrate that patterns of sensitivity to texture statistics can be used to recover broad organizational axes within visual cortex, including dimensions that capture semantic image content. These results provide a key step forward in characterizing how midlevel feature representations emerge hierarchically across the visual system.SIGNIFICANCE STATEMENT Intermediate visual features, like texture, play an important role in cortical computations and may contribute to tasks like object and scene recognition. Here, we used a texture model proposed in past work to construct encoding models that predict the responses of neural populations in human visual cortex (measured with fMRI) to natural scene stimuli. We show that responses of neural populations at multiple levels of the visual system can be predicted by this model, and that the model is able to reveal an increase in the complexity of feature representations from early retinotopic cortex to higher areas of ventral and lateral visual cortex. These results support the idea that texture-like representations may play a broad underlying role in visual processing.

Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features.

Representations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position) and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the region's preferred category. To address the generality of this "natural scene statistics" hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses. First, across a large set of rich natural scene images, we demonstrated reliable associations between low-level (Gabor) features and high-level semantic categories (faces, buildings, animate/inanimate objects, small/large objects, indoor/outdoor scenes), with these relationships varying spatially across the visual field. Second, we used a large-scale functional MRI dataset (the Natural Scenes Dataset) and a voxelwise forward encoding model to estimate the feature and spatial selectivity of neural populations throughout visual cortex. We found that voxels in category-selective visual regions exhibit systematic biases in their feature and spatial selectivity, which are consistent with their hypothesized roles in category processing. We further showed that these low-level tuning biases are not driven by selectivity for categories themselves. Together, our results are consistent with a framework in which low-level feature selectivity contributes to the computation of high-level semantic category information in the brain.

Semantic Representations during Language Comprehension Are Affected by Context.

The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. fMRI was used to record human brain activity while four subjects (two female) read words in four conditions that vary in context: narratives, isolated sentences, blocks of semantically similar words, and isolated words. We then compared the signal-to-noise ratio (SNR) of evoked brain responses, and we used a voxelwise encoding modeling approach to compare the representation of semantic information across the four conditions. We find four consistent effects of varying context. First, stimuli with more context evoke brain responses with higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared with stimuli with little context. Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. In individual subjects, only natural language stimuli consistently evoke widespread representation of semantic information. Third, context affects voxel semantic tuning. Finally, models estimated using stimuli with little context do not generalize well to natural language. These results show that context has large effects on the quality of neuroimaging data and on the representation of meaning in the brain. Thus, neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.SIGNIFICANCE STATEMENT Context is an important part of understanding the meaning of natural language, but most neuroimaging studies of meaning use isolated words and isolated sentences with little context. Here, we examined whether the results of neuroimaging studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuro-imaging data and changes where and how semantic information is represented in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life.

Selectivity for food in human ventral visual cortex.

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI "localizer" using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

Brainprints: identifying individuals from magnetoencephalograms.

Magnetoencephalography (MEG) is used to study a wide variety of cognitive processes. Increasingly, researchers are adopting principles of open science and releasing their MEG data. While essential for reproducibility, sharing MEG data has unforeseen privacy risks. Individual differences may make a participant identifiable from their anonymized recordings. However, our ability to identify individuals based on these individual differences has not yet been assessed. Here, we propose interpretable MEG features to characterize individual difference. We term these features brainprints (brain fingerprints). We show through several datasets that brainprints accurately identify individuals across days, tasks, and even between MEG and Electroencephalography (EEG). Furthermore, we identify consistent brainprint components that are important for identification. We study the dependence of identifiability on the amount of data available. We also relate identifiability to the level of preprocessing and the experimental task. Our findings reveal specific aspects of individual variability in MEG. They also raise concerns about unregulated sharing of brain data, even if anonymized.

Combining computational controls with natural text reveals aspects of meaning composition.

To study a core component of human intelligence-our ability to combine the meaning of words-neuroscientists have looked to linguistics. However, linguistic theories are insufficient to account for all brain responses reflecting linguistic composition. In contrast, we adopt a data-driven approach to study the composed meaning of words beyond their individual meaning, which we term 'supra-word meaning'. We construct a computational representation for supra-word meaning and study its brain basis through brain recordings from two complementary imaging modalities. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests that composed meaning might be maintained through a different neural mechanism than the synchronized firing of pyramidal cells. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.

Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling.

A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repetitions of data corresponding to the same stimulus. However, repetition of stimulus can be undesirable, because underlying neural activity has been shown to change across trials, and repeating stimuli limits the breadth of the stimulus space experienced by subjects. In particular, the rising popularity of naturalistic studies with a single viewing of a movie or story necessitates the discovery of new approaches to increase SNR. We introduce a simple framework to reduce noise in single-trial MEG data by leveraging correlations in neural responses across subjects as they experience the same stimulus. We demonstrate its use in a naturalistic reading comprehension task with 8 subjects, with MEG data collected while they read the same story a single time. We find that our procedure results in data with reduced noise and allows for better discovery of neural phenomena. As proof-of-concept, we show that the N400m's correlation with word surprisal, an established finding in literature, is far more clearly observed in the denoised data than the original data. The denoised data also shows higher decoding and encoding accuracy than the original data, indicating that the neural signals associated with reading are either preserved or enhanced after the denoising procedure.

A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG.

OBJECTIVE: Intraoperative neurophysiological monitoring (IONM) is the use of electrophysiological methods during certain high-risk surgeries to assess the functional integrity of nerves in real time and alert the surgeon to prevent damage. However, the efficiency of IONM in current practice is limited by latency of verbal communications, inter-rater variability, and the subjective manner in which electrophysiological signals are described. METHODS: In an attempt to address these shortcomings, we investigate automated classification of free-running electromyogram (EMG) waveforms during IONM. We propose a hybrid model with a convolutional neural network (CNN) component and a long short-term memory (LSTM) component to better capture complicated EMG patterns under conditions of both electrical noise and movement artifacts. Moreover, a preprocessing pipeline based on data normalization is used to handle classification of data from multiple subjects. To investigate model robustness, we also analyze models under different methods for processing of artifacts. RESULTS: Compared with several benchmark modeling methods, CNN-LSTM performs best in classification, achieving accuracy of 89.54% and sensitivity of 94.23% in cross-patient evaluation. CONCLUSION: The CNN-LSTM model shows promise for automated classification of continuous EMG in IONM. SIGNIFICANCE: This technique has potential to improve surgical safety by reducing cognitive load and inter-rater variability.

Incremental Language Comprehension Difficulty Predicts Activity in the Language Network but Not the Multiple Demand Network.

What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general "multiple demand" (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, functional magnetic resonance imaging data collected during naturalistic story listening are compared with theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant- or trial-level variability, the neuroimaging and behavioral datasets were collected in nonoverlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal "core language network," in both left-hemispheric areas and their right hemispheric homotopic areas. These results argue against strong involvement of domain-general executive circuits in language comprehension.

The lexical semantics of adjective-noun phrases in the human brain.

As a person reads, the brain performs complex operations to create higher order semantic representations from individual words. While these steps are effortless for competent readers, we are only beginning to understand how the brain performs these actions. Here, we explore lexical semantics using magnetoencephalography (MEG) recordings of people reading adjective-noun phrases presented one word at a time. We track the neural representation of single word representations over time, through different brain regions. Our results reveal two novel findings: (a) a neural representation of the adjective is present during noun presentation, but this representation is different from that observed during adjective presentation and (b) the neural representation of adjective semantics observed during adjective reading is reactivated after phrase reading, with remarkable consistency. We also note that while the semantic representation of the adjective during the reading of the adjective is very distributed, the later representations are concentrated largely to temporal and frontal areas previously associated with composition. Taken together, these results paint a picture of information flow in the brain as phrases are read and understood.

REGULARIZED BRAIN READING WITH SHRINKAGE AND SMOOTHING.

Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare a suite of approaches which regularize via shrinkage: ridge regression, the elastic net (a generalization of ridge regression and the lasso), and a hierarchical Bayesian model based on small area estimation (SAE). We contrast regularization with spatial smoothing and combinations of smoothing and shrinkage. All methods are tested on functional magnetic resonance imaging (fMRI) data from multiple subjects participating in two different experiments related to reading, for both predicting neural response to stimuli and decoding stimuli from responses. Interestingly, when the regularization parameters are chosen by cross-validation independently for every voxel, low/high regularization is chosen in voxels where the classification accuracy is high/low, indicating that the regularization intensity is a good tool for identification of relevant voxels for the cognitive task. Surprisingly, all the regularization methods work about equally well, suggesting that beating basic smoothing and shrinkage will take not only clever methods, but also careful modeling.

Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses.

Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.

Tracking neural coding of perceptual and semantic features of concrete nouns.

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes.