Leila Wehbe

All Publications

• Flexibility and Invariance of Object Representations in the Human Brain. ArXiv Dirani, J., Chawla, S., Wehbe, L., Mahon, B. Z. 2026

  Abstract

  The human brain represents objects in a way that is both invariant across instances and flexible enough to support different contexts and tasks. Yet how the brain reconciles these demands, holding an object's representation stable while flexibly reshaping it to meet the current context, remains unknown. Using fMRI during naturalistic movie viewing we investigated how the same objects are represented when they are passive scene elements versus targets of goal-directed actions. Action targets engaged a parietal action network centered in the supramarginal and postcentral gyri, while passive objects recruited a distributed occipito-temporal network involved in visual object recognition. Within context-selective networks, representational geometry showed a double dissociation: target objects were organized by action affordance and hand posture affordance dimensions, while passive objects aligned with semantic dimensions. The visual structure of object representations, by contrast, was invariant across contexts. Searchlight analyses further showed that this dissociation reflected the relative engagement of networks of regions rather than the strict presence or absence of each representational format. Flexibility and invariance are not opposing principles of neural representation, but operate simultaneously at different levels of a common, distributed representational system.

  View details for DOI 10.1145/3626772.3657878

  View details for PubMedID 42238060

  View details for PubMedCentralID PMC13229080

• Higher visual areas act like domain-general filters with strong selectivity and functional specialization NATURE COMMUNICATIONS Khosla, M., Wehbe, L. 2026; 17 (1)

  Abstract

  Neuroscientific studies rely heavily on a-priori hypotheses, which can bias results toward existing theories. Here, we use a hypothesis-neutral approach to study category selectivity in higher visual cortex. Using only stimulus images and their associated fMRI activity, we constrain randomly initialized neural networks to predict voxel activity. Despite no category-level supervision, units in the trained networks act as detectors for semantic concepts like 'faces' or 'words', providing solid empirical support for categorical selectivity. Importantly, this selectivity is mostly maintained when training the networks without images that contain the preferred category, strongly suggesting that selectivity is not domain-specific machinery, but sensitivity to generic patterns that characterize preferred categories. The ability of the models' representations to transfer to perceptual tasks further reveals the functional role of their selective responses. Finally, our models show selectivity only for a limited number of categories, all previously identified, suggesting that the essential categories are already known.

  View details for DOI 10.1038/s41467-026-73938-9

  View details for Web of Science ID 001855815900006

  View details for PubMedID 42285966

  View details for PubMedCentralID PMC13408694

• Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes. bioRxiv : the preprint server for biology Lewis, L., Pitkow, X., Wehbe, L. 2025

  Abstract

  How does the brain process language over time? Research suggests that natural human language is processed hierarchically across brain regions over time. However, attempts to characterize this computation have thus far been limited to tightly controlled experimental settings that capture only a coarse picture of the brain dynamics underlying human natural language comprehension. The recent emergence of LLM encoding models promises a new avenue to discover and characterize rich semantic information in the brain, yet interpretable methods for linking information in LLMs to language processing over time are limited. In this work, we develop a low-rank tensor regression method to decompose LLM encoding models into interpretable components of semantics, time, and brain region activation, and apply the method to a Magnetoencephalography (MEG) dataset in which subjects listened to narrative stories. With only a few components, we show improved performance compared to a standard ridge regression encoding model, suggesting the low-rank models provide a good inductive bias for language encoding. In addition, our method discovers a diverse spectrum of interpretable response components that are sensitive to a rich set of low-level and semantic language features, showing that our method is able to separate distinct language processing features in neural signals. After controlling for low-level audio and sentence features, we demonstrate better capture of semantic features. Through use of low-rank tensor encoding models we are able to decompose neural responses to language features, showing improved encoding performance and interpretable processing components, suggesting our method as a useful tool for uncovering language processes in naturalistic settings.

  View details for DOI 10.1101/2025.06.02.657514

  View details for PubMedID 40501791

  View details for PubMedCentralID PMC12157526

• Origins of food selectivity in human visual cortex TRENDS IN NEUROSCIENCES Henderson, M. M., Tarr, M. J., Wehbe, L. 2025; 48 (2): 113-123

  Abstract

  Several recent studies, enabled by advances in neuroimaging methods and large-scale datasets, have identified areas in human ventral visual cortex that respond more strongly to food images than to images of many other categories, adding to our knowledge about the broad network of regions that are responsive to food. This finding raises important questions about the evolutionary and developmental origins of a possible food-selective neural population, as well as larger questions about the origins of category-selective neural populations more generally. Here, we propose a framework for how visual properties of food (particularly color) and nonvisual signals associated with multimodal reward processing, social cognition, and physical interactions with food may, in combination, contribute to the emergence of food selectivity. We discuss recent research that sheds light on each of these factors, alongside a broader account of category selectivity that incorporates both visual feature statistics and behavioral relevance.

  View details for DOI 10.1016/j.tins.2024.12.001

  View details for Web of Science ID 001451436400001

  View details for PubMedID 39893107

• A Generalist Intracortical Motor Decoder. bioRxiv : the preprint server for biology Ye, J., Rizzoglio, F., Smoulder, A., Mao, H., Ma, X., Marino, P., Chowdhury, R., Moore, D., Blumenthal, G., Hockeimer, W., Kunigk, N. G., Mayo, J. P., Batista, A., Chase, S., Rouse, A., Boninger, M. L., Greenspon, C., Schwartz, A. B., Hatsopoulos, N. G., Miller, L. E., Bouchard, K. E., Collinger, J. L., Wehbe, L., Gaunt, R. 2025

  Abstract

  Mapping the relationship between neural activity and motor behavior is a central aim of sensorimotor neuroscience and neurotechnology. While most progress to this end has relied on restricting complexity, the advent of foundation models instead proposes integrating a breadth of data as an alternate avenue for broadly advancing downstream modeling. We quantify this premise for motor decoding from intracortical microelectrode data, pretraining an autoregressive Transformer on 2000 hours of neural population spiking activity paired with diverse motor covariates from over 30 monkeys and humans. The resulting model is broadly useful, benefiting decoding on 8 downstream decoding tasks and generalizing to a variety of neural distribution shifts. However, we also highlight that scaling autoregressive Transformers seems unlikely to resolve limitations stemming from sensor variability and output stereotypy in neural datasets. Code: https://github.com/joel99/ndt3.

  View details for DOI 10.1101/2025.02.02.634313

  View details for PubMedID 39975007

  View details for PubMedCentralID PMC11838490

• Stacked regressions and structured variance partitioning for interpretable brain maps NEUROIMAGE Lin, R., Naselaris, T., Kay, K., Wehbe, L. 2024; 298: 120772

  Abstract

  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 uses 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.

  View details for DOI 10.1016/j.neuroimage.2024.120772

  View details for Web of Science ID 001294572500001

  View details for PubMedID 39117095

  View details for PubMedCentralID PMC12117960

• Computational Language Modeling and the Promise of In Silico Experimentation. Neurobiology of language (Cambridge, Mass.) Jain, S., Vo, V. A., Wehbe, L., Huth, A. G. 2024; 5 (1): 80-106

  Abstract

  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.

  View details for DOI 10.1162/nol_a_00101

  View details for PubMedID 38645624

  View details for PubMedCentralID PMC11025654

• Divergences between Language Models and Human Brains Zhou, Y., Liu, E., Neubig, G., Tarr, M. J., Wehbe, L. edited by Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2024

  View details for Web of Science ID 001633273000262

• Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset NATURE MACHINE INTELLIGENCE Wang, A. Y., Kay, K., Naselaris, T., Tarr, M. J., Wehbe, L. 2023; 5 (12): 1415-1426

  View details for DOI 10.1038/s42256-023-00753-y

  View details for Web of Science ID 001101937000002

• Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity. bioRxiv : the preprint server for biology Ye, J., Collinger, J. L., Wehbe, L., Gaunt, R. 2023

  Abstract

  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.

  View details for DOI 10.1101/2023.09.18.558113

  View details for PubMedID 37781630

  View details for PubMedCentralID PMC10541112

• A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex JOURNAL OF NEUROSCIENCE Henderson, M. M., Tarr, M. J., Wehbe, L. 2023; 43 (22): 4144-4161

  Abstract

  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.

  View details for DOI 10.1523/JNEUROSCI.1822-22.2023

  View details for Web of Science ID 001016122700006

  View details for PubMedID 37127366

  View details for PubMedCentralID PMC10255092

• Semantic Representations during Language Comprehension Are Affected by Context JOURNAL OF NEUROSCIENCE Deniz, F., Tseng, C., Wehbe, L., la Tour, T., Gallant, J. L. 2023; 43 (17): 3144-3158

  Abstract

  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.

  View details for DOI 10.1523/JNEUROSCI.2459-21.2023

  View details for Web of Science ID 000993216100003

  View details for PubMedID 36973013

  View details for PubMedCentralID PMC10146529

• Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features JOURNAL OF VISION Henderson, M. M., Tarr, M. J., Wehbe, L. 2023; 23 (4): 8

  Abstract

  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.

  View details for DOI 10.1167/jov.23.4.8

  View details for Web of Science ID 001470384700002

  View details for PubMedID 37103010

  View details for PubMedCentralID PMC10150833

• Selectivity for food in human ventral visual cortex COMMUNICATIONS BIOLOGY Jain, N., Wang, A., Henderson, M. M., Lin, R., Prince, J. S., Tarr, M. J., Wehbe, L. 2023; 6 (1): 175

  Abstract

  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.

  View details for DOI 10.1038/s42003-023-04546-2

  View details for Web of Science ID 000935179100002

  View details for PubMedID 36792693

  View details for PubMedCentralID PMC9932019

• Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity Ye, J., Collinger, J. L., Wehbe, L., Gaunt, R. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001227224000007

• Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images Sarch, G. H., Tarr, M. J., Fragkiadaki, K., Wehbe, L. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001220600000017

• Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models Luo, A. F., Henderson, M. M., Wehbe, L., Tarr, M. J. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001220600000016

• Combining computational controls with natural text reveals aspects of meaning composition. Nature computational science Toneva, M., Mitchell, T. M., Wehbe, L. 2022; 2 (11): 745-757

  Abstract

  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.

  View details for DOI 10.1038/s43588-022-00354-6

  View details for PubMedID 36777107

  View details for PubMedCentralID PMC9912822

• Brainprints: identifying individuals from magnetoencephalograms COMMUNICATIONS BIOLOGY Wu, S., Ramdas, A., Wehbe, L. 2022; 5 (1): 852

  Abstract

  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.

  View details for DOI 10.1038/s42003-022-03727-9

  View details for Web of Science ID 000843203100002

  View details for PubMedID 35995976

  View details for PubMedCentralID PMC9395342

• Same Cause; Different Effects in the Brain. Proceedings of machine learning research Toneva, M., Williams, J., Bollu, A., Dann, C., Wehbe, L. 2022; 177: 787-825

  Abstract

  To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find out which brain "zone" (e.g. which region of interest, volume pixel or electrophysiology sensor) is predicted from the stimulus properties. Given the assumptions underlying this setup, when stimulus properties are predictive of the activity in a zone, these properties are understood to cause activity in that zone. In recent years, researchers have used neural networks to construct representations that capture the diverse properties of complex stimuli, such as natural language or natural images. Encoding models built using these high-dimensional representations are often able to significantly predict the activity in large swathes of cortex, suggesting that the activity in all these brain zones is caused by stimulus properties captured in the representation. It is then natural to ask: "Is the activity in these different brain zones caused by the stimulus properties in the same way?" In neuroscientific terms, this corresponds to asking if these different zones process the stimulus properties in the same way. Here, we propose a new framework that enables researchers to ask if the properties of a stimulus affect two brain zones in the same way. We use simulated data and two real fMRI datasets with complex naturalistic stimuli to show that our framework enables us to make such inferences. Our inferences are strikingly consistent between the two datasets, indicating that the proposed framework is a promising new tool for neuroscientists to understand how information is processed in the brain.

  View details for PubMedID 40395838

• Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling. Frontiers in computational neuroscience Ravishankar, S., Toneva, M., Wehbe, L. 2021; 15: 737324

  Abstract

  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.

  View details for DOI 10.3389/fncom.2021.737324

  View details for PubMedID 34858157

  View details for PubMedCentralID PMC8632362

• Incremental Language Comprehension Difficulty Predicts Activity in the Language Network but Not the Multiple Demand Network CEREBRAL CORTEX Wehbe, L., Blank, I., Shain, C., Futrell, R., Levy, R., von der Malsburg, T., Smith, N., Gibson, E., Fedorenko, E. 2021; 31 (9): 4006-4023

  Abstract

  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.

  View details for DOI 10.1093/cercor/bhab065

  View details for Web of Science ID 000741349100003

  View details for PubMedID 33895807

  View details for PubMedCentralID PMC8328211

• A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG. IEEE transactions on medical robotics and bionics Zha, X., Wehbe, L., Sclabassi, R. J., Mace, Z., Liang, Y. V., Yu, A., Leonardo, J., Cheng, B. C., Hillman, T. A., Chen, D. A., Riviere, C. N. 2021; 3 (1): 44-52

  Abstract

  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.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.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.The CNN-LSTM model shows promise for automated classification of continuous EMG in IONM.This technique has potential to improve surgical safety by reducing cognitive load and inter-rater variability.

  View details for DOI 10.1109/tmrb.2020.3048255

  View details for PubMedID 33997657

  View details for PubMedCentralID PMC8117925

• Can fMRI reveal the representation of syntactic structure in the brain Reddy, A., Wehbe, L. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

  View details for Web of Science ID 000901616404017

• Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction Toneva, M., Stretcu, O., Poczos, B., Wehbe, L., Mitchell, T. M. edited by Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

  View details for Web of Science ID 001207690602048

• The lexical semantics of adjective-noun phrases in the human brain HUMAN BRAIN MAPPING Fyshe, A., Sudre, G., Wehbe, L., Rafidi, N., Mitchell, T. M. 2019; 40 (15): 4457-4469

  Abstract

  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.

  View details for DOI 10.1002/hbm.24714

  View details for Web of Science ID 000476082900001

  View details for PubMedID 31313467

  View details for PubMedCentralID PMC6865843

• Language Processing in the Brain: Mapping Neural Activity to Language Meaning HUMAN LANGUAGE: FROM GENES AND BRAINS TO BEHAVIOR Wehbe, L., Fyshe, A., Mitchell, T. M. edited by Hagoort, P. 2019: 325-335

  View details for Web of Science ID 000552895700024

• Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity Wang, A. Y., Tarr, M. J., Wehbe, L. edited by Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000535866907019

• Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain) Toneva, M., Wehbe, L. edited by Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000535866906059

• Self-Discriminative Learning for Unsupervised Document Embedding Chen, H., Hu, C., Wehbe, L., Lin, S., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 2465-2474

  View details for Web of Science ID 000900116902066

• Inducing brain-relevant bias in natural language processing models Schwartz, D., Toneva, M., Wehbe, L. edited by Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000535866905074

• REGULARIZED BRAIN READING WITH SHRINKAGE AND SMOOTHING. The annals of applied statistics Wehbe, L., Ramdas, A., Steorts, R. C., Shalizi, C. R. 2015; 9 (4): 1997-2022

  Abstract

  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.

  View details for DOI 10.1214/15-aoas837

  View details for PubMedID 34326914

  View details for PubMedCentralID PMC8317475

• The Spatio-Temporal Representation of Natural Reading Wehbe, L. edited by Yang, Q., Wooldridge, M. IJCAI-INT JOINT CONF ARTIF INTELL. 2015: 4407-4408

  View details for Web of Science ID 000442637804100

• Nonparametric Independence Testing for Small Sample Sizes Ramdas, A., Wehbe, L. edited by Yang, Q., Wooldridge, M. IJCAI-INT JOINT CONF ARTIF INTELL. 2015: 3777-3783

  View details for Web of Science ID 000442637803117

• Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses PLOS ONE Wehbe, L., Murphy, B., Talukdar, P., Fyshe, A., Ramdas, A., Mitchell, T. 2014; 9 (11): e112575

  Abstract

  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.

  View details for DOI 10.1371/journal.pone.0112575

  View details for Web of Science ID 000349145400019

  View details for PubMedID 25426840

  View details for PubMedCentralID PMC4245107

• Tracking neural coding of perceptual and semantic features of concrete nouns NEUROIMAGE Sudre, G., Pomerleau, D., Palatucci, M., Wehbe, L., Fyshe, A., Salmelin, R., Mitchell, T. 2012; 62 (1): 451-463

  Abstract

  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.

  View details for DOI 10.1016/j.neuroimage.2012.04.048

  View details for Web of Science ID 000305859300044

  View details for PubMedID 22565201

  View details for PubMedCentralID PMC4465409