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A brain–computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain–computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain–computer interfaces. Tang et al. show that continuous language can be decoded from functional MRI recordings to recover the meaning of perceived and imagined speech stimuli and silent videos and that this language decoding requires subject cooperation.

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.

Speech comprehension is a complex process that draws on humans’ abilities to extract lexical information, parse syntax, and form semantic understanding. These sub-processes have traditionally been studied using separate neuroimaging experiments that attempt to isolate specific effects of interest. More recently it has become possible to study all stages of language comprehension in a single neuroimaging experiment using narrative natural language stimuli. The resulting data are richly varied at every level, enabling analyses that can probe everything from spectral representations to high-level representations of semantic meaning. We provide a dataset containing BOLD fMRI responses recorded while 8 participants each listened to 27 complete, natural, narrative stories (~6 hours). This dataset includes pre-processed and raw MRIs, as well as hand-constructed 3D cortical surfaces for each participant. To address the challenges of analyzing naturalistic data, this dataset is accompanied by a python library containing basic code for creating voxelwise encoding models. Altogether, this dataset provides a large and novel resource for understanding speech and language processing in the human brain.

Arundo donax (hereafter referred to as Arundo), a robust herbaceous plant, has invaded the riparian zones of the Rio Grande River and the rivers of the Texas Hill Country over the last two decades. Arundo was first observed along the Nueces River in central Texas in 1995 by the Nueces River Authority (NRA). It then spread rapidly downstream due to its fast growth rate and availability of streamflow for its consumptive use, and it completely displaced the native vegetation, primarily Panicum virgatum (hereafter referred to as switchgrass) in the riparian zone. It was hypothesized that Arundo reduced streamflows due to higher water use by Arundo when compared to switchgrass. The overall goal of this study was to assess the impacts of Arundo invasion on hydrology of the headwaters of the Nueces River through observed long-term streamflow and precipitation data analysis and simulation modeling with the Soil and Water Assessment Tool (SWAT). The observed data analysis indicated that while there was no significant change in monthly precipitation between the pre-Arundo invasion (1979–1994) and post-Arundo invasion (1995–2010) periods, streamflows changed significantly showing a positive (slightly increasing) trend during the pre-invasion period and a negative (slightly decreasing) trend during the post-invasion periods. The simulated average (1995–2010) annual evapotranspiration of Arundo in the seven Hydrologic Response Units (HRUs) in which Arundo invaded, was higher by 137 mm when compared to switchgrass. The water uptake by Arundo was therefore higher by 7.2% over switchgrass. Higher water uptake by Arundo resulted in a 93 mm higher irrigation (water use from the reach/stream) annually when compared to switchgrass. In addition, the simulated average annual water yield (net amount of water that was generated from the seven Arundo HRUs and contributed to streamflow) under Arundo was less by about 17 mm as compared to switchgrass. In conclusion, model simulations indicated that Arundo invasion in the Nueces River has caused a statistically significant increase in water uptake and reduction in streamflow compared to the native switchgrass, which previously dominated the headwaters.

Disclosure: S. Jain: None. M. Crabtree: None. C. Houston: None. Background: Structural homology between human chorionic gonadotropin (hCG) and thyrotropin stimulating hormone (TSH) allows high levels of hCG to exert thyrotropic effects via TSH receptors. Although hCG-mediated hyperthyroidism is most frequently encountered in the context of pregnancy, it also occurs rarely as a paraneoplastic syndrome. We present a case of hCG-mediated hyperthyroidism due to metastatic choriocarcinoma in a postmenopausal woman. Case Presentation: A previously healthy 59-year-old woman was evaluated for shortness of breath and found to have multiple lung nodules concerning for metastatic malignancy. Further imaging demonstrated an enlarged heterogeneous uterus with an enlarged endometrial cavity. Endometrial biopsy revealed poorly differentiated carcinoma with some areas of possible squamous differentiation. Subspecialty cytopathology review was requested. One week after biopsy, the patient was brought to the emergency department with acute hypoxic respiratory failure following a seizure. She was found to be febrile, tachycardic, hypertensive, and minimally responsive. Head CT revealed a left frontal brain mass with surrounding vasogenic edema, concerning for metastasis. Laboratory evaluation revealed TSH of 0.03 mIU/L (normal 0.35-4.94) and free T4 of 2.30 ng/dL (normal 0.70-1.48). Thyroid stimulating immunoglobulin was negative. Beta-hCG was found to be significantly elevated at 982,093 mIU/mL after 1:100 dilution. She was started on treatment with propranolol, dexamethasone, and chemotherapy with etoposide and cisplatin. Meanwhile, cytopathology review identified scattered cells with features of choriocarcinoma and positive hCG immunostaining, felt to represent a diagnosis of poorly differentiated choriocarcinoma. After one cycle of chemotherapy, the patient’s beta-hCG decreased to 477,668 mIU/mL and free T4 decreased to 1.05 ng/dL. Due to progressive worsening of her clinical status, she was transitioned to palliative care and passed away on hospital day 14. Discussion: Hyperthyroidism mediated by hCG is most often seen in patients with hyperemesis gravidarum, gestational transient thyrotoxicosis, or gestational trophoblastic disease. Our case underscores the importance of considering hCG-mediated hyperthyroidism outside the context of pregnancy. Rare paraneoplastic sources of hCG include choriocarcinoma, seminoma, and other germ cell tumors. Hyperthyroidism in these cases is usually subclinical or mild, but instances of thyroid storm have been reported with metastatic disease. Beta blockers, glucocorticoids, and thionamides can be used as temporizing measures, but definitive therapy requires treatment of the causative malignancy. Presentation Date: Saturday, June 17, 2023

A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, decoders that reconstruct continuous language use invasive recordings from surgically implanted electrodes1–3, while decoders that use non-invasive recordings can only identify stimuli from among a small set of letters, words, or phrases4–7. Here we introduce a non-invasive decoder that reconstructs continuous natural language from cortical representations of semantic meaning8 recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech, and even silent videos, demonstrating that a single language decoder can be applied to a range of semantic tasks. To study how language is represented across the brain, we tested the decoder on different cortical networks, and found that natural language can be separately decoded from multiple cortical networks in each hemisphere. As brain-computer interfaces should respect mental privacy9, we tested whether successful decoding requires subject cooperation, and found that subject cooperation is required both to train and to apply the decoder. Our study demonstrates that continuous language can be decoded from non-invasive brain recordings, enabling future multipurpose brain-computer interfaces.

Keywords: Low level lasers, aphthous ulcers, pain Introduction: It is a well-known fact that a high level laser exhibits photothermal properties resulting in therapeutic effects like surgical cutting and hemostasis while the low level lasers exhibit properties like analgesic, anti-inflammatory and biostimulation.1,2 Low level Lasers are employed in numerous clinical scenarios like mucositis, neural regeneration, postherpetic neuralgia, synovitis, arthritis, problems of the temporomandibular joint, acute swelling, periapical granuloma, chronic orofacial pain, gingival depigmentation, and bone regeneration.3,4,5 The analgesic effect of LLLT causes neural conduction blockade by stimulating the synthesis of endogenous endorphins (beta-endorphin), reducing inflammatory cytokines and enzymes, altering the pain threshold, inducing changes in morphological neurons, reducing the mitochondrial membrane potential, and blocking rapid axon flow.6 The anti-inflammatory effect occurs due to the increase in the phagocytic activity, the number and the diameter of lymphatic vessels, diminished permeability of blood vessels and microcapillary blood circulation restoration, normalization of blood vessel permeability along with diminished edema.6,7 Recurrent aphthous stomatitis (RAS) encompasses a set of long standing, inflammatory, ulcerative disease affecting the oral mucosa, the prevalence of which is 21.7%.8,9 Clinically, three kinds of RAS have been advocated in the form of Minor aphthae, major aphthae, and herpetiform aphthae.10 This current study is intended to assess the effectiveness of low level lasers in the management of recurrent aphthous stomatitis. In addition to this, the side effects they are associated with and the patient compliance that is required have made this option a less reliable option.2 However, literature evidence suggesting the positive effects of lasers on the cell metabolism, inflammatory modulation, edema reduction, tissue regeneration, healing time, and pain relief makes their use a viable option in the management of recurrent aphthous stomatitis.13,14,15 Low-level laser therapy off late is being considered as the first line of treatment for managing these conditions. The results of this study are in accordance with previous studies.20 This could be attributed to the fact that low level lasers enhance the healing by improving the ATP synthesis leading to an enhanced mitotic activity which increases the protein synthesis by mitochondria, resulting in greater tissue regeneration in the repair process.2 It can be concluded based on the results of this study along with the evidence from the existing literature that low level lasers application on recurrent aphthous stomatistis would reduce the healing time, pain intensity, and frequency of the lesion. [...]it can be considered as the most appropriate treatment

Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is unclear what features of the language stimulus drive the response in each brain area. We present generative causal testing (GCT), a framework for generating concise explanations of language selectivity in the brain from predictive models and then testing those explanations in follow-up experiments using LLM-generated stimuli.This approach is successful at explaining selectivity both in individual voxels and cortical regions of interest (ROIs), including newly identified microROIs in prefrontal cortex. We show that explanatory accuracy is closely related to the predictive power and stability of the underlying predictive models. Finally, we show that GCT can dissect fine-grained differences between brain areas with similar functional selectivity. These results demonstrate that LLMs can be used to bridge the widening gap between data-driven models and formal scientific theories.

Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is unclear what features of the language stimulus drive the response in each brain area. We present generative causal testing (GCT), a framework for generating concise explanations of language selectivity in the brain from predictive models and then testing those explanations in follow-up experiments using LLM-generated stimuli.This approach is successful at explaining selectivity both in individual voxels and cortical regions of interest (ROIs), including newly identified microROIs in prefrontal cortex. We show that explanatory accuracy is closely related to the predictive power and stability of the underlying predictive models. Finally, we show that GCT can dissect fine-grained differences between brain areas with similar functional selectivity. These results demonstrate that LLMs can be used to bridge the widening gap between data-driven models and formal scientific theories.