Biology subjects

Schain, M.

Publications and source records attributed to Schain, M..

10 recordsLinked to original sources

bioRxiv2024

Information-making processes in the speaker's brain drive human conversations forward

Natural human conversation is driven by the exchange of information-rich messages that surprise the listener and deviate from predictable context. While extensive research has characterized how the brain processes unexpected linguistic input during comprehension, the neural mechanisms underlying the generation of such information by the speaker remain poorly understood. In this study, we hypothesize that the speakers brain actively creates information through extra neural computation, transforming internal thoughts into novel, meaningful linguistic outputs. Utilizing a unique 24/7 dataset of continuous electrocorticography (ECoG) comprising approximately 100 hours of spontaneous natural conversations, we contrast the neural basis of speech production and comprehension within the same participants. Behaviorally, we find that speakers take longer pausing for an additional 100-150 milliseconds before producing information-rich, improbable words, even when controlling for word identity and frequency. Neurally, we provide converging evidence for a previously unreported process: generating information-rich words elicits significantly stronger neural activity (ERP) and enhanced neural encoding and decoding in language-related areas starting 100-500 ms before articulation. This pattern contrasts sharply with speech comprehension, where enhanced neural activity for predictable words occurs before onset, while responses to improbable words emerge only after onset as prediction errors. Furthermore, we demonstrate that large language models (LLMs) mirror this biological process, requiring deeper internal computation across layers to generate improbable versus probable words. Together, these results reveal that the speakers brain is not merely a transmission channel but an active generator of information, recruiting extra neural resources to create novel content that diverges from listener expectations.

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bioRxiv2023

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

Humans effortlessly use the continuous acoustics of speech to communicate rich linguistic meaning during everyday conversations. In this study, we leverage 100 hours (half a million words) of spontaneous open-ended conversations and concurrent high-quality neural activity recorded using electrocorticography (ECoG) to decipher the neural basis of real-world speech production and comprehension. Employing a deep multimodal speech-to-text model named Whisper, we develop encoding models capable of accurately predicting neural responses to both acoustic and semantic aspects of speech. Our encoding models achieved high accuracy in predicting neural responses in hundreds of thousands of words across many hours of left-out recordings. We uncover a distributed cortical hierarchy for speech and language processing, with sensory and motor regions encoding acoustic features of speech and higher-level language areas encoding syntactic and semantic information. Many electrodes--including those in both perceptual and motor areas--display mixed selectivity for both speech and linguistic features. Notably, our encoding model reveals a temporal progression from language-to-speech encoding before word onset during speech production and from speech-to-language encoding following word articulation during speech comprehension. This study offers a comprehensive account of the unfolding neural responses during fully natural, unbounded daily conversations. By leveraging a multimodal deep speech recognition model, we highlight the power of deep learning for unraveling the neural mechanisms of language processing in real-world contexts.

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bioRxiv2022

Kinetic models for PET displacement studies

The traditional design of PET target engagement studies is based on a baseline scan and one or more scans after drug administration. We here evaluate an alternative design in which the drug is administered during an on-going scan (i.e., a displacement study). This approach results both in lower radiation exposure and lower costs. Existing kinetic models assume steady state. This condition is not present during a drug displacement and consequently, our aim here was to develop kinetic models for analysing PET displacement data. We modified existing compartment models to accommodate a time-variant increase in occupancy following the pharmacological in-scan intervention. Since this implies the use of differential equations that cannot be solved analytically, we developed instead one approximate and one numerical solution. Through simulations, we show that if the occupancy is relatively high, it can be estimated without bias and with good accuracy. The models were applied to PET data from six pigs where [11C]UCB-J was displaced by intravenous brivaracetam. The dose-occupancy relationship estimated from these scans showed good agreement with occupancies calculated with Lassen plot applied to baseline-block scans of two pigs. In summary, the proposed models provide a framework to determine target occupancy from a single displacement scan.

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bioRxiv2022

Correspondence between the layered structure of deep language models and temporal structure of natural language processing in the human brain

Deep language models (DLMs) provide a novel computational paradigm for how the brain processes natural language. Unlike symbolic, rule-based models described in psycholinguistics, DLMs encode words and their context as continuous numerical vectors. These "embeddings" are constructed by a sequence of computations organized in "layers" to ultimately capture surprisingly sophisticated representations of linguistic structures. How does this layered hierarchy map onto the human brain during natural language comprehension? In this study, we used electrocorticography (ECoG) to record neural activity in language areas along the superior temporal gyrus and inferior frontal gyrus while human participants listened to a 30-minute spoken narrative. We supplied this same narrative to a high-performing DLM (GPT2-XL) and extracted the contextual embeddings for each word in the story across all 48 layers of the model. We next trained a set of linear encoding models to predict the temporally-evolving neural activity from the embeddings at each layer. We found a striking correspondence between the layer-by-layer sequence of embeddings from GPT2-XL and the temporal sequence of neural activity in language areas. In addition, we found evidence for the gradual accumulation of recurrent information along the linguistic processing hierarchy. However, we also noticed additional neural processes in the brain, but not in DLMs, during the processing of surprising (unpredictable) words. These findings point to a connection between human language processing and DLMs where the layer-by-layer accumulation of contextual information in DLM embeddings matches the temporal dynamics of neural activity in high-order language areas.

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bioRxiv2022

Differential D1 and D2 receptor internalization and recycling induced by amphetamine in vivo

The dopamine system plays a significant role in drug reward and the pathogenesis of addiction. Psychostimulant drugs acutely increase dopamine levels, triggering receptor internalization. In vitro data suggest that dopamine D1 receptors (D1R) recycle, whereas D2 receptors (D2R) degrade in response to activation. Yet, receptor fates in vivo remain unclear. This study bridges in vitro mechanisms and in vivo measurements of stimulant-induced modulation of receptor states using longitudinal multi-modal imaging combined with neuropharmacology. We demonstrate how repeated amphetamine administration differentially modulates D1R vs. D2R signaling in nonhuman primates over 24 hours using simultaneous positron emission tomography and functional magnetic resonance imaging. In contrast to predominantly inhibitory D2R signaling due to an initial amphetamine challenge, excitatory D1R functional signaling prevails three hours later, while D2Rs stay internalized. These results demonstrate differential externalization mechanisms of the D1R and D2R in vivo and a shift in receptor subtype activation after a dopamine surge.

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bioRxiv2022

Brain embeddings with shared geometry to artificial contextual embeddings, as a code for representing language in the human brain

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. Do language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language? To test this hypothesis, we densely recorded the neural activity in the Inferior Frontal Gyrus (IFG, also known as Brocas area) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derived for each patient a continuous vectorial representation for each word (i.e., a brain embedding). Using stringent, zero-shot mapping, we demonstrated that brain embeddings in the IFG and the DLM contextual embedding space have strikingly similar geometry. This shared geometry allows us to precisely triangulate the position of unseen words in both the brain embedding space (zero-shot encoding) and the DLM contextual embedding space (zero-shot decoding). The continuous brain embedding space provides an alternative computational framework for how natural language is represented in cortical language areas.

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bioRxiv2021

Nigro-striatal dopamine activation lowers behavioral and neuronal phenotypes associated with obsessive-compulsive disorder

Dorsal striatal dopamine transmission engages the cortico-striato-thalamo-cortical (CSTC) circuit, which is implicated in many neuropsychiatric diseases, including obsessive-compulsive disorder (OCD). Yet it is unknown if dorsal striatal dopamine hyperactivity is the cause or consequence of changes elsewhere in the CSTC circuit. Classical pharmacological and neurotoxic manipulations of the CSTC and other brain circuits suffer from various drawbacks related to off-target effects and adaptive changes. Chemogenetics, on the other hand, enables a highly selective targeting of specific neuronal populations within a given circuit. In this study, we developed a chemogenetic method for selective activation of dopamine neurons in the substantia nigra, which innervating the rat dorsal striatum. We used this model to investigate effects of targeted dopamine activation on CSTC circuit function, especially in fronto-cortical regions. We found that chemogenetic activation of these neurons increased movement, as expected from dopamine release, rearings and time spend in center, while it also lowered self-grooming and increased prepulse inhibition in females. Remarkably, we observed reduced [18F]FDG metabolism in frontal cortex, following dopamine activation in the dorsal striatum, yet total glutamate levels-in this region were increased. A finding which may help explain the contradiction in some clinical studies of increased [18F]FDG metabolism and lower glutamate levels in diseases like OCD. Taken together, these results establish the importance of nigro-striatal dopamine transmission for modulating CSTC function, especially with respect to fronto-cortical activity, glutamate levels and behaviors related anxiety and compulsive actions. One Sentence SummaryDorsal striatum dopamine induce fronto-cortical hypoactivity and reduce compulsive behaviors in rats

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bioRxiv2020

Thinking ahead: prediction in context as a keystone of language in humans and machines

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuous next-word prediction before word-onset; 2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.

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bioRxiv2019

Validity and reliability of extrastriatal raclopride binding quantification in the living human brain

[11C]raclopride is a well established PET tracer for the quantification of dopamine 2/3 receptors (D2/3R) in the striatum. Outside of the striatum the receptor density is up to two orders of magnitude lower. In contrast to striatal binding, the characteristics of extrastriatal [11C]raclopride binding quantification has not been thoroughly described. Still, binding data for e.g., neocortex is frequently reported in the scientific literature. Here we evaluate the validity and reliability of extrastriatal [11C]raclopride binding quantification. Two sets of healthy control subjects were examined with HRRT and [11C]raclopride: i) To assess the validity of extrastriatal [11C]raclopride binding estimates, eleven subjects were examined at baseline and after dosing with quetiapine, a D2/3R antagonist. ii) To assess test-retest repeatability, nine subjects were examined twice. Non displaceable binding potential (BPND) was quantified using the simplified reference tissue model. Quetiapine dosing was associated with decrease in [11C]raclopride BPND in temporal cortex (18{+/-}17% occupancy) and thalamus (20{+/-}17%), but not in frontal cortex. Extrastriatal occupancy was lower than in putamen (51{+/-}4%). The mean absolute variation was 4-7% in the striatal regions, 17% in thalamus, and 13-59% in cortical regions. Our data indicate that [11C]raclopride PET is not a suitable tool for D2/3R binding quantification in extrastriatal regions.

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bioRxiv2018

Accuracy and reliability of PBR28 specific binding estimated without the use of a reference region

[11C]PBR28 is a positron emission tomography radioligand used to estimate the expression of 18kDa translocator protein (TSPO). TSPO is expressed on glial cells and can function as a marker for immune activation. Since TSPO is expressed throughout the brain, no true reference region exists. For this reason, an arterial input function is required for accurate quantification of [11C]PBR28 binding and the most common outcome measure is the total distribution volume (VT). Notably, VT reflects both specific binding and non-displaceable binding (VND). Therefore, estimates of specific binding, such as binding potentials (e.g., BPND) and specific distribution volume (VS) should theoretically be more sensitive to underlying differences in TSPO expression. It is unknown, however, if unbiased and accurate estimates of these measures are obtainable for [11C]PBR28.\n\nThe Simultaneous Estimation (SIME) method uses time-activity-curves from multiple brain regions with the aim to obtain a brain-wide estimate of VND, which can subsequently be used to improve the estimation of BPND and VS. In this study we evaluated the accuracy of SIME-derived VND, and the reliability of resulting estimates of specific binding for [11C]PBR28, using a combination of simulation experiments and in vivo studies in healthy humans.\n\nThe simulation experiments showed that VND values estimated using SIME were both precise and accurate. Data from a pharmacological competition challenge showed that SIME provided VND values that were on average 19% lower than those obtained using the Lassen plot, but similar to values obtained using the Likelihood-Estimation of Occupancy technique. Test-retest data showed that SIME-derived VS values exhibited good reliability and precision, while larger variability was observed in SIME-derived BPND values.\n\nThe results support the use of SIME for quantifying specific binding of [11C]PB28, and suggest that VS can be used in preference to, or as a complement to the conventional outcome measure VT. Additional studies in patient cohorts are warranted.

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