Shared computational principles for language processing in humans and deep language models

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 generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (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; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies

Gradients and Modulation of K+ Channels Optimize Temporal Accuracy in Networks of Auditory Neurons

Accurate timing of action potentials is required for neurons in auditory brainstem nuclei to encode the frequency and phase of incoming sound stimuli. Many such neurons express “high threshold” Kv3-family channels that are required for firing at high rates (>∼200 Hz). Kv3 channels are expressed in gradients along the medial-lateral tonotopic axis of the nuclei. Numerical simulations of auditory brainstem neurons were used to calculate the input-output relations of ensembles of 1–50 neurons, stimulated at rates between 100–1500 Hz. Individual neurons with different levels of potassium currents differ in their ability to follow specific rates of stimulation but all perform poorly when the stimulus rate is greater than the maximal firing rate of the neurons. The temporal accuracy of the combined synaptic output of an ensemble is, however, enhanced by the presence of gradients in Kv3 channel levels over that measured when neurons express uniform levels of channels. Surprisingly, at high rates of stimulation, temporal accuracy is also enhanced by the occurrence of random spontaneous activity, such as is normally observed in the absence of sound stimulation. For any pattern of stimulation, however, greatest accuracy is observed when, in the presence of spontaneous activity, the levels of potassium conductance in all of the neurons is adjusted to that found in the subset of neurons that respond better than their neighbors. This optimization of response by adjusting the K+ conductance occurs for stimulus patterns containing either single and or multiple frequencies in the phase-locking range. The findings suggest that gradients of channel expression are required for normal auditory processing and that changes in levels of potassium currents across the nuclei, by mechanisms such as protein phosphorylation and rapid changes in channel synthesis, adapt the nuclei to the ongoing auditory environment

Assessment of Ionic Interferences to Nitrate and Potassium Analyses with Ion-Selective Electrodes

Ion-selective electrodes (ISEs) are simple tools used for rapid measurement of nitrate nitrogen (NO3-N) and potassium (K) concentrations in plant sap. With the development of best management practices (BMPs), interest exists in using ISEs for soil leachate and soil and fertilizer solutions. Nitrate N and K concentrations in the 0 to 10,000 mg L–1 ISE working range were measured in diluted solutions of common salts to assess ionic interference of calcium (Ca2+), ammonium (NH4+), chloride (Cl–), and sulfate (SO42–). The effects of meter (replication) were unexpectedly significant in one out of three ranges for NO3-N and K (P values of 0.50, 0.72, and 0.01 for NO3-N and 0.99, 0.01, and 0.74 for K, for the 0–100, 100–1,000 and 1,000–10,000 mg L–1 ranges, respectively). The responses of calculated NO3-N and K concentrations to measured NO3-N and K concentrations were linear, but slopes ranged from 0.85 to 1.54, from 0.24 to 2.72, and from 0.93 to 5.48 for NO3-N and from 0.80 to 1.01, from 0.71 to 1.39, and from 0.93 to 2.21 for K for the 0–100, 100–1,000, and 1,000–10,000 mg L–1 measuring ranges, respectively. All slopes were significantly different from zero, and several were significantly different from each other and the 1:1 line. Pairwise slope comparisons conducted with covariance analysis showed that SO42– alone interfered with NO3-N measurements at concentrations ranging from 34 to 171 mg L–1, which was less than the manufacturer’s information, and by its presence in combination with K+, NH4+, Ca2+, and Cl– within the medium and high concentration ranges. Potassium measurements were not subject to interference from the ions tested for all three concentration ranges. These results highlight the importance of using quality assurance / quality control (QA/QC) samples in the set of unknown samples to detect inacceptable departure from linearity in routine analysis. The increase in measurement variability from one range to the next showed the importance of keeping measurements within a single concentration range by using dilutions. Hence, ISEs may be used for field measurements of NO3-N and K concentrations in soil leachate as well as soil and nutrient solutions and are therefore a practical BMP tool. However, ISEs should not be used as substitutes for the laboratory methods when official measurements are needed

Acute ischemic stroke alters the brain's preference for distinct dynamic connectivity states

Acute ischemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for loss of specialized neural tissue and function. Static resting-state functional magnetic resonance imaging (fMRI) studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we utilized dynamic resting-state fMRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting-state fMRI data of 31 acute ischemic stroke patients and 17 age-matched healthy controls. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. Temporal connectivity patterns of stroke patients markedly diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n=18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n=13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased inter-hemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting-state fMRI studies post-stroke. In summary, acute ischemic stroke not only prompted changes in connectivity between distinct functional networks, yet also caused severe aberrations in temporal properties of large-scale network interactions depending on the individual deficit severity. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients

Dynamic connectivity predicts acute motor impairment and recovery post-stroke

Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We herebuilt random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied onstructural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivitywas estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later.A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patientswith and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.6760.01). In contrast,patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on thevariability of dynamic connectivity (0.8360.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortexand putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivityconfigurations (0.8960.01) in combination with the initial impairment. Here, better recovery was linked to a shortertime spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acuteimpairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery