JOSA: Joint surface-based registration and atlas construction of brain geometry and function

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal contribution. arXiv admin note: text overlap with arXiv:2303.0159

Single-Trial Decoding of Scalp EEG under Natural Conditions

There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding

Context-sensitive features predict sentence memorability in the absence of memorable words

What makes some sentences more memorable than others? In this work, we treat the problem of recognizing previously seen sentences as a comparison between a target stimulus and noisy memory representations of previously presented stimuli. Building on past work in image and word memorability, we conduct a large-scale memorability experiment with 500 participants and 2,500 target sentences, eliciting variation in how accurately participants recognize repeated sentences. We predict the memorability of sentences from a) empirically established word-level memorability scores, and b) sentence-level distinctiveness and surprisal features that capture the compositional semantics of sentences. We find that the presence of individually memorable words is highly predictive of sentence memorability, but that sentence-level features also predict sentence memorability – especially in the absence of memorable words. This suggests that otherwise forgettable words can together create memorable compositional meanings that remain in memory and facilitate recognition

The neural architecture of language: Integrative modeling converges on predictive processing

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking this approach to higher-level cognition: human language processing, our species’ signature cognitive skill. We find that the most powerful “transformer” models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (functional MRI and electrocorticography). Models’ neural fits (“brain score”) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.</jats:p

Agonist-antagonist myoneural interface amputation preserves proprioceptive sensorimotor neurophysiology in lower limbs

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works The brain undergoes marked changes in function and functional connectivity after limb amputation. The agonist-antagonist myoneural interface (AMI) amputation is a procedure that restores physiological agonist-antagonist muscle relationships responsible for proprioceptive sensory feedback to enable greater motor control. We compared results from the functional neuroimaging of individuals (n = 29) with AMI amputation, traditional amputation, and no amputation. Individuals with traditional amputation demonstrated a significant decrease in proprioceptive activity, measured by activation of Brodmann area 3a, whereas functional activation in individuals with AMIs was not significantly different from controls with no amputation (P< 0.05). The degree of proprioceptive activity in the brain strongly correlated with fascicle activity in the peripheral muscles and performance on motor tasks (P < 0.05), supporting the mechanistic basis of the AMI procedure. These results suggest that surgical techniques designed to restore proprioceptive peripheral neuromuscular constructs result in desirable central sensorimotor plasticity