Distinct roles for the anterior temporal lobe and angular gyrus in the spatiotemporal cortical semantic network

Semantic knowledge is supported by numerous brain regions, but the spatiotemporal configuration of the network that links these areas remains an open question. The hub-and-spokes model posits that a central semantic hub coordinates this network. In this study, we explored distinct aspects that define a semantic hub, as reflected in the spatiotemporal modulation of neural activity and connectivity by semantic variables, from the earliest stages of semantic processing. We used source-reconstructed electro/magnetoencephalography, and investigated the concreteness contrast across three tasks. In a whole-cortex analysis, the left anterior temporal lobe (ATL) was the only area that showed modulation of evoked brain activity from 100 ms post-stimulus. Furthermore, using Dynamic Causal Modeling of the evoked responses, we investigated effective connectivity amongst the candidate semantic hub regions, that is, left ATL, supramarginal/angular gyrus (SMG/AG), middle temporal gyrus, and inferior frontal gyrus. We found that models with a single semantic hub showed the highest Bayesian evidence, and the hub region was found to change from ATL (within 250 ms) to SMG/AG (within 450 ms) over time. Our results support a single semantic hub view, with ATL showing sustained modulation of neural activity by semantics, and both ATL and AG underlying connectivity depending on the stage of semantic processing

Modelling subject variability in the spatial and temporal characteristics of functional modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates