Topographic gradients of intrinsic dynamics across neocortex

Abstract

<jats:title>Abstract</jats:title><jats:p>The intrinsic dynamics of neuronal populations are shaped by both macroscale connectome architecture and microscale attributes. Neural activity arising from the interplay of these local and global factors therefore varies from moment to moment, with rich temporal patterns. Here we comprehensively characterize intrinsic dynamics throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we estimate over 6,000 statistical properties of individual brain regions’ time series across the neocortex. We identify two robust topographic gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and the other spanning a unimodal-transmodal axis. These gradients are distinct in terms of their temporal composition and reflect spatial patterns of microarray gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are closely correlated with patterns of functional activation, differentiating cognitive <jats:italic>versus</jats:italic> affective processing and sensory <jats:italic>versus</jats:italic> higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.</jats:p>

Comparing spatial null models for brain maps

“…The connections and interactions among these circuits ultimately manifest as unique topographic distributions of structural and functional properties. Recent advances in imaging, tracing, and recording technologies (Insel et al 2013), together with global data sharing initiatives (Casey et al 2018, Poldrack et al 2013, Sudlow et al 2015, Van Essen et al 2013), have resulted in the generation of high-resolution maps of many of these properties, including gene expression (Akbarian et al 2015, Hawrylycz et al 2012), cytology (Scholtens et al 2018, von Economo and Koskinas 1925), receptor densities (Beliveau et al 2017, Norgaard et al 2020, Zilles and Amunts 2009, Zilles et al 2004), intracortical myelin (Burt et al 2018, Whitaker et al 2016), and functional organization (Bellec et al 2010, Damoiseaux et al 2006, Margulies et al 2016, Murray et al 2014, Shafiei et al 2020, Yeo et al 2011).…”

“…Increasingly, modern scientific discovery in neuroimaging research involves identifying correspondences between the topographies of brain maps (Baum et al 2020, Demirtaş et al 2019, Gao et al 2020, Hansen et al 2020, Shafiei et al 2020, Vázquez-Rodríguez et al 2019, Wang et al 2019); however, standard methods for statistical inference fall short when making such comparisons (Alexander-Bloch et al 2013, 2018, Breakspear et al 2004, Burt et al 2020, Fulcher et al 2020, Gordon et al 2016). Namely, in spatially-embedded systems—like the brain—neighboring data points are not statistically independent, violating the assumptions of many common inferential frameworks.…”

Learning function from structure in neuromorphic networks

“…How does the organization of the brain confer computational capacity? One way to address this question is to relate structural connectivity to neural dynamics and emergent patterns of functional connectivity [22, 119, 135, 144]. Another is to relate structural connectivity to individual differences in behaviour [90, 96, 117].…”

rest2vec: Vectorizing the resting-state functional connectome using graph embedding

“…Recently, Bethlehem et al (2020) investigated how the functional communities in their diffusion map embedding space change across age using a novel dispersion metric. In a different approach, Shafiei et al (2020) have proposed using tools from the time series analysis field to perform feature extraction on the temporal dynamics of functional connectivity to study the intrinsic dynamics of cortical gradients. Interestingly, the principal gradient identified across many of these techniques bear a striking similarity, suggesting this gradient accounts for much of the variance of intrinsic functional connectivity.…”