A place for time: the spatiotemporal structure of neural dynamics during natural audition

Stephens, Greg J.; Honey, Christopher J.; Hasson, Uri

doi:10.1152/jn.00268.2013

Cited by 139 publications

(172 citation statements)

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“…The temporal integration hierarchy is related to a gradient in intrinsic dynamics. Areas with longer temporal receptive fields show slower resting-state fluctuations in human electrocorticography [51] and functional MRI data [54], as well as in single neuron spike trains in the macaque monkey [55].…”

Section: A Temporal Hierarchy Links Structural Gradients and Functionmentioning

confidence: 99%

Large-Scale Gradients in Human Cortical Organization

Huntenburg

Bazin

Margulies

2018

Trends in Cognitive Sciences

723

738

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Recent advances in mapping cortical areas in the human brain provide a basis for investigating the significance of their spatial arrangement. Here we describe a dominant gradient in cortical features that spans between sensorimotor and transmodal areas. We propose that this gradient constitutes a core organizing axis of the human cerebral cortex, and describe an intrinsic coordinate system on its basis. Studying the cortex with respect to these intrinsic dimensions can inform our understanding of how the spectrum of cortical function emerges from structural constraints. The Significance of Cortical LocationFor more than a century, neuroscientists have studied the cerebral cortex by delineating individual cortical areas (see Glossary) and mapping their function [1]. This agenda has substantially advanced in recent years, as automated parcellation methods improve and data sets of unprecedented size and quality become available [2][3][4]. Nevertheless, our understanding of how the complex structure of the cerebral cortex emerges and gives rise to its elaborate functions remains fragmentary. To complement the description of individual cortical areas, we propose an inquiry into the significance of their spatial arrangement, asking the basic question: Why are cortical areas located where they are?Early formulations of this question date to theories from classical neuroanatomy [1,[5][6][7]. They state that the spatial layout of cortical areas is not arbitrary, but a consequence of developmental mechanisms, shaped through evolutionary selection. The location of an area among its neighbors thus provides insight into its microstructural characteristics [6], its connections to other parts of the brain [7], and eventually its position in global processing hierarchies [8]. Consider, for example, the well-researched visual system of the macaque monkey [9,10]. Along the visual hierarchy, low-level visual features are increasingly abstracted and integrated with information from other systems. Traditionally, areas are ordered based on their degree of microstructural differentiation, and the classification of their connections as feedforward or feedback [11]. The framework we advocate emphasizes that an area's position in the visual processing hierarchy -and thus many of its microstructural and connectional features -is strongly related to its distance from the primary visual area [12,13].More generally, we propose that the spatial arrangement of areas along a global gradient between sensorimotor and transmodal regions is a key feature of human cortical organization. A gradient is an axis of variance in cortical features, along which areas fall in a spatially continuous order. Areas that resemble each other with respect to the feature of interest occupy similar positions along the gradient. As we will point out, there is a strong relationship between the similarity of two areas -that is, their relative position along the gradient -and their relative position along the cortical surface. This important role of cortical location pro...

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Section: A Temporal Hierarchy Links Structural Gradients and Functionmentioning

confidence: 99%

Large-Scale Gradients in Human Cortical Organization

Huntenburg

Bazin

Margulies

2018

Trends in Cognitive Sciences

723

738

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“…4B). As an area's TRW size was found to be related to intrinsically slower cortical dynamics in these areas (7,8), we also calculated the proportion of low-frequency power during a resting state scan. We found that voxels with a larger neural difference between stories also had a greater proportion of low-frequency power (r = 0.246, P < 0.001) (Fig.…”

Section: Figmentioning

confidence: 99%

“…Areas at the top of the processing hierarchy, including the temporal parietal junction (TPJ), angular gyrus, and posterior and frontal medial cortices, have long TRWs (many seconds to minutes) that are sufficient to integrate information at the paragraph and narrative levels (2, 5-7). The ability of an area to integrate information over time may be related to its intrinsic cortical dynamics: Long-TRW areas typically have slower neural dynamic than short-TRW areas (7,8).Long-TRW areas, which were identified with coherent narratives spanning many minutes, overlap with the semantic system, which plays an important role in complex information integration (9, 10). It was previously suggested that this network of areas supports multimodal conceptual representation by integrating information from lower level modality-specific areas (11-15).…”

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confidence: 99%

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confidence: 99%

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Amplification of local changes along the timescale processing hierarchy

Yeshurun

Nguyen

Hasson

2017

Proc. Natl. Acad. Sci. U.S.A.

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Small changes in word choice can lead to dramatically different interpretations of narratives. How does the brain accumulate and integrate such local changes to construct unique neural representations for different stories? In this study, we created two distinct narratives by changing only a few words in each sentence (e.g., "he" to "she" or "sobbing" to "laughing") while preserving the grammatical structure across stories. We then measured changes in neural responses between the two stories. We found that differences in neural responses between the two stories gradually increased along the hierarchy of processing timescales. For areas with short integration windows, such as early auditory cortex, the differences in neural responses between the two stories were relatively small. In contrast, in areas with the longest integration windows at the top of the hierarchy, such as the precuneus, temporal parietal junction, and medial frontal cortices, there were large differences in neural responses between stories. Furthermore, this gradual increase in neural differences between the stories was highly correlated with an area's ability to integrate information over time. Amplification of neural differences did not occur when changes in words did not alter the interpretation of the story (e.g., sobbing to "crying"). Our results demonstrate how subtle differences in words are gradually accumulated and amplified along the cortical hierarchy as the brain constructs a narrative over time.timescale | amplification | fMRI | narrative | hierarchy S tories unfold over many minutes and are organized into temporarily nested structures: Paragraphs are made of sentences, which are made of words, which are made of phonemes. Understanding a story therefore requires processing the story at multiple timescales, starting with the integration of phonemes to words within a relatively short temporal window, to the integration of words to sentences across a longer temporal window of a few seconds, and up to the integration of sentences and paragraphs into a coherent narrative over many minutes. It was recently suggested that the temporally nested structure of language is processed hierarchically along the cortical surface (1-4). A consequence of the hierarchical structure of language is that small changes in word choice can give rise to large differences in sentence and overall narrative interpretation. Here, we propose that local momentary changes in linguistic input in the context of a narrative (e.g., "he" to "she" or "sobbing" to "laughing") are accumulated and amplified during the processing of linguistic content across the cortex. Specifically, we hypothesize that as areas in the brain increase in their ability to integrate information over time (e.g., processing "word" level content vs. "sentence" or "narrative" level content), the neural response to small word changes will become increasingly divergent.Previously, we defined a temporal receptive window (TRW) as the length of time during which prior information from an ongoing stimulu...

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“…22 Other evidence indicates that brain dynamics are governed by a hierarchy of intrinsic timescales across regions, from slowly varying prefrontal areas high in the anatomical hierarchy 23 (that are thought to accumulate information over longer durations), to the relatively rapid dynamics of sensory regions low in the hierarchy. [24][25][26][27][28][29] This hierarchical organization of timescales across the brain may facilitate the processing of (and predictions about) the diverse timescales of stimuli in the world around us. Computational modeling has begun to shed light on the role of connectivity in shaping this interregional heterogeneity in characteristic timescales, 11 including the emergence of slower dynamics in densely connected, high-degree brain network hubs in identical, connectome-coupled neural mass models.…”

Section: Introductionmentioning

confidence: 99%

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Sethi

Zerbi

Wenderoth

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag Nervous systems are complex networks with a topology governed by the pattern of axonal connections linking distinct neural elements. Highly connected and topologically central elements are thought to play an important role in meditating the flow of information across different parts of the system. However, it is unclear how the intrinsic dynamics of a given neuronal population relates to the pattern of connections that population shares with other network nodes. In this work, we show that there is a strong and robust correlation between the structural connectivity properties of a brain region and its blood-oxygenation-level-dependent (BOLD) signal dynamics, as measured with resting-state fMRI (rs-fMRI) in the mouse. The strongest relationship is found with the total weight of incoming connections to a brain region, or weighted in-degree, which is associated with longer dynamical timescales of rs-fMRI dynamics. Our findings indicate that structural connection weights convey important information about neural activity, and that the aggregate strength of incoming projections to a brain region is closely related to its BOLD signal dynamics.

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A place for time: the spatiotemporal structure of neural dynamics during natural audition

Cited by 139 publications

References 39 publications

Large-Scale Gradients in Human Cortical Organization

Large-Scale Gradients in Human Cortical Organization

Amplification of local changes along the timescale processing hierarchy

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

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