The default mode network (DMN) is a set of widely distributed brain regions in the parietal, temporal and frontal cortex. These regions often show reductions in activity during attention-demanding ...tasks but increase their activity across multiple forms of complex cognition, many of which are linked to memory or abstract thought. Within the cortex, the DMN has been shown to be located in regions furthest away from those contributing to sensory and motor systems. Here, we consider how our knowledge of the topographic characteristics of the DMN can be leveraged to better understand how this network contributes to cognition and behaviour.
Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra ...incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain-behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.
Along with the increased interest in and volume of social cognition research, there has been higher awareness of a lack of agreement on the concepts and taxonomy used to study social processes. Two ...central concepts in the field, empathy and Theory of Mind (ToM), have been identified as overlapping umbrella terms for different processes of limited convergence. Here, we review and integrate evidence of brain activation, brain organization, and behavior into a coherent model of social-cognitive processes. We start with a meta-analytic clustering of neuroimaging data across different social-cognitive tasks. Results show that understanding others' mental states can be described by a multilevel model of hierarchical structure, similar to models in intelligence and personality research. A higher level describes more broad and abstract classes of functioning, whereas a lower one explains how functions are applied to concrete contexts given by particular stimulus and task formats. Specifically, the higher level of our model suggests 3 groups of neurocognitive processes: (a) predominantly cognitive processes, which are engaged when mentalizing requires self-generated cognition decoupled from the physical world; (b) more affective processes, which are engaged when we witness emotions in others based on shared emotional, motor, and somatosensory representations; (c) combined processes, which engage cognitive and affective functions in parallel. We discuss how these processes are explained by an underlying principal gradient of structural brain organization. Finally, we validate the model by a review of empathy and ToM task interrelations found in behavioral studies.
Public Significance Statement
Empathy and Theory of Mind are important human capacities for understanding others. Here, we present a meta-analysis of neuroimaging data from 4,207 participants, which shows that these abilities can be deconstructed into specific and partially shared neurocognitive subprocesses. Our findings provide systematic, large-scale support for the hypothesis that understanding others' mental states can be described by a multilevel model of hierarchical structure, similar to models in intelligence and personality research.
Networks of widely distributed regions populate human association cortex. One network, often called the default network, is positioned at the apex of a gradient of sequential networks that radiate ...outward from primary cortex. Here, extensive anatomical data made available through the Marmoset Brain Architecture Project are explored to show a homologue exists in marmoset. Results reveal that a gradient of networks extend outward from primary cortex to progressively higher-order transmodal association cortex in both frontal and temporal cortex. The apex transmodal network comprises frontopolar and rostral temporal association cortex, parahippocampal areas TH / TF, the ventral posterior midline, and lateral parietal association cortex. The positioning of this network in the gradient and its composition of areas make it a candidate homologue to the human default network. That the marmoset, a physiologically- and genetically-accessible primate, might possess a default-network-like candidate creates opportunities for study of higher cognitive and social functions.
Large-Scale Gradients in Human Cortical Organization Huntenburg, Julia M.; Bazin, Pierre-Louis; Margulies, Daniel S.
Trends in cognitive sciences,
January 2018, 2018-01-00, 20180101, 2018-01, Letnik:
22, Številka:
1
Journal Article
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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.
Advances in neuroimaging technologies and analytics have enabled the discovery of gradients in microstructure, connectivity, gene expression, and function in the human cerebral cortex.
The notion that functional processing hierarchies are confined to sensorimotor systems is challenged by recent descriptions of global hierarchies, extending throughout transmodal association areas.
An innovative line of research has uncovered a cortical hierarchy in the temporal domain that accounts for spatially distributed functional specialization.
When not engaged in the moment, we often spontaneously represent people, places and events that are not present in the environment. Although this capacity has been linked to the default mode network ...(DMN), it remains unclear how interactions between the nodes of this network give rise to particular mental experiences during spontaneous thought. One hypothesis is that the core of the DMN integrates information from medial and lateral temporal lobe memory systems, which represent different aspects of knowledge. Individual differences in the connectivity between temporal lobe regions and the default mode network core would then predict differences in the content and form of people's spontaneous thoughts. This study tested this hypothesis by examining the relationship between seed-based functional connectivity and the contents of spontaneous thought recorded in a laboratory study several days later. Variations in connectivity from both medial and lateral temporal lobe regions was associated with different patterns of spontaneous thought and these effects converged on an overlapping region in the posterior cingulate cortex. We propose that the posterior core of the DMN acts as a representational hub that integrates information represented in medial and lateral temporal lobe and this process is important in determining the content and form of spontaneous thought.
Regions of transmodal cortex, in particular the default mode network (DMN), have historically been argued to serve functions unrelated to task performance, in part because of associations with ...naturally occurring periods of off-task thought. In contrast, contemporary views of the DMN suggest it plays an integrative role in cognition that emerges from its location at the top of a cortical hierarchy and its relative isolation from systems directly involved in perception and action. The combination of these topographical features may allow the DMN to support abstract representations derived from lower levels in the hierarchy and so reflect the broader cognitive landscape. To investigate these contrasting views of DMN function, we sampled experience as participants performed tasks varying in their working-memory load while inside an fMRI scanner. We used self-report data to establish dimensions of thought that describe levels of detail, the relationship to a task, the modality of thought, and its emotional qualities. We used representational similarity analysis to examine correspondences between patterns of neural activity and each dimension of thought. Our results were inconsistent with a task-negative view of DMN function. Distinctions between on- and off-task thought were associated with patterns of consistent neural activity in regions adjacent to unimodal cortex, including motor and premotor cortex. Detail in ongoing thought was associated with patterns of activity within the DMN during periods of working-memory maintenance. These results demonstrate a contribution of the DMN to ongoing cognition extending beyond task-unrelated processing that can include detailed experiences occurring under active task conditions.
Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an ...alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular "betweenness centrality" - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.
While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While ...nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses, which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive magnetic resonance imaging (MRI) data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta-analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition, such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.
Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, ...nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a connectivity gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default mode network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. We used a semantic judgment task, which parametrically varied the global semantic similarity of items within a feature matching task to create a ‘task gradient’, from conceptual combinations that were highly overlapping in long-term memory to trials that only shared the goal-relevant feature. We found the brain’s response to the task gradient varied systematically along the connectivity gradient, with the strongest response in default mode network when the probe and target items were highly overlapping conceptually. This graded functional change was seen in multiple brain regions and within individual brains, and was not readily explained by task difficulty. Moreover, the gradient captured the spatial layout of networks involved in semantic processing, providing an organizational principle for controlled semantic cognition across the cortex. In this way, the cortex is organized to support semantic decision-making in both highly familiar and less familiar situations.
•Neural response to semantic similarity varied along principal gradient connectivity.•Default network showed strongest response when input overlapped with long-term memory.•This graded functional change was seen in multiple brain regions.•This graded functional change was not readily explained by task difficulty.•The gradient captured the spatial layout of networks involved in semantic processing.
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