Dissociation between the Language Network and the Multiple Demand (MD) Network, and Their Functional Roles in Language

2020 ◽  
Author(s):  
Weixi Kang ◽  
Sònia Pineda Hernández ◽  
Afshin Azadikhah
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Functional Roles ◽  
Structural And Functional Properties ◽  
Language Recovery ◽  
Core Language ◽  
Large Scale Networks ◽  
Network Studies ◽  
Parietal Cortices ◽  
Language Network

Language operations rely on multiple mental activities that are supported by the frontal, temporal, and parietal cortices. Moreover, these cortices are not organized into individual isolated previous but rather consist of multiple large-scale networks: sets of brain regions that share structural and functional properties. Literature generally agrees with two well documented systems in the human brain, which are the language network, and the multiple demand (MD) network. Studies have reported both the participation of language network (e.g., Fedorenko et al., 2010, Vagharchakian et al., 2012; Fedorenko et al., 2016; Scott et al., 2017; Deniz et al., 2019) and MD network in language (e.g., Kuperberg et al., 2003; Rodd et al., 2005, Novais-Santos et al., 2007; January et al., 2009; Peelle et al., 2010; Nieuwland et al., 2012; McMillan et al., 2013), but their possible role in language is debated. In this paper, we review how the language-specific and domain-general MD systems support different aspects of cognition and how they can be dissociated from one another. We argue that core language operations are supported by the language network rather than the MD network, and the MD network does not contribute directly to language recovery after stroke, but plays a role in the recovery of other cognitive functions that are engaged in the same language task.

scholarly journals Dynamic connectivity analyses of intracranial EEG during recognition memory reveal various large-scale functional networks

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Jaroslav Hlinka ◽  
Elodie Despouy ◽  
Luc Valton ◽  
Marie Denuelle ◽  
...  
Keyword(s):  
Recognition Memory ◽  
Large Scale ◽  
Visual Recognition ◽  
Effective Connectivity ◽  
Memory Task ◽  
Brain Regions ◽  
Neural Basis ◽  
Intracranial Eeg ◽  
Large Scale Networks ◽  
The Right

Recognition memory is the ability to recognize previously encountered events, objects, or people. It is characterized by its robustness and rapidness. Even this relatively simple ability requires the coordinated activity of a surprisingly large number of brain regions. These spatially distributed, but functionally linked regions are interconnected into large-scale networks. Understanding memory requires an examination of the involvement of these networks and the interactions between different regions while memory processes unfold. However, little is known about the dynamical organization of large-scale networks during the early phases of recognition memory. We recorded intracranial EEG, which affords high temporal and spatial resolution, while epileptic subjects performed a visual recognition memory task. We analyzed dynamic functional and effective connectivity as well as network properties. Various networks were identified, each with its specific characteristics regarding information flow (feedforward or feedback), dynamics, topology, and stability. The first network mainly involved the right visual ventral stream and bilateral frontal regions. It was characterized by early predominant feedforward activity, modular topology, and high stability. It was followed by the involvement of a second network, mainly in the left hemisphere, but notably also involving the right hippocampus, characterized by later feedback activity, integrated topology, and lower stability. The transition between networks was associated with a change in network topology. Overall, these results confirm that several large-scale brain networks, each with specific properties and temporal manifestation, are involved during recognition memory. Ultimately, understanding how the brain dynamically faces rapid changes in cognitive demand is vital to our comprehension of the neural basis of cognition.

scholarly journals The role of anatomical connection strength for interareal communication in macaque cortex

2020 ◽  
Author(s):  
Julien Vezoli ◽  
Martin Vinck ◽  
Conrado A. Bosman ◽  
Andre M. Bastos ◽  
Christopher M Lewis ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
Connection Strength ◽  
Non Invasive ◽  
Large Scale Networks ◽  
Invasive Techniques ◽  
The Relationship ◽  
Anatomical Connection

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

scholarly journals Functional Specialization in the Attention Network

2020 ◽  
Vol 71 (1) ◽  
pp. 221-249 ◽  
Author(s):  
Ian C. Fiebelkorn ◽  
Sabine Kastner
Keyword(s):  
Spatial Attention ◽  
Cognitive Flexibility ◽  
Sensory Processing ◽  
Dynamic Environments ◽  
Brain Regions ◽  
Neural Mechanisms ◽  
Functional Specialization ◽  
Attention Network ◽  
Functional Roles ◽  
Parietal Cortices

Spatial attention is comprised of neural mechanisms that boost sensory processing at a behaviorally relevant location while filtering out competing information. The present review examines functional specialization in the network of brain regions that directs such preferential processing. This attention network includes both cortical (e.g., frontal and parietal cortices) and subcortical (e.g., the superior colliculus and the pulvinar nucleus of the thalamus) structures. Here, we piece together existing evidence that these various nodes of the attention network have dissociable functional roles by synthesizing results from electrophysiology and neuroimaging studies. We describe functional specialization across several dimensions (e.g., at different processing stages and within different behavioral contexts), while focusing on spatial attention as a dynamic process that unfolds over time. Functional contributions from each node of the attention network can change on a moment-to-moment timescale, providing the necessary cognitive flexibility for sampling from highly dynamic environments.

scholarly journals Intrinsic Architecture Underlying the Relations among the Default, Dorsal Attention, and Frontoparietal Control Networks of the Human Brain

2013 ◽  
Vol 25 (1) ◽  
pp. 74-86 ◽  
Author(s):  
R. Nathan Spreng ◽  
Jorge Sepulcre ◽  
Gary R. Turner ◽  
W. Dale Stevens ◽  
Daniel L. Schacter
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Dynamic Balance ◽  
Brain Regions ◽  
Human Cognition ◽  
Control Network ◽  
Resting State Functional Connectivity ◽  
Attention Networks ◽  
Large Scale Networks ◽  
Frontoparietal Control Network

Human cognition is increasingly characterized as an emergent property of interactions among distributed, functionally specialized brain networks. We recently demonstrated that the antagonistic “default” and “dorsal attention” networks—subserving internally and externally directed cognition, respectively—are modulated by a third “frontoparietal control” network that flexibly couples with either network depending on task domain. However, little is known about the intrinsic functional architecture underlying this relationship. We used graph theory to analyze network properties of intrinsic functional connectivity within and between these three large-scale networks. Task-based activation from three independent studies were used to identify reliable brain regions (“nodes”) of each network. We then examined pairwise connections (“edges”) between nodes, as defined by resting-state functional connectivity MRI. Importantly, we used a novel bootstrap resampling procedure to determine the reliability of graph edges. Furthermore, we examined both full and partial correlations. As predicted, there was a higher degree of integration within each network than between networks. Critically, whereas the default and dorsal attention networks shared little positive connectivity with one another, the frontoparietal control network showed a high degree of between-network interconnectivity with each of these networks. Furthermore, we identified nodes within the frontoparietal control network of three different types—default-aligned, dorsal attention-aligned, and dual-aligned—that we propose play dissociable roles in mediating internetwork communication. The results provide evidence consistent with the idea that the frontoparietal control network plays a pivotal gate-keeping role in goal-directed cognition, mediating the dynamic balance between default and dorsal attention networks.

scholarly journals Hierarchy and levels: analysing networks to study mechanisms in molecular biology

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190320 ◽  
Author(s):  
William Bechtel
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Biological Knowledge ◽  
Theme Issue ◽  
Epistatic Interactions ◽  
Philosophical Foundations ◽  
High Throughput Data ◽  
Cell Components ◽  
Large Scale Networks ◽  
Network Studies

Network representations are flat while mechanisms are organized into a hierarchy of levels, suggesting that the two are fundamentally opposed. I challenge this opposition by focusing on two aspects of the ways in which large-scale networks constructed from high-throughput data are analysed in systems biology: identifying clusters of nodes that operate as modules or mechanisms and using bio-ontologies such as gene ontology (GO) to annotate nodes with information about where entities appear in cells and the biological functions in which they participate. Of particular importance, GO organizes biological knowledge about cell components and functions hierarchically. I illustrate how this supports mechanistic interpretation of networks with two examples of network studies, one using epistatic interactions among genes to identify mechanisms and their parts and the other using deep learning to predict phenotypes. As illustrated in these examples, when network research draws upon hierarchical information such as provided by GO, the results not only can be interpreted mechanistically but provide new mechanistic knowledge. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

AbstractAccording to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals High-level language brain regions are sensitive to sub-lexical regularities

2021 ◽  
Author(s):  
Tamar I Regev ◽  
Josef Affourtit ◽  
Xuanyi Chen ◽  
Abigail E Schipper ◽  
Leon Bergen ◽  
...  
Keyword(s):  
Language Processing ◽  
Sentence Comprehension ◽  
Brain Regions ◽  
Linguistic Knowledge ◽  
High Level Language ◽  
Word Meanings ◽  
Sound Patterns ◽  
Core Language ◽  
High Level ◽  
Language Network

A network of left frontal and temporal brain regions supports 'high-level' language processing-including the processing of word meanings, as well as word-combinatorial processing-across presentation modalities. This 'core' language network has been argued to store our knowledge of words and constructions as well as constraints on how those combine to form sentences. However, our linguistic knowledge additionally includes information about sounds (phonemes) and how they combine to form clusters, syllables, and words. Is this knowledge of phoneme combinatorics also represented in these language regions? Across five fMRI experiments, we investigated the sensitivity of high-level language processing brain regions to sub-lexical linguistic sound patterns by examining responses to diverse nonwords-sequences of sounds/letters that do not constitute real words (e.g., punes, silory, flope). We establish robust responses in the language network to visually (Experiment 1a, n=605) and auditorily (Experiments 1b, n=12, and 1c, n=13) presented nonwords relative to baseline. In Experiment 2 (n=16), we find stronger responses to nonwords that obey the phoneme-combinatorial constraints of English. Finally, in Experiment 3 (n=14) and a post-hoc analysis of Experiment 2, we provide suggestive evidence that the responses in Experiments 1 and 2 are not due to the activation of real words that share some phonology with the nonwords. The results suggest that knowledge of phoneme combinatorics and representations of sub-lexical linguistic sound patterns are stored within the same fronto-temporal network that stores higher-level linguistic knowledge and supports word and sentence comprehension.

scholarly journals Disentangling Predictive Processing in the Brain: A Meta-analytic Study in Favour of a Predictive Network

2021 ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signal. Despite extensive research has investigated the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a task-based meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals Mouse brain-wide mitochondrial connectivity anchored in gene, brain and behavior

2021 ◽  
Author(s):  
Ayelet Rosenberg ◽  
Manish Saggar ◽  
Peter Rogu ◽  
Aaron W. Limoges ◽  
Carmen Sandi ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Production Capacity ◽  
Mitochondrial Respiratory Chain ◽  
Brain Regions ◽  
Wide Scale ◽  
Brain And Behavior ◽  
Large Scale Networks ◽  
And Behavior ◽  
Multimodal Evidence

AbstractThe brain and behavior are under energetic constraints, which are likely driven by mitochondrial energy production capacity. However, the mitochondria-behavior relationship has not been systematically studied on a brain-wide scale. Here we examine the association between mitochondrial health index and stress-related behaviors in mice with diverse mitochondrial and behavioral phenotypes. Miniaturized assays of mitochondrial respiratory chain function and mitochondrial DNA (mtDNA) content were deployed on 571 samples from 17 brain regions. We find specific patterns of mito-behavior associations that vary across brain regions and behaviors. Furthermore, multi-slice network analysis applied to our brain-wide mitochondrial dataset identified three large-scale networks of brain regions. A major network composed of cortico-striatal regions exhibits highest mitochondria-behavior correlations, suggesting that this mito-based network is functionally significant. Mito-based networks can also be recapitulated using correlated gene expression and structural connectome data, thereby providing convergent multimodal evidence of mitochondrial functional organization anchored in gene, brain and behavior.

scholarly journals Measuring and Modeling Nonlinear Interactions Between Brain Regions with fMRI

2016 ◽  
Author(s):  
Stefano Anzellotti ◽  
Evelina Fedorenko ◽  
Alexander J E Kell ◽  
Alfonso Caramazza ◽  
Rebecca Saxe
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Brain Regions ◽  
Nonlinear Dependence ◽  
Statistical Dependence ◽  
Nonlinear Interactions ◽  
Analysis Framework ◽  
Standard Assumption ◽  
Neural Computations ◽  
Large Scale Networks

AbstractIn the study of connectivity in large-scale networks of brain regions, a standard assumption is made that the statistical dependence between regions is univariate and linear. However, brain regions encode information in multivariate responses, and neural computations are nonlinear. Multivariate and nonlinear statistical dependence between regions is likely ubiquitous, but it is not captured by current methods. To fill this gap, we introduce a novel analysis framework: fMRI responses are characterized as points in multidimensional spaces, and nonlinear dependence is modeled using artificial neural networks. Converging evidence from multiple experiments shows that nonlinear dependence 1) models mappings between brain regions more accurately than linear dependence, explaining more variance in left-out data; 2) reveals functional subdivisions within cortical networks, and 3) is modulated by the task participants are performing.

Sign in / Sign up

Export Citation Format

Share Document