Large-scale meta-analysis suggests low regional modularity in lateral frontal cortex

Mapping Intimacies ◽

10.1101/083352 ◽

2016 ◽

Cited By ~ 1

Author(s):

Alejandro de la Vega ◽

Tal Yarkoni ◽

Tor D. Wager ◽

Marie T. Banich

Keyword(s):

Frontal Cortex ◽

Large Scale ◽

Meta Analysis ◽

Brain Networks ◽

Mental States ◽

Whole Brain ◽

Psychological States ◽

Fmri Study ◽

Data Driven Approach ◽

Modes Of Processing

AbstractExtensive fMRI study of human lateral frontal cortex (LFC) has yet to yield a consensus mapping between discrete anatomy and psychological states, partly due to the difficulty of inferring mental states in individual studies. Here, we used a data-driven approach to generate a comprehensive functional-anatomical mapping of LFC from 11,406 neuroimaging studies. We identified putatively separable LFC regions on the basis of whole-brain co-activation, revealing 14 clusters organized into three whole-brain networks. Next, we used multivariate classification to identify the psychological states that best predicted activity in each sub-region, resulting in preferential psychological profiles. We observed large functional differences between networks, suggesting brain networks support distinct modes of processing. Within each network, however, we observed low functional specificity, suggesting discrete psychological states are not modularly organized. Our results are consistent with the view that individual LFC regions work as part of highly parallel, distributed networks to give rise to flexible, adaptive behavior.

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Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01306 ◽

2018 ◽

Vol 30 (9) ◽

pp. 1209-1228 ◽

Cited By ~ 6

Author(s):

David Rothlein ◽

Joseph DeGutis ◽

Michael Esterman

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Relevant Information ◽

Mental States ◽

Attention Task ◽

Default Mode ◽

Neural Representations ◽

Similarity Structure ◽

Similarity Matrices

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

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Human Movement Responses to the Rorschach and Mirroring Activity: An fMRI Study

Assessment ◽

10.1177/1073191117731813 ◽

2017 ◽

Vol 26 (1) ◽

pp. 56-69 ◽

Cited By ~ 6

Author(s):

Luciano Giromini ◽

Donald J. Viglione ◽

Jaime A. Pineda ◽

Piero Porcelli ◽

David Hubbard ◽

...

Keyword(s):

Large Scale ◽

Mirror Neuron System ◽

Repetitive Transcranial Magnetic Stimulation ◽

Meta Analysis ◽

Mirror Neuron ◽

Region Of Interest ◽

Human Movement ◽

Brain Areas ◽

Embodied Simulation ◽

Fmri Study

It has been suggested that the Rorschach human movement (M) response could be associated with an embodied simulation mechanism mediated by the mirror neuron system (MNS). To date, evidence for this hypothesis comes from two electroencephalogram studies and one repetitive transcranial magnetic stimulation study. To provide additional data on this topic, the Rorschach was administered during fMRI to a sample of 26 healthy adult volunteers. Activity in MNS-related brain areas temporally associated with M responses was compared with such activity for other, non-M Rorschach responses. Data analyses focused on MNS regions of interest identified by Neurosynth, a web-based platform for large scale, automated meta-analysis of fMRI data. Consistent with the hypothesis that M responses involve embodied simulation and MNS activity, univariate region of interest analyses showed that production of M responses associated with significantly greater activity in MNS-related brain areas when compared with non-M Rorschach responses. This finding is consistent with the traditional interpretation of the M code.

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Erratum: de la Vega et al., “Large-Scale Meta-Analysis of Human Medial Frontal Cortex Reveals Tripartite Functional Organization”

Journal of Neuroscience ◽

10.1523/jneurosci.0471-17.2017 ◽

2017 ◽

Vol 37 (13) ◽

pp. 3735-3735 ◽

Cited By ~ 1

Keyword(s):

Frontal Cortex ◽

Large Scale ◽

Functional Organization ◽

Meta Analysis ◽

Medial Frontal Cortex

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Large-Scale Meta-Analysis of Human Medial Frontal Cortex Reveals Tripartite Functional Organization

Journal of Neuroscience ◽

10.1523/jneurosci.4402-15.2016 ◽

2016 ◽

Vol 36 (24) ◽

pp. 6553-6562 ◽

Cited By ~ 130

Author(s):

Alejandro de la Vega ◽

Luke J. Chang ◽

Marie T. Banich ◽

Tor D. Wager ◽

Tal Yarkoni

Keyword(s):

Frontal Cortex ◽

Large Scale ◽

Functional Organization ◽

Meta Analysis ◽

Medial Frontal Cortex

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Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity

Schizophrenia Bulletin ◽

10.1093/schbul/sbx034 ◽

2017 ◽

Vol 44 (1) ◽

pp. 168-181 ◽

Cited By ~ 110

Author(s):

Debo Dong ◽

Yulin Wang ◽

Xuebin Chang ◽

Cheng Luo ◽

Dezhong Yao

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Meta Analysis ◽

Brain Networks ◽

Resting State Functional Connectivity

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Large-scale Meta-analysis Suggests Low Regional Modularity in Lateral Frontal Cortex

Cerebral Cortex ◽

10.1093/cercor/bhx204 ◽

2017 ◽

Vol 28 (10) ◽

pp. 3414-3428 ◽

Cited By ~ 10

Author(s):

Alejandro de la Vega ◽

Tal Yarkoni ◽

Tor D Wager ◽

Marie T Banich

Keyword(s):

Frontal Cortex ◽

Large Scale ◽

Meta Analysis

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The Global Signal and Observed Anticorrelated Resting State Brain Networks

Journal of Neurophysiology ◽

10.1152/jn.90777.2008 ◽

2009 ◽

Vol 101 (6) ◽

pp. 3270-3283 ◽

Cited By ~ 1224

Author(s):

Michael D. Fox ◽

Dongyang Zhang ◽

Abraham Z. Snyder ◽

Marcus E. Raichle

Keyword(s):

Resting State ◽

Large Scale ◽

Brain Networks ◽

Blood Oxygen Level Dependent ◽

Great Promise ◽

Blood Oxygen Level ◽

Whole Brain ◽

Global Signal ◽

Global Regression

Resting state studies of spontaneous fluctuations in the functional MRI (fMRI) blood oxygen level dependent (BOLD) signal have shown great promise in mapping the brain's intrinsic, large-scale functional architecture. An important data preprocessing step used to enhance the quality of these observations has been removal of spontaneous BOLD fluctuations common to the whole brain (the so-called global signal). One reproducible consequence of global signal removal has been the finding that spontaneous BOLD fluctuations in the default mode network and an extended dorsal attention system are consistently anticorrelated, a relationship that these two systems exhibit during task performance. The dependence of these resting-state anticorrelations on global signal removal has raised important questions regarding the nature of the global signal, the validity of global signal removal, and the appropriate interpretation of observed anticorrelated brain networks. In this study, we investigate several properties of the global signal and find that it is, indeed, global, not residing preferentially in systems exhibiting anticorrelations. We detail the influence of global signal removal on resting state correlation maps both mathematically and empirically, showing an enhancement in detection of system-specific correlations and improvement in the correspondence between resting-state correlations and anatomy. Finally, we show that several characteristics of anticorrelated networks including their spatial distribution, cross-subject consistency, presence with modified whole brain masks, and existence before global regression are not attributable to global signal removal and therefore suggest a biological basis.

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Common brain networks underlying human social interactions: evidence from large-scale neuroimaging meta-analysis

Neuroscience & Biobehavioral Reviews ◽

10.1016/j.neubiorev.2021.03.025 ◽

2021 ◽

Author(s):

Chunliang Feng ◽

Simon B. Eickhoff ◽

Ting Li ◽

Li Wang ◽

Benjamin Becker ◽

...

Keyword(s):

Social Interactions ◽

Large Scale ◽

Meta Analysis ◽

Brain Networks

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Whole-brain estimates of directed connectivity for human connectomics

10.1101/2020.02.20.958124 ◽

2020 ◽

Cited By ~ 2

Author(s):

Stefan Frässle ◽

Zina M. Manjaly ◽

Cao T. Do ◽

Lars Kasper ◽

Klaas P. Pruessmann ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Motor Task ◽

Brain Network ◽

Brain Organization ◽

Whole Brain ◽

Functional Roles ◽

Reciprocal Connections ◽

Theoretical Analyses

ABSTRACTConnectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation.Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

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