scholarly journals A Domain-general Cognitive Core defined in Multimodally Parcellated Human Cortex

2019 ◽  
Author(s):  
Moataz Assem ◽  
Matthew F. Glasser ◽  
David C. Van Essen ◽  
John Duncan
Keyword(s):  
Resting State ◽  
Spatial Localization ◽  
Functional Differentiation ◽  
Resting State Networks ◽  
Activation Pattern ◽  
Imaging Studies ◽  
Human Cortex ◽  
Mri Features ◽  
Cognitive Operations ◽  
Human Connectome Project

AbstractNumerous brain imaging studies identified a domain-general or “multiple-demand” (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with cortex of each individual parcellated using neurobiologically grounded multi-modal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.

scholarly journals A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex

2020 ◽  
Vol 30 (8) ◽  
pp. 4361-4380 ◽  
Author(s):  
Moataz Assem ◽  
Matthew F Glasser ◽  
David C Van Essen ◽  
John Duncan
Keyword(s):  
Spatial Localization ◽  
Functional Differentiation ◽  
Resting State Networks ◽  
Activation Pattern ◽  
Imaging Studies ◽  
Human Cortex ◽  
Mri Features ◽  
Cognitive Operations ◽  
Human Connectome Project ◽  
Multimodal Mri

Abstract Numerous brain imaging studies identified a domain-general or “multiple-demand” (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation, and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with the cortex of each individual parcellated using neurobiologically grounded multimodal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting-state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.

scholarly journals Neuronal dynamics enable the functional differentiation of resting state networks in the human brain

2018 ◽  
Vol 40 (5) ◽  
pp. 1445-1457 ◽  
Author(s):  
Marco Marino ◽  
Quanying Liu ◽  
Jessica Samogin ◽  
Franca Tecchio ◽  
Carlo Cottone ◽  
...  
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Functional Differentiation ◽  
Resting State Networks ◽  
Neuronal Dynamics

scholarly journals Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Lan Yang ◽  
Jing Wei ◽  
Ying Li ◽  
Bin Wang ◽  
Hao Guo ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Functional Magnetic Resonance ◽  
Intra Class Correlation ◽  
Retest Reliability ◽  
Brain Signals ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
The Brain

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

scholarly journals Medial Temporal Lobe Contributions to Resting-State Networks

2021 ◽  
Author(s):  
Sara Seoane ◽  
Cristián Modroño ◽  
José Luis González Mora ◽  
Niels Janssen
Keyword(s):  
Temporal Lobe ◽  
Resting State ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Resting State Networks ◽  
Attention Networks ◽  
Default Mode ◽  
Parahippocampal Cortex ◽  
Human Connectome Project ◽  
Dorsal Attention Network

Abstract The medial temporal lobe (MTL) is a set of interconnected brain regions that have been shown to play a central role in behavior as well as in neurological disease. Recent studies using resting-state functional Magnetic Resonance Imaging (rsfMRI) have attempted to understand the MTL in terms of its functional connectivity with the rest of the brain. However, the exact characterization of the whole-brain networks that co-activate with the MTL as well as how the various sub-regions of the MTL are associated with these networks remains poorly understood. Here we attempted to advance these issues by exploiting the high spatial-resolution 7T rsfMRI dataset from the Human Connectome Project with a data-driven analysis approach that relied on Independent Component Analysis (ICA) restricted to the MTL. We found that four different well-known resting-state networks co-activated with a unique configuration of MTL subcomponents. Specifically, we found that different sections of the parahippocampal cortex were involved in the default mode, visual and dorsal attention networks, sections of the hippocampus in the somatomotor and default mode networks, and the lateral entorhinal cortex in the dorsal attention network. We replicated this set of results in a validation sample. These results provide new insight into how the MTL and its subcomponents contribute to known resting-state networks. The participation of the MTL in an expanded range of resting-state networks requires a rethink of its presumed role in behavior and disease.

scholarly journals Human resting-state electrophysiological networks in the alpha frequency band: Evidence from magnetoencephalographic source imaging

2017 ◽  
Author(s):  
R. Hindriks ◽  
C. Micheli ◽  
D. Mantini ◽  
G. Deco
Keyword(s):  
Frequency Band ◽  
Resting State ◽  
Functional Networks ◽  
Alpha Power ◽  
Source Imaging ◽  
Data Set ◽  
Human Cortex ◽  
Alpha Frequency ◽  
Alpha Frequency Band ◽  
Human Connectome Project

AbstractIn the resting-state, extended regions of the human cortex engage in electrical oscillations within the alpha-frequency band (7–14 Hz) that can be measured outside the head by magnetoencephalography (MEG). Given the accumulating evidence that alpha oscillations play a fundamental role in attentional processing and working memory, it becomes increasingly important to characterize their cortical organization. Event-related studies have demonstrated that attentional allocation can modulate alpha power selectively within the visual, auditory, and somatosensory cortices, as well as in higher-level regions. Such studies demonstrate the existence of multiple generators by exploiting experimental contrasts and trial-averaging. The identification of alpha generators from resting-state data alone has proven much harder and, consequently, relatively little is known about their organization: Apart from the classical visual, somatosensory, and auditory rhythms, it is unclear how many more generators can be observed with MEG and how they are organized into functional networks. Such knowledge, however, possibly enables to delineate separate cognitive, perceptual, and motor processes that co-occur in the resting-state and is therefore important. In this study we use the resting-state MEG data-set provided by the Human Connectome Project to identify cortical alpha generators and to characterize their organization into functional networks. The large number of subjects (N = 94), multiple scans per subject, and state-of-the-art surface-based cortical registration enable a detailed characterization of alpha in human cortex. By applying non-negative matrix factorization to source-projected power fluctuations, we identify 16 reliable cortical generators in each hemisphere. These include the classical sensory alpha rhythms as well as several additional ones in the lateral occipital and temporal lobes and in inferior parietal cortex. We show that the generators are coordinated across hemispheres and hence form resting-state networks (RSNs), two of which are the default mode network (DMN) and the ventral attention network (VAN). Our study hence provides a further subdivision of RSNs within the alpha frequency band and shows that these RSNs are supported by alpha generators. As such, it links the classical literature on human alpha with more recent research into electrophysiological RNSs.

scholarly journals Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

2018 ◽  
Author(s):  
James M. Kunert-Graf ◽  
Kristian M. Eschenburg ◽  
David J. Galas ◽  
J. Nathan Kutz ◽  
Swati D. Rane ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Regions ◽  
Dynamic Mode Decomposition ◽  
Single Subject ◽  
Dynamic Mode ◽  
Resting State Networks ◽  
Time Resolved ◽  
Mode Decomposition ◽  
Human Connectome Project

AbstractResting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5–15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

scholarly journals Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Alba Xifra-Porxas ◽  
Michalis Kassinopoulos ◽  
Georgios D Mitsis
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Large Data ◽  
Time Varying ◽  
Resting State Networks ◽  
Human Connectome Project ◽  
Noninvasive Biomarker ◽  
Recurrent Patterns

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

scholarly journals Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

2022 ◽  
Author(s):  
Victor Nozais ◽  
Stephanie J Forkel ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten ◽  
marc joliot
Keyword(s):  
White Matter ◽  
Resting State ◽  
Grey Matter ◽  
Functional Neuroimaging ◽  
White Matter Lesions ◽  
Blood Oxygen Level Dependent ◽  
Resting State Networks ◽  
Multiple Networks ◽  
Human Connectome Project ◽  
The Impact

Over the past two decades, the study of resting-state functional magnetic resonance imaging (fMRI) has revealed the existence of multiple brain areas displaying synchronous functional blood oxygen level-dependent signals (BOLD) - resting-state networks (RSNs). The variation in functional connectivity between the different areas of a resting-state network or between multiple networks, have been extensively studied and linked to cognitive states and pathologies. However, the white matter connections supporting each network remain only partially described. In this work, we developed a data-driven method to systematically map the white and grey matter contributing to resting-state networks. Using the Human Connectome Project, we generated an atlas of 30 resting-state networks, each with two maps: white matter and grey matter. By integrating structural and functional neuroimaging data, this method builds an atlas that unlocks the joint anatomical exploration of white and grey matter to resting-state networks. The method also allows highlighting the overlap between networks, which revealed that most (89%) of the brain's white matter is shared amongst multiple networks, with 16% shared by at least 7 resting-state networks. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the correlations and the communication within resting-state networks. We provide an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage on RSNs.

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