scholarly journals Negative Functional Connectivity and Its Dependence on the Shortest Path Length of Positive Network in the Resting-State Human Brain

2011 ◽  
Vol 1 (3) ◽  
pp. 195-206 ◽  
Author(s):  
Guangyu Chen ◽  
Gang Chen ◽  
Chunming Xie ◽  
Shi-Jiang Li
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Shortest Path ◽  
Resting State ◽  
Path Length

scholarly journals Predicting MEG resting-state functional connectivity from microstructural information

2021 ◽  
pp. 1-42
Author(s):  
Eirini Messaritaki ◽  
Sonya Foley ◽  
Simona Schiavi ◽  
Lorenzo Magazzini ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Shortest Path ◽  
Resting State ◽  
Path Length ◽  
Structural Connectivity ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Brain Microstructure ◽  
Functional Mechanisms

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries in the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

scholarly journals Predicting MEG resting-state functional connectivity using microstructural information

2020 ◽  
Author(s):  
Eirini Messaritaki ◽  
Sonya Foley ◽  
Simona Schiavi ◽  
Lorenzo Magazzini ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Shortest Path ◽  
Resting State ◽  
Path Length ◽  
Structural Connectivity ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Brain Microstructure ◽  
Structural Metrics

AbstractUnderstanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries at four frequency bands: delta (1 − 4 Hz), theta (3 − 8 Hz), alpha (8 − 13 Hz) and beta (13 − 30 Hz). Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant.The microstructurally-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

scholarly journals Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 128 ◽  
Author(s):  
Aline Viol ◽  
Fernanda Palhano-Fontes ◽  
Heloisa Onias ◽  
Draulio de Araujo ◽  
Philipp Hövel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Structural Information ◽  
Brain Connectivity ◽  
Functional Brain ◽  
States Of Consciousness ◽  
Functional Brain Networks ◽  
Empirical Networks ◽  
Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

scholarly journals Resting-state functional connectivity in the human brain revealed with diffuse optical tomography

NeuroImage ◽  
2009 ◽  
Vol 47 (1) ◽  
pp. 148-156 ◽  
Author(s):  
Brian R. White ◽  
Abraham Z. Snyder ◽  
Alexander L. Cohen ◽  
Steven E. Petersen ◽  
Marcus E. Raichle ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Resting State Functional Connectivity

scholarly journals Frontal cortical functional connectivity is impacted by anaesthesia in macaques

2021 ◽  
Author(s):  
Camille Giacometti ◽  
Audrey Dureux ◽  
Delphine Autran-Clavagnier ◽  
Charles C. R. E. Wilson ◽  
Jerome Sallet ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Critical Aspect ◽  
Macaque Monkeys ◽  
Cortical Areas ◽  
Correlation Strength ◽  
The Impact

A critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional Magnetic Resonance Imaging (rs-fMRI) in anaesthetized non-human primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity (FC) organization of a well characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques revealed a similar FC pattern to that previously uncovered in the human brain. Rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas while caudal seeds in the cingulate sulcus displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrates that the FC pattern of cingulo-frontal cortical networks is preserved from macaque to human but some of its properties can only be observed in the awake state, warranting significant caution when comparing FC patterns across species under different states.

scholarly journals The Role of the Human Brain Neuron-Glia-Synaptic Composition in Forming Resting State Functional Connectivity Networks

2021 ◽  
Author(s):  
Sayan Kahali ◽  
Marcus E Raichle ◽  
Dmitriy A Yablonskiy
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Functional Networks ◽  
Bold Signal ◽  
Neuronal Density ◽  
Functional Relationships ◽  
Wide Range ◽  
Functional Units ◽  
The Brain

While significant progress has been achieved in studying resting state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain's cellular constituents remain open. In this paper we use quantitative Gradient Recalled Echo (qGRE) MRI for in vivo quantitative mapping of human brain cellular composition, and BOLD (blood oxygen level dependent) MRI resting state data from the Human Connectome Project to explore how the brain cellular constituents relate to resting state functional networks. Our results show that the BOLD-signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the strength of the functional connectivity between functional units is influenced not only by the neuronal but also glia and synaptic components of brain tissue cellular constituents. Data show that these cellular-functional relationships are most evident in the infra-slow frequency range (0.01-0.16 Hz) of brain activity, which is known to be linked with fluctuations of the BOLD signal. These mechanisms lead to a rather broad distribution of resting state functional network properties. We found that visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of BOLD signal in individual functional units, as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but a remarkably balanced cellular content enabling DMN prominent role in the overall organization of the brain and the hierarchy of functional networks in health and disease.

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