scholarly journals Topological segregation of functional networks increases in developing brains

2018 ◽  
Author(s):  
Wei He ◽  
Paul F. Sowman ◽  
Jon Brock ◽  
Andrew C. Etchell ◽  
Cornelis J. Stam ◽  
...  
Keyword(s):  
Network Topology ◽  
Minimum Spanning Tree ◽  
Brain Networks ◽  
Developmental Trajectory ◽  
Brain Maturation ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Reconstructed Time Series ◽  
Cortical Regions

AbstractA growing literature conceptualises human brain development from a network perspective, but it remains unknown how functional brain networks are refined during the preschool years. The extant literature diverges in its characterisation of functional network development, with little agreement between haemodynamic- and electrophysiology-based measures. In children aged from 4 to 12 years, as well as adults, age appropriate magnetoencephalography was used to estimate unbiased network topology, using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series. During childhood, network topology becomes increasingly segregated, while cortical regions decrease in centrality. We propose a heuristic MST model, in which a clear developmental trajectory for the emergence of complex brain networks is delineated. Our results resolve topological reorganisation of functional networks across temporal and special scales in youth and fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years.

Deep Learning for the Automated Classification of Functional Brain Networks in fMRI (Preprint)

2021 ◽  
Author(s):  
Lukman Ismael ◽  
Pejman Rasti ◽  
Florian Bernard ◽  
Philippe Menei ◽  
Aram Ter Minassian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Brain Networks ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Deep Learning Model

BACKGROUND The functional MRI (fMRI) is an essential tool for the presurgical planning of brain tumor removal, allowing the identification of functional brain networks in order to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rsfMRI). However, this technique is not routinely used because of the necessity to have a expert reviewer to identify manually each functional networks. OBJECTIVE We aimed to automatize the detection of brain functional networks in rsfMRI data using deep learning and machine learning algorithms METHODS We used the rsfMRI data of 82 healthy patients to test the diagnostic performance of our proposed end-to-end deep learning model to the reference functional networks identified manually by 2 expert reviewers. RESULTS Experiment results show the best performance of 86% correct recognition rate obtained from the proposed deep learning architecture which shows its superiority over other machine learning algorithms that were equally tested for this classification task. CONCLUSIONS The proposed end-to-end deep learning model was the most performant machine learning algorithm. The use of this model to automatize the functional networks detection in rsfMRI may allow to broaden the use of the rsfMRI, allowing the presurgical identification of these networks and thus help to preserve the patient’s neurological status. CLINICALTRIAL Comité de protection des personnes Ouest II, decision reference CPP 2012-25)

scholarly journals Alternating dynamics of segregation and integration in human brain functional networks during working-memory task

2016 ◽  
Author(s):  
Antonio G. Zippo ◽  
Pasquale A. Della Rosa ◽  
Isabella Castiglioni ◽  
Gabriele E. M. Biella
Keyword(s):  
Working Memory ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Networks ◽  
Memory Task ◽  
Functional Networks ◽  
Second Phase ◽  
Functional Brain ◽  
Brain Functional Networks ◽  
Functional Brain Networks

AbstractBrain functional networks show high variability in short time windows but mechanisms governing these transient dynamics still remain unknown. In this work we studied the temporal evolution of functional brain networks involved in a working memory task while recording high-density electroencephalography in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation fell down and the functional integration prevailed. Notably, wrong trials were associated with different sequences of the segregation-integration profile and measures of network centrality and modularity were able to catch crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a working memory task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations.

scholarly journals Multiple Functional Brain Networks Related to Pain Perception Revealed by fMRI

2021 ◽  
Author(s):  
Matteo Damascelli ◽  
Todd S. Woodward ◽  
Nicole Sanford ◽  
Hafsa B. Zahid ◽  
Ryan Lim ◽  
...  
Keyword(s):  
Pain Perception ◽  
Brain Networks ◽  
Data Driven ◽  
Functional Networks ◽  
Brain State ◽  
Imaging Data ◽  
Whole Brain ◽  
Functional Brain ◽  
Network Activation ◽  
Functional Brain Networks

AbstractThe rise of functional magnetic resonance imaging (fMRI) has led to a deeper understanding of cortical processing of pain. Central to these advances has been the identification and analysis of “functional networks”, often derived from groups of pre-selected pain regions. In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique—Constrained Principal Component Analysis for fMRI (fMRI-CPCA)—that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activation intensities. Three functional networks emerged: a sensorimotor response network, a salience-mediated attention network, and the default-mode network. Together, these networks constituted a brain state that explained variability in pain perception, both within and between individuals, demonstrating the potential of data-driven, whole-brain functional network techniques for the analysis of pain imaging data.

scholarly journals The Human Thalamus is an Integrative Hub for Functional Brain Networks

2016 ◽  
Author(s):  
Kai Hwang ◽  
Maxwell Bertolero ◽  
William Liu ◽  
Mark D’Esposito
Keyword(s):  
Meta Analysis ◽  
Functional Brain Imaging ◽  
Modular Organization ◽  
Functional Networks ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Human Thalamus ◽  
Functional Brain Networks ◽  
Human Participants ◽  
Cortical Regions

AbstractThe thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that the human thalamus displays network properties capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate heteromodal information and maintain the modular structure of cortical functional networks.

Cortico-Cortical Evoked Potential Mapping

2018 ◽  
pp. 431-452 ◽  
Author(s):  
Riki Matsumoto ◽  
Takeharu Kunieda
Keyword(s):  
Human Brain ◽  
Cortical Excitability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Single Pulse ◽  
Functional Brain ◽  
Cortical Connections ◽  
Functional Brain Networks ◽  
Cortical Regions

The utility of single-pulse electrical stimulation (SPES) for epilepsy surgery has been highlighted in the last decade. When applied at a frequency of about 1 Hz, it can probe cortico-cortical connections by averaging electrocorticographic signal time-locked to stimuli to record cortico-cortical evoked potentials (CCEPs) emanating from adjacent and remote cortices. Although limited to patients undergoing invasive presurgical evaluations, CCEPs provide a novel way to explore inter-regional connectivity in vivo in the living human brain to probe functional brain networks such as language and cognitive motor networks. In addition to its impact on basic systems neuroscience, this method, in combination with 50 Hz electrical cortical stimulation, can contribute clinically to the mapping of functional brain systems by tracking cortico-cortical connections among functional cortical regions in individual patients. This approach may help identify normal cortico-cortical networks in pathological brain, or plasticity of brain systems in conjunction with pathology. Because of its high practical value, it has been applied to intraoperative monitoring of functional brain networks in patients with brain tumours. With regard to epilepsy, SPES has been used to probe cortical excitability of the focus (epileptogenicity) and seizure networks. Both early (i.e. CCEP) and delayed responses are regarded as surrogate markers of epileptogenicity. With regard to its potential impact on human brain connectivity maps, worldwide collaboration is warranted to establish standardized CCEP connectivity maps as a solid reference for non-invasive connectome research.

scholarly journals Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Johann H. Martínez ◽  
María Eugenia López ◽  
Pedro Ariza ◽  
Mario Chavez ◽  
José A. Pineda-Pardo ◽  
...  
Keyword(s):  
Network Topology ◽  
Cognitive Reserve ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks
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