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.

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 Author Correction: Dynamic reconfiguration of functional brain networks during working memory training

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Karolina Finc ◽  
Kamil Bonna ◽  
Xiaosong He ◽  
David M. Lydon-Staley ◽  
Simone Kühn ◽  
...  
Keyword(s):  
Working Memory ◽  
Brain Networks ◽  
Dynamic Reconfiguration ◽  
Working Memory Training ◽  
Memory Training ◽  
Functional Brain ◽  
Functional Brain Networks

Task-merging for finer separation of functional brain networks in working memory

Cortex ◽  
2020 ◽  
Vol 125 ◽  
pp. 246-271 ◽  
Author(s):  
Nicole Sanford ◽  
Jennifer C. Whitman ◽  
Todd S. Woodward
Keyword(s):  
Working Memory ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks

scholarly journals Vascular physiology drives functional brain networks

2018 ◽  
Author(s):  
Molly G. Bright ◽  
Joseph R. Whittaker ◽  
Ian D. Driver ◽  
Kevin Murphy
Keyword(s):  
Time Series ◽  
Vascular Function ◽  
Brain Networks ◽  
Memory Task ◽  
Imaging Data ◽  
Dual Nature ◽  
Functional Brain ◽  
Vascular Regulation ◽  
Functional Brain Networks ◽  
Dual Regression

ABSTRACTWe present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO2 inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three “neuronal networks” were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R2 parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO2 time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO2 challenge was delivered and CO2 levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain’s vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology.

scholarly journals Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques

2021 ◽  
Vol 15 ◽  
Author(s):  
Ramon Casanova ◽  
Robert G. Lyday ◽  
Mohsen Bahrami ◽  
Jonathan H. Burdette ◽  
Sean L. Simpson ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Resting State ◽  
Brain Networks ◽  
Memory Task ◽  
Fmri Data ◽  
High Dimensional ◽  
Functional Brain ◽  
Learning Techniques ◽  
Human Connectome Project ◽  
Functional Brain Networks

Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning.Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics.Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly.Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.

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