$$\delta $$ -MAPS: From fMRI Data to Functional Brain Networks

2017 ◽  
pp. 1237-1249
Author(s):  
Ilias Fountalis ◽  
Constantine Dovrolis ◽  
Bistra Dilkina ◽  
Shella Keilholz
Keyword(s):  
Brain Networks ◽  
Fmri Data ◽  
Functional Brain ◽  
Functional Brain Networks

scholarly journals METHODOLOGY OF DISCOVERING FUNCTIONAL BRAIN NETWORKS FROM FMRI DATA IN RESTING STAGE

2019 ◽  
Author(s):  
Irina Enyagina ◽  
Alexey Poyda ◽  
Vadim Ushakov ◽  
Maxim Sharaev ◽  
Vyacheslav Orlov ◽  
...  
Keyword(s):  
Brain Networks ◽  
Fmri Data ◽  
Functional Brain ◽  
Functional Brain Networks

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.

scholarly journals Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

NeuroImage ◽  
2021 ◽  
pp. 118513
Author(s):  
Seyedeh-Rezvan Farahibozorg ◽  
Janine D. Bijsterbosch ◽  
Weikang Gong ◽  
Saad Jbabdi ◽  
Stephen M Smith ◽  
...  
Keyword(s):  
Brain Networks ◽  
Fmri Data ◽  
Functional Brain ◽  
Hierarchical Modelling ◽  
Functional Brain Networks

scholarly journals Alterations in functional brain networks in depressed patients with a suicide attempt history

2019 ◽  
Vol 45 (6) ◽  
pp. 964-974 ◽  
Author(s):  
JeYoung Jung ◽  
Sunyoung Choi ◽  
Kyu-Man Han ◽  
Aram Kim ◽  
Wooyoung Kang ◽  
...  
Keyword(s):  
Suicide Attempt ◽  
Brain Networks ◽  
Functional Brain ◽  
Depressed Patients ◽  
Functional Brain Networks

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

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