scholarly journals Gene expression and functional brain networks

2015 ◽  
Vol 16 (8) ◽  
pp. 439-439
Author(s):  
Denise Waldron
Keyword(s):  
Gene Expression ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks

scholarly journals Commentary: BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241-4

2016 ◽  
Author(s):  
Spiro P. Pantazatos ◽  
Xinyi Li
Keyword(s):  
Gene Expression ◽  
Resting State ◽  
Euclidean Distance ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Spatial Proximity ◽  
Functional Magnetic Resonance ◽  
Future Directions ◽  
Functional Brain ◽  
Functional Brain Networks

SummaryA recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e. high within-network tissue-tissue “strength fraction”, SF) (Richiardi et al., 2015). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e. clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in (Richiardi et al., 2015), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.

scholarly journals Distance is not everything in imaging genomics of functional networks: reply to a commentary on Correlated gene expression supports synchronous activity in brain networks

2017 ◽  
Author(s):  
Jonas Richiardi ◽  
Andre Altmann ◽  
Michael Greicius
Keyword(s):  
Gene Expression ◽  
Euclidean Distance ◽  
Brain Networks ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Mouse Orthologs ◽  
Made In

AbstractOur 2015 paper (Richiardi et al., 2015), showed that transcriptional similarity of gene expression level is higher than expected by chance within functional brain networks (defined by functional magnetic resonance imaging), a relationship that is driven by around 140 genes. These results were replicated in vivo in adolescents, where we showed that SNPs of these genes where associated above chance with in-vivo fMRI connectivity, and in the mouse, where mouse orthologs of our genes showed above-chance association with meso-scale axonal connectivity. This paper has received a commentary on biorXiv (Pantazatos and Li, 2016), making several claims about our results and methods, mainly pointing out that Euclidean distance explains our results (“…high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks…”). Here we address these claims and their weaknesses, and show that our original results stand, contrary to the claims made in the commentary.

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.

scholarly journals Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs

2018 ◽  
Vol 37 (1) ◽  
pp. 230-240 ◽  
Author(s):  
Thomas A. W. Bolton ◽  
Anjali Tarun ◽  
Virginie Sterpenich ◽  
Sophie Schwartz ◽  
Dimitri Van De Ville
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks
Sign in / Sign up

Export Citation Format

Share Document