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 High frequency functional brain networks in neonates revealed by rapid acquisition resting state fMRI

2015 ◽  
Vol 36 (7) ◽  
pp. 2483-2494 ◽  
Author(s):  
Adam P.R. Smith‐Collins ◽  
Karen Luyt ◽  
Axel Heep ◽  
Risto A. Kauppinen
Keyword(s):  
Resting State ◽  
High Frequency ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Functional Brain ◽  
Rapid Acquisition ◽  
Functional Brain Networks

scholarly journals Aging Reduces the Functional Brain Networks Strength—a Resting State fMRI Study of Healthy Mouse Brain

2019 ◽  
Vol 11 ◽  
Author(s):  
Ander Egimendia ◽  
Anuka Minassian ◽  
Michael Diedenhofen ◽  
Dirk Wiedermann ◽  
Pedro Ramos-Cabrer ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Resting State ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Healthy Mouse ◽  
Functional Brain ◽  
Fmri Study ◽  
Functional Brain Networks

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

2021 ◽  
Author(s):  
Seyedeh-Rezvan Farahibozorg ◽  
Janine D Bijsterbosch ◽  
Weikang Gong ◽  
Saad Jbabdi ◽  
Stephen M Smith ◽  
...  
Keyword(s):  
Big Data ◽  
Resting State ◽  
Brain Networks ◽  
Resting State Fmri ◽  
List Type ◽  
Uk Biobank ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Dual Regression ◽  
Cognitive Traits

AbstractA major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model’s utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than we have achieved previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.HighlightsWe introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big datasPROFUMO hierarchically estimates fMRI networks in population and individualsWe characterised high dimensional resting state fMRI networks from UK BiobankModel outperforms ICA and dual regression for estimation of individual-specific network topographyWe demonstrate the model’s utility for predicting cognitive traits

scholarly journals Hyperbolic disc embedding of functional human brain connectomes using resting state fMRI

2021 ◽  
Author(s):  
Wonseok Whi ◽  
Seunggyun Ha ◽  
Hyejin Kang ◽  
Dong Soo Lee
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Resting State Fmri ◽  
Hyperbolic Distance ◽  
Functional Brain ◽  
Scale Free ◽  
Functional Brain Networks ◽  
Individual Scale ◽  
The Brain

The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging (rs-fMRI), we constructed a scale-free binary graph for each subject, using internodal time-series correlation of regions-of-interest (ROIs) as a proximity measure. The resulted network could be embedded onto manifolds of various curvature and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using a popularity-similarity optimization model (PSOM) on the hyperbolic plane, we reduced the dimension of the network into 2-D hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the PSOM discs. Each individual PSOM disc revealed decentralized nature of information flow and anatomic relevance. Using the hyperbolic distance on the PSOM disc, we could detect the anomaly of network in autistic spectrum disorder (ASD) subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.

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 Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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