scholarly journals Elucidating the structural–functional connectome of language in glioma‐induced aphasia using nTMS and DTI

2021 ◽  
Author(s):  
Haosu Zhang ◽  
Sebastian Ille ◽  
Lisa Sogerer ◽  
Maximilian Schwendner ◽  
Axel Schröder ◽  
...  
Keyword(s):  
Functional Connectome

Graph theoretical analysis of tau burden and the functional connectome in Alzheimers disease

2019 ◽  
Author(s):  
A Weller ◽  
GN Bischof ◽  
P Schlüter ◽  
N Richter ◽  
J Kukolja ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Alzheimers Disease ◽  
Graph Theoretical Analysis ◽  
Functional Connectome

scholarly journals Development of the brain functional connectome follows puberty-dependent nonlinear trajectories

NeuroImage ◽  
2021 ◽  
Vol 229 ◽  
pp. 117769
Author(s):  
Zeus Gracia-Tabuenca ◽  
Martha Beatriz Moreno ◽  
Fernando A. Barrios ◽  
Sarael Alcauter
Keyword(s):  
Functional Connectome ◽  
The Brain

Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

scholarly journals The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic

2021 ◽  
Vol 14 ◽  
pp. 100285
Author(s):  
Peiduo Liu ◽  
Wenjing Yang ◽  
Kaixiang Zhuang ◽  
Dongtao Wei ◽  
Rongjun Yu ◽  
...  
Keyword(s):  
Functional Connectome

scholarly journals On statistical tests of functional connectome fingerprinting

2021 ◽  
Author(s):  
Zeyi Wang ◽  
Haris I. Sair ◽  
Ciprian Crainiceanu ◽  
Martin Lindquist ◽  
Bennett A. Landman ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Functional Connectome

scholarly journals A Large-Scale Structural and Functional Connectome of Social Mentalizing

NeuroImage ◽  
2021 ◽  
pp. 118115
Author(s):  
Yin Wang ◽  
Athanasia Metoki ◽  
Yunman Xia ◽  
Yinyin Zang ◽  
Yong He ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Connectome ◽  
Social Mentalizing

scholarly journals Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment

2018 ◽  
Vol 10 ◽  
Author(s):  
Juan Wang ◽  
Reza Khosrowabadi ◽  
Kwun Kei Ng ◽  
Zhaoping Hong ◽  
Joanna Su Xian Chong ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Network Topology ◽  
Brain Network ◽  
Functional Connectome

scholarly journals Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans

2017 ◽  
Author(s):  
Hexuan Liu ◽  
Jimin Kim ◽  
Eli Shlizerman
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Neuronal Network ◽  
Graphical Model ◽  
Data Matrix ◽  
Series Data ◽  
Response Dynamics ◽  
Functional Connectome ◽  
Network Response ◽  
Posterior Inference

AbstractWe propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response pathways in dynamic neuronal networks. In particular, posterior inference reveals the relations between known stimuli and downstream neurons or allows to query which stimuli are associated with downstream neurons. For validation and as an example for our approach we apply our methodology to a model of Caenorhabiditis elegans nervous system which structure and dynamics are well-studied. From its dynamical model we collect time series of the network response and use singular value decomposition to obtain a low-dimensional projection of the time series data. We then extract dominant patterns in each data matrix to get pairwise dependency information and create a graphical model for the full somatic nervous system. The PGM enables us to obtain and verify underlying neuronal pathways dominant for known behavioral scenarios and to detect possible pathways for novel scenarios.

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