Results 71 to 80 of about 270,788 (377)

On the role of the corpus callosum in interhemispheric functional connectivity in humans [PDF]

, 2017
Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal)
Hacker, Carl D., Leuthardt, Eric C., Limbrick, David D., Mitra, Anish, Raichle, Marcus E., Roland, Jarod L., Shimony, Joshua S., Smyth, Matthew D., Snyder, Abraham Z.   +8 more
core   +2 more sources

Network analysis of EEG related functional MRI changes due to medication withdrawal in focal epilepsy

NeuroImage: Clinical, 2015
Anti-epileptic drugs (AEDs) have a global effect on the neurophysiology of the brain which is most likely reflected in functional brain activity recorded with EEG and fMRI.
Kees Hermans, Pauly Ossenblok, Petra van Houdt, Liesbeth Geerts, Rudolf Verdaasdonk, Paul Boon, Albert Colon, Jan C. de Munck   +7 more
doaj   +1 more source

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture [PDF]

Front. Neuroinform., 2017
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification.
R. Meszlényi, Krisztián Búza, Z. Vidnyánszky   +2 more
semanticscholar   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Estimating effective connectivity in linear brain network models

, 2017
Contemporary neuroscience has embraced network science to study the complex and self-organized structure of the human brain; one of the main outstanding issues is that of inferring from measure data, chiefly functional Magnetic Resonance Imaging (fMRI ...
Bertoldo, Alessandra, Chiuso, Alessandro, Prando, Giulia, Zorzi, Mattia   +3 more
core   +1 more source

Dynamic BOLD functional connectivity in humans and its electrophysiological correlates [PDF]

, 2012
Neural oscillations subserve many human perceptual and cognitive operations. Accordingly, brain functional connectivity is not static in time, but fluctuates dynamically following the synchronization and desynchronization of neural populations.
Brodbeck, Verena, Laufs, Helmut, Morzelewski, Astrid, Tagliazucchi, Enzo, Wegner, Frederic von   +4 more
core   +2 more sources

Clinical applications of resting state functional connectivity

Frontiers in Systems Neuroscience, 2010
During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of fMRI.
Michael D Fox, Michael Greicius
doaj   +1 more source

EEG Alpha Power Modulation of fMRI Resting-State Connectivity [PDF]

Brain Connectivity, 2012
In the past decade, the fast and transient coupling and uncoupling of functionally related brain regions into networks has received much attention in cognitive neuroscience. Empirical tools to study network coupling include functional magnetic resonance imaging (fMRI)-based functional and/or effective connectivity, and electroencephalography (EEG ...
Scheeringa, R, Petersson, KM, Kleinschmidt, A, Jensen, O, Bastiaansen, MCM   +4 more
openaire   +4 more sources

Genome‐Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes

Advanced Science, EarlyView.
This study explores genome‐wide by lifetime environment interactions on brain imaging phenotypes. Gene‐environment interactions explain more phenotypic variance than main effects, pinpoint regulatory variants, and reveal exposure‐specific biological pathways.
Sijia Wang, Meiyun Wang, Peng Zhang, Jingliang Cheng, Longjiang Zhang, Wenzhen Zhu, Shijun Qiu, Zuojun Geng, Guangbin Cui, Yongqiang Yu, Weihua Liao, Xi‐Nian Zuo, Hui Zhang, Bo Gao, Xiaojun Xu, Tong Han, Zhenwei Yao, Quan Zhang, Feng Liu, Qiang Xu, Jiayuan Xu, Jilian Fu, Nana Liu, Yuan Ji, Jie Tang, Lining Guo, Mengge Liu, Xiaoxiao Xiao, Xiaoxuan Liu, Wei Li, Caihong Wang, Wei Wei, Dapeng Shi, Su Lui, Zhihan Yan, Feng Chen, Jing Zhang, Wen Shen, Yanwei Miao, Dawei Wang, Jia‐Hong Gao, Yunjun Yang, Kai Xu, Junfang Xian, Bing Zhang, Xiaochu Zhang, Zhaoxiang Ye, Le Yu, Wen Qin, Meng Liang, Chunshui Yu, the CHIMGEN Consortium   +51 more
wiley   +1 more source

At risk of being risky: The relationship between "brain age" under emotional states and risk preference. [PDF]

, 2017
Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk.
Bonnie, Richard J, Breiner, Kaitlyn, Casey, BJ, Chein, Jason, Cohen, Alexandra O, Dellarco, Danielle V, Fair, Damien A, Fettich, Karla C, Galván, Adriana, Miranda-Domínguez, Oscar, Richeson, Jennifer A, Rudolph, Marc D, Scott, Elizabeth S, Steinberg, Laurence, Taylor-Thompson, Kim   +14 more
core   +2 more sources