3,384 Hits in 5.7 sec

Motor skill learning induces brain network plasticity: A diffusion-tensor imaging study

Yan-Ling Pi, Xu-Heng Wu, Feng-Juan Wang, Ke Liu, Yin Wu, Hua Zhu, Jian Zhang, Satoru Hayasaka
2019 PLoS ONE  
However, few studies have characterized the brain network topological features of motor skill learning, especially open skill.  ...  the network properties of the two groups at global and regional levels.  ...  A pioneering study using graph theory to examine the brain networks of elite athletes was conducted by Wang et al. (2013) [16] .  ... 
doi:10.1371/journal.pone.0210015 pmid:30726222 pmcid:PMC6364877 fatcat:l47i6mnxtbcppnxuzlx6gqojm4

Children's academic attainment is linked to the global organization of the white matter connectome

Joe Bathelt, Susan E Gathercole, Sally Butterfield, Duncan E Astle
2018 Developmental Science  
Our findings indicate that distributed brain systems contribute to the etiology of difficulties with academic learning, which cannot be captured using a more traditional voxel-wise statistical approach  ...  However, most studies emphasize focal brain contributions to literacy and numeracy development by employing case-control designs and voxel-by-voxel statistical comparisons.  ...  To investigate how the organization of the structural brain network may relate to academic attainment, we constructed a network that represents white matter connections throughout the brain based on diffusion-weighted  ... 
doi:10.1111/desc.12662 pmid:29532626 fatcat:zkbqkagl65a3dmvlphcr576msq

A concise and persistent feature to study brain resting‐state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Liqun Kuang, Xie Han, Kewei Chen, Richard J. Caselli, Eric M. Reiman, Yalin Wang
2018 Human Brain Mapping  
cost feature to model brain networks over all possible scales.  ...  (MCI) exhibit disrupted topological organization in large-scale brain networks.  ...  ACKNOWLEDGEMENT This work was partially supported by the National Natural Science Foundation of China (61379080, 61672473 and 61602426 for LK and XH); National Institute on Aging (R21AG043760 and RF1AG051710  ... 
doi:10.1002/hbm.24383 pmid:30569583 pmcid:PMC6570412 fatcat:oh6le3bhhzeixnp77yykn47fr4

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

2018 IEEE journal of biomedical and health informatics  
systems-level disruptions initiated by the disease.  ...  Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for  ...  constructed from pathway databases (by means of nodes and edge properties), (Fig 1C) (iv) identification of activated regions, or subpathways, of the pathway interaction network through graph mining  ... 
doi:10.1109/jbhi.2018.2863202 pmid:30080151 fatcat:vtub3nvgpvbflersyeaciftoxy

Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network [article]

Xi Sheryl Zhang, Jingyuan Chou, Fei Wang
2019 arXiv   pre-print
The information contained in the patient EHRs before the acquisition of each brain image is captured by a memory network because of its sequential nature.  ...  In this paper, we proposed a framework, Memory-Based Graph Convolution Network (MemGCN), to perform integrative analysis with such multi-modal data.  ...  The authors would like to thank the support from Amazon Web Service Machine Learning for Research Award (AWS MLRA).  ... 
arXiv:1809.06018v4 fatcat:tqawiaohcfcrfo4kazsx2fhm5y

Diagnostic potential of multimodal neuroimaging in posttraumatic stress disorder

Jooyeon Jamie Im, Binna Kim, Jaeuk Hwang, Jieun E. Kim, Jung Yoon Kim, Sandy Jeong Rhie, Eun Namgung, Ilhyang Kang, Sohyeon Moon, In Kyoon Lyoo, Chang-hyun Park, Sujung Yoon (+1 others)
2017 PLoS ONE  
whether local, connectivity, and network features of brain regions of the fear circuitry including the amygdala, orbitofrontal and ventromedial prefrontal cortex (OMPFC), hippocampus, insula, and thalamus  ...  Based on previous findings that the structural features of the fear circuitry-related brain regions may dynamically change during recovery from the trauma, we employed a machine learning approach to determine  ...  Region-wise connection density and cost were obtained by averaging the pair-wise values for each ROI. Pair-wise connectivity features.  ... 
doi:10.1371/journal.pone.0177847 pmid:28558004 pmcid:PMC5448741 fatcat:ryeq2pyf5zgo7g534tm24wxyoy

Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks [article]

Jiahao Liu, Guixiang Ma, Fei Jiang, Chun-Ta Lu, Philip S. Yu, Ann B. Ragin
2019 arXiv   pre-print
Moreover, we propose to preserve the community structure of brain networks in the graph convolutions by considering the intra-community and inter-community properties in the learning process.  ...  In this paper, we propose a framework of Siamese community-preserving graph convolutional network (SCP-GCN) to learn the structural and functional joint embedding of brain networks.  ...  By comparing S-GCN with GCN and SCP-GCN with CP-GCN, we can see that the pair-wise similarity learning enabled by Siamese network leads to a better learning performance, which shows the pair-wise similarity  ... 
arXiv:1911.03583v1 fatcat:ahuskrkwyjdfrabpx23lrlwfkm

An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

Bo-yong Park, Richard AI Bethlehem, Casey Paquola, Sara Larivière, Raul Rodríguez-Cruces, Reinder Vos de Wael, Edward T Bullmore, Boris C Bernhardt, Neuroscience in Psychiatry Network (NSPN) Consortium
2021 eLife  
Adolescence is a critical time for the continued maturation of brain networks.  ...  Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain.  ...  (B) Group-wise consistency matrix was constructed by averaging subject-wise consistency matrices. The k-238 means clustering with silhouette coefficient was used for defining modules.  ... 
doi:10.7554/elife.64694 fatcat:qey45dlnwfd4ti33tqcp4yjb6y

Macroscale connectome manifold expansion in adolescence [article]

Bo-yong Park, Richard A.I. Bethlehem, Casey Paquola, Sara Lariviere, Raul R Cruces, Reinder Vos de Wael, Edward T Bullmore, Boris Bernhardt, Neuroscience in Psychiatry Network (NSPN) Consortium
2020 bioRxiv   pre-print
networks from the rest of the brain.  ...  Adolescence is a critical time for the continued maturation of brain networks, and the current work assessed longitudinal reconfigurations of diffusion MRI derived connectomes in a large sample (n = 208  ...  We constructed the group-wise consistency matrix by averaging the consistency matrix of all subjects and applied k-means clustering (Fig. S1B ).  ... 
doi:10.1101/2020.06.22.165621 fatcat:ob5kwzvdunclxp4zp3udmpiolm

Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points -quantifying its progression is then expressed in terms of  ...  To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs.  ...  In other words, we preserve some structural consistencies across the groups while still allowing the group-wise bases to be different.  ... 
doi:10.1109/cvpr.2016.276 dblp:conf/cvpr/HwangACRBJS16 fatcat:g65toqv2ajhfniaa22bonzb6gm

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  Then, the authors implemented a residual learning architecture with graph convolutions to capture brain longitudinal changes to predict missing DMRI data over time in a patch-wise manner.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Alexandru D. Iordan, Katherine A. Cooke, Kyle D. Moored, Benjamin Katz, Martin Buschkuehl, Susanne M. Jaeggi, John Jonides, Scott J. Peltier, Thad A. Polk, Patricia A. Reuter-Lorenz
2018 Frontiers in Aging Neuroscience  
Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network  ...  Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions  ...  ACKNOWLEDGMENTS This research was supported by National Institute on Aging [R21-AG-045460] grant to PR-L. The authors thank Sneha Rajen and KyungJun Kim for assistance with data analysis.  ... 
doi:10.3389/fnagi.2017.00419 pmid:29354048 pmcid:PMC5758500 fatcat:45xguneofbh53lig242uxyvsv4

Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging

Zhanxiong Wu, Yun Peng, Sudhakar Selvaraj, Paul E. Schulz, Yingchun Zhang
2020 Frontiers in Aging Neuroscience  
Brain structural network changes provide key information about the aging process of the brain.  ...  In this study, a newly developed Ensemble Average Propagator (EAP) based probabilistic tractography method was applied to construct a structural network, with the strength of the link between any two brain  ...  Network graphs were created that consisted of a series of nodes connected by edges to interpret the generated adjacency matrices.  ... 
doi:10.3389/fnagi.2020.00061 pmid:32210792 pmcid:PMC7076118 fatcat:2i7b5foudnhelb3gf33q4qszie

Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia [article]

Caroline Garcia Forlim, Leonie Klock, Johanna Baechle, Laura Stoll, Patrick Giemsa, Marie Fuchs, Nikola Schoofs, Christiane Montag, Juergen Gallinat, Simone Kuehn
2018 bioRxiv   pre-print
Interestingly, graph analysis on the whole brain functional networks did not reveal group differences.  ...  NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology.  ...  In rsfMRI, functional brain networks are often constructed based on brain areas that were obtained from anatomical templates for nodes and temporal correlations given by Pearson's correlation coefficient  ... 
doi:10.1101/506576 fatcat:5o2bagoniven5i73l2o6jqlvvu

Brain Structural Connectivity Network Alterations in Insomnia Disorder Reveal a Central Role of the Right Angular Gyrus

Yishul Wei, Tom Bresser, Rick Wassing, Diederick Stoffers, Eus J.W. Van Someren, Jessica C. Foster-Dingley
2019 NeuroImage: Clinical  
Probabilistic tractography was performed to construct the whole-brain structural connectivity network of each participant.  ...  Moreover, converging support was given by the finding of heightened right angular gyrus nodal efficiency (harmonic centrality) across varying graph density in people with ID.  ...  We evaluated significance of group differences consistent across varying graph density with permutation tests, as follows.  ... 
doi:10.1016/j.nicl.2019.102019 pmid:31678910 pmcid:PMC6839281 fatcat:wwymdd3et5h4xpsahmhgg37hyu
« Previous Showing results 1 — 15 out of 3,384 results