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Dynamic Bayesian network modeling for longitudinal brain morphometry

Rong Chen, Susan M. Resnick, Christos Davatzikos, Edward H. Herskovits
2012 NeuroImage  
We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment -the Baltimore Longitudinal Study of Aging.  ...  The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes.  ...  In subsequent sections, we describe our DBN-based approach to modeling inter-regional associations in a longitudinal study of brain morphometry.  ... 
doi:10.1016/j.neuroimage.2011.09.023 pmid:21963916 pmcid:PMC3254821 fatcat:zkpkcfn7crh4naqwtgybrywrj4

Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

Ethan R. Buch, Amirali Modir Shanechi, Alissa D. Fourkas, Cornelia Weber, Niels Birbaumer, Leonardo G. Cohen
2012 Brain  
rhythms in the lesioned brain, secondarily improving grasping function through brain-computer interface applications.  ...  by guest on September 18, 2015 http://brain.oxfordjournals.org/ Downloaded from may serve as a future predictor of response to longitudinal therapeutic interventions geared towards training sensorimotor  ...  On the other hand, while alternative methodologies involving source-based connectivity modelling of magnetoencephalography data (i.e. dynamic causal model) allow for more direct interpretations about specific  ... 
doi:10.1093/brain/awr331 pmid:22232595 pmcid:PMC3286199 fatcat:bjjtcqkotfgprhzumpicjdhoji

Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging [chapter]

Michelle Yongmei
2013 Functional Brain Mapping and the Endeavor to Understand the Working Brain  
The simplest DBN is a HMM, with one discrete hidden node and one discrete or continuous observed node per slice.  ...  Dynamical brain system Attractors and brain dynamics Computational neuroscience illustrates the network dynamics of neurons and synapses with models to reproduce emergent properties or predict observed  ... 
doi:10.5772/55262 fatcat:go2r6jruyzdqrp4lqcnt64j7va

Characterization of Functional and Structural Integrity in Experimental Focal Epilepsy: Reduced Network Efficiency Coincides with White Matter Changes

Willem M. Otte, Rick M. Dijkhuizen, Maurits P. A. van Meer, Wilhelmina S. van der Hel, Suzanne A. M. W. Verlinde, Onno van Nieuwenhuizen, Max A. Viergever, Cornelis J. Stam, Kees P.J. Braun, Alice Y. W. Chang
2012 PLoS ONE  
Conclusions/Significance: Our longitudinal study on the pathogenesis of network dynamics in epileptic brains reveals that, despite the locality of the epileptogenic area, epileptic brains differ in their  ...  global network topology, functional connectivity and structural changes in the interictal brain in relation to focal epilepsy in a rat model.  ...  Acknowledgments The authors thank Gerard van Vliet, Annette van der Toorn and Ward Jennekens for technical assistance. Author Contributions  ... 
doi:10.1371/journal.pone.0039078 pmid:22808026 pmcid:PMC3395639 fatcat:bnzs5ompnrhpfbcuug5dwhpws4

Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder

Zhao-Min Wu, Alberto Llera, Martine Hoogman, Qing-Jiu Cao, Marcel P. Zwiers, Janita Bralten, Li An, Li Sun, Li Yang, Bin-Rang Yang, Yu-Feng Zang, Barbara Franke (+3 others)
2019 NeuroImage: Clinical  
Neuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD.  ...  The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data.  ...  Another analysis (Kessler et al., 2014) of gray and white matter morphometry and the whole brain functional connectome revealed that subjects with ADHD showed reduced DMN task-positive network segregation  ... 
doi:10.1016/j.nicl.2019.101851 pmid:31077980 pmcid:PMC6514365 fatcat:nalv77yj6rdldhrhcgcs3hfbey

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Danielle S. Bassett, Marcelo G. Mattar
2017 Trends in Cognitive Sciences  
We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.  ...  Finally, we discuss how network neuroscience could provide a quantitative framework that complements existing models of learning by cohesively accounting for network structure in neurophysiological and  ...  In this model, one starts with a network of m 0 connected nodes, and then adds new nodes one at a time by connecting a new node to m ≤ m 0 existing nodes with a probability p that is proportional to the  ... 
doi:10.1016/j.tics.2017.01.010 pmid:28259554 pmcid:PMC5366087 fatcat:4q4pohqn4rcwxeahenxakxaequ

Localizing Sources of Brain Disease Progression with Network Diffusion Model

Chenhui Hu, Xue Hua, Jun Ying, Paul M. Thompson, Georges E. Fakhri, Quanzheng Li
2016 IEEE Journal on Selected Topics in Signal Processing  
In this paper, we propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy.  ...  With this model, Raj et al. also predicted longitudinal atrophy patterns based on the current atrophy [31] .  ...  Hu Thompson specializes in the field of human brain imaging, with research interest in mathematical and computational algorithm development for human brain mapping, and has contributed to more than 1300  ... 
doi:10.1109/jstsp.2016.2601695 pmid:28503250 pmcid:PMC5423678 fatcat:3eys73rdjrex5kqur3nw2hp43q

A review of structural neuroimaging in schizophrenia: from connectivity to connectomics

Anne L. Wheeler, Aristotle N. Voineskos
2014 Frontiers in Human Neuroscience  
The distributed nature of these abnormalities in schizophrenia suggests that multiple brain circuits are impaired, a neural feature that may be better addressed with network level analyses.  ...  In patients with schizophrenia neuroimaging studies have revealed global differences with some brain regions showing focal abnormalities.  ...  ACKNOWLEDGMENTS Canadian Institutes of Health Research, Brain and Behavior Research Foundation, CAMH and the CAMH Foundation (thanks to the Kimel family, the Koerner New Scientist Award, and the Paul E  ... 
doi:10.3389/fnhum.2014.00653 pmid:25202257 pmcid:PMC4142355 fatcat:tdcsuyufgzgwflmwrcokyxg7ki

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Golia Shafiei, Vincent Bazinet, Mahsa Dadar, Ana L. Manera, D. Louis Collins, Alain Dagher, Barbara Borroni, Raquel Sanchez-Valle, Fermin Moreno, Robert Laforce, Caroline Graff, Matthis Synofzik (+23 others)
2022 Brain  
We first identify distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class.  ...  Regional atrophy was significantly correlated with atrophy of structurally- and functionally- connected neighbors, demonstrating that network structure shapes atrophy patterns.  ...  value of brain region or node , is atrophy of -th neighbor of node , is the strength of structural connection between nodes and , and is the total number of neighbors that are connected to node with a  ... 
doi:10.1093/brain/awac069 pmid:35188955 fatcat:6tkkn3i4nnh7jig6voiixtda5m

Resting-State Network Plasticity Induced by Music Therapy after Traumatic Brain Injury

Noelia Martínez-Molina, Sini-Tuuli Siponkoski, Linda Kuusela, Sari Laitinen, Milla Holma, Mirja Ahlfors, Päivi Jordan-Kilkki, Katja Ala-Kauhaluoma, Susanna Melkas, Johanna Pekkola, Antoni Rodríguez-Fornells, Matti Laine (+6 others)
2021 Neural Plasticity  
Extending this study, we performed longitudinal rsFC analyses of resting-state fMRI data using a ROI-to-ROI approach assessing within-network and between-network rsFC in the frontoparietal (FPN), dorsal  ...  By contrast, the DMN was less connected with sensory networks after the intervention.  ...  Relationship between Brain Morphometry and rsFC Changes.  ... 
doi:10.1155/2021/6682471 pmid:33763126 pmcid:PMC7964116 fatcat:grjeekc2cvbnzatpppfqij7ghy

Structural MRI correlates of PASAT performance in multiple sclerosis

Jordi A. Matias-Guiu, Ana Cortés-Martínez, Paloma Montero, Vanesa Pytel, Teresa Moreno-Ramos, Manuela Jorquera, Miguel Yus, Juan Arrazola, Jorge Matías-Guiu
2018 BMC Neurology  
Methods: PASAT (3-s) was administered together with a comprehensive neuropsychological battery. Global brain volumes and total T2-weighted lesion volumes were estimated.  ...  Voxel-based morphometry and lesion symptom mapping analyses were performed. Results: Mean PASAT score was 42.98 ± 10.44; results indicated impairment in 75 cases (31.0%).  ...  Acknowledgements The authors thank the Spanish Society of Neurology's Research Operations Office for helping in the English language revision of this paper.  ... 
doi:10.1186/s12883-018-1223-0 fatcat:xt2n73dhvvay5p37h4iqjrdg7m

Co-ordinated structural and functional covariance in the adolescent brain underlies face processing performance

Daniel Joel Shaw, Radek Mareček, Marie-Helene Grosbras, Gabriel Leonard, G. Bruce Pike, Tomáš Paus
2016 Social Cognitive and Affective Neuroscience  
By combining measures of task-related functional connectivity and brain morphology, we reveal that both the structural covariance and functional connectivity among 'distal' nodes of the face-processing  ...  Furthermore, we show that the trajectory of increasing functional connectivity between the distal nodes occurs in tandem with the development of their structural covariance.  ...  Acknowledgements We wish to thank Candice Cartier, Elissa Golden, Valerie Legge, Kristina Martinu and Line Gingras for assistance with the recruitment of participants and data collection.  ... 
doi:10.1093/scan/nsv138 pmid:26772669 pmcid:PMC4814784 fatcat:7ts5misodvhuhkurlqr3zn3shm

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Andrei Irimia, Bo Wang, Stephen R. Aylward, Marcel W. Prastawa, Danielle F. Pace, Guido Gerig, David A. Hovda, Ron Kikinis, Paul M. Vespa, John D. Van Horn
2012 NeuroImage: Clinical  
This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties  ...  value.  ...  The understanding that brain network topology and dynamics modulate a vast array of brain functions that are affected by disease has prompted an increasing interest in the theoretical aspects of network  ... 
doi:10.1016/j.nicl.2012.08.002 pmid:24179732 pmcid:PMC3757727 fatcat:2yxon4yu6rfrlfpton5qc4k5si

Microstructure-informed connectomics: enriching large-scale descriptions of healthy and diseased brains

Sara Lariviere, Reinder Vos de Wael, Casey Paquola, Seok-Jun Hong, Bratislav Misic, Neda Bernasconi, Andrea Bernasconi, Leonardo Bonilha, Boris Bernhardt
2018 Brain Connectivity  
Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them.  ...  In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and  ...  As for the nodes, the characterization of network edges has benefited from developments in MRI acquisition and modeling.  ... 
doi:10.1089/brain.2018.0587 pmid:30079754 pmcid:PMC6444904 fatcat:4lcxp2axmzfpdhuhvmkcvya3fe

Multivariate dynamical modelling of structural change during development

Gabriel Ziegler, Gerard R. Ridgway, Sarah-Jayne Blakemore, John Ashburner, Will Penny
2017 NeuroImage  
Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development.  ...  Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289  ...  This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute  ... 
doi:10.1016/j.neuroimage.2016.12.017 pmid:27979788 pmcid:PMC5315058 fatcat:wxnpz2ung5dqddlcg7plrtxq5e
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