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Dynamic Bayesian network modeling for longitudinal brain morphometry
2012
NeuroImage
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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
2012
Brain
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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]
2013
Functional Brain Mapping and the Endeavor to Understand the Working Brain
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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
2012
PLoS ONE
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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
2019
NeuroImage: Clinical
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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
2017
Trends in Cognitive Sciences
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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
2016
IEEE Journal on Selected Topics in Signal Processing
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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
2014
Frontiers in Human Neuroscience
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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
2022
Brain
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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
2021
Neural Plasticity
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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
2018
BMC Neurology
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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
2016
Social Cognitive and Affective Neuroscience
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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
2012
NeuroImage: Clinical
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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
2018
Brain Connectivity
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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
2017
NeuroImage
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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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