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Functional connectivity analysis of fMRI data using parameterized regions-of-interest
2011
NeuroImage
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Available online xxxx Connectivity analysis of fMRI data requires correct specification of regions-of-interest (ROIs). ...
We extend a flexible framework for fMRI analysis (Activated Region Fitting, Weeda et al. 2009 ) to connectivity analysis of fMRI data. ...
analysis of fMRI data using parameterized regions-of-interest, NeuroImage (2010), doi:10.1016/j.neuroimage.2010.07.022 ...
doi:10.1016/j.neuroimage.2010.07.022
pmid:20637877
fatcat:v7xdxrgqfzgcdhsle5cnrscxkm
Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra
[chapter]
2017
Lecture Notes in Computer Science
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We use ideas from differential geometry and functional data analysis to define a functional representation for fMRI signals. ...
The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their power ...
the analysis of both amplitude and phase changes in fMRI across regions, and lastly iii) the use of group level connectivity analysis for detecting changes in patterns of fMRI connectivity across populations ...
doi:10.1007/978-3-319-67159-8_15
pmid:29953126
pmcid:PMC6018059
fatcat:sg3ortdl35dhpmfbc3ght6a74y
Joint Modeling of Anatomical and Functional Connectivity for Population Studies
2012
IEEE Transactions on Medical Imaging
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Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior ...
The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. ...
The error is averaged over 10 resamplings of the data. The fMRI likelihood parameterization is fixed at and . (a) Anatomical. (b) Functional. ...
doi:10.1109/tmi.2011.2166083
pmid:21878411
pmcid:PMC4395500
fatcat:5ku3mmnfr5gojd42rpdkhmekkq
Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner
2018
Data in Brief
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Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response ...
function (HRF). ...
Supporting information Transparency data associated with this Q5 article can be found in the online version at https://doi.org/ 10.1016/j.dib.2018.01.003. ...
doi:10.1016/j.dib.2018.01.003
pmid:29876476
pmcid:PMC5988211
fatcat:4m7x2vx4ejhs7aa2wlpswk6sny
Advances on Medical Imaging and Computing
[chapter]
2005
Lecture Notes in Computer Science
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The second part consists of brain connectivity, which includes anatomical connectivity based on diffusion tensor imaging (DTI), functional and effective connectivity with functional magnetic resonance ...
imaging (fMRI). ...
Analysis of white matter from DTI is mostly based on region of interesting (ROI) in image data set, which is specified by user. ...
doi:10.1007/11569541_3
fatcat:6siqxjm25bdwhekefbndybzkeq
Dynamic causal modelling for fMRI: A two-state model
2008
NeuroImage
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Dynamical causal modelling (DCM) for functional magnetic resonance imaging (fMRI) is a technique to infer directed connectivity among brain regions. ...
Critically, this gives us an explicit model of intrinsic (between-population) connectivity within a region. ...
Introduction Dynamic causal modelling (DCM) for fMRI is a natural extension of the convolution models used in the standard analysis of fMRI . ...
doi:10.1016/j.neuroimage.2007.08.019
pmid:17936017
fatcat:g5t453tp3jaihj3krmjcl7jpx4
Dynamic properties of simulated brain network models and empirical resting-state data
2019
Network Neuroscience
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Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. ...
We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. ...
To explain its complexity, studies have used resting-state fMRI (rs-fMRI) scans and functional connectivity (FC) analysis to describe the coordination between different brain regions of interest (ROIs) ...
doi:10.1162/netn_a_00070
pmid:30793089
pmcid:PMC6370489
fatcat:foedstzo5nbq7j5rwktwnppxw4
Relating Structural and Functional Connectivity to Performance in a Communication Task
[chapter]
2010
Lecture Notes in Computer Science
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Functional synchronization of BOLD fMRI signals between frontal and temporal regions connected by the uncinate fasciculus was also found to predict the performance measure. ...
Multiple regression analysis demonstrated that combining equidimensional measures of functional and structural connectivity identified the network components that most significantly predict performance ...
Analysis of fMRI For the examination of FC the previously cortical regions were examined. A region-growing process was used to create 5 regions of equal volume. ...
doi:10.1007/978-3-642-15745-5_35
fatcat:6vf35ypcl5aajbrp36pw4y6qze
A Window into the Brain: Advances in Psychiatric fMRI
2015
BioMed Research International
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Here we review recent advances in fMRI methods in general use and progress made in understanding the neural basis of mental illness. ...
Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research. ...
C10454) and the National Natural Science Foundation of China (no. 31371128) for financial support. ...
doi:10.1155/2015/542467
pmid:26413531
pmcid:PMC4564608
fatcat:3hat4unyozdzhgx366jgj2fq2y
A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks
2013
IEEE Transactions on Medical Imaging
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Most often, the functional connectivity between a set of regions is described by the covariance matrix, Σ ∈ R n×n , between the time series of the mean activation in the different regions of interest. ...
Beyond its typical use for mapping taskrelated regions, it reveals intrinsic functional connectivity via spontaneous fluctuations of brain activity [20] , in restingstate fMRI (rs-fMRI) experiments. ...
doi:10.1109/tmi.2013.2276916
pmid:23934663
fatcat:26fl7luy6refzfjywvuc3edzvu
Methods for Simultaneous EEG-fMRI: An Introductory Review
2012
Journal of Neuroscience
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The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. ...
The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of ...
This information was subsequently used for a parameterized analysis For an event of interest (e.g., the occurrence of a response error), a parameter value of an EEG feature (e.g., the amplitude of an ERP ...
doi:10.1523/jneurosci.0447-12.2012
pmid:22553012
pmcid:PMC6622140
fatcat:fnemhavinvaajalvheiwb46q4i
Benchmarking functional connectome-based predictive models for resting-state fMRI
2019
NeuroImage
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For each step we benchmark typical choices: 8 different ways of defining regions -either pre-defined or generated from the rest-fMRI data- 3 measures to build functional connectomes from the extracted ...
We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations ...
Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage 49, 3110. ...
doi:10.1016/j.neuroimage.2019.02.062
pmid:30836146
fatcat:gyc6jxopp5gihn3alwgrf7zcge
Relating Brain Functional Connectivity to Anatomical Connections: Model Selection
[chapter]
2012
Lecture Notes in Computer Science
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We aim to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity. ...
We select the appropriate sparsity of the connectivity matrices and demonstrate that choosing an ordering for the MAR that lends to sparser models is more appropriate than a random. ...
In a population of S subjects, we consider the connectivity between a set of N regions of interest (ROIs). ...
doi:10.1007/978-3-642-34713-9_23
fatcat:bmxbnlovnfccbb45tdfu6orv5m
Mirror Neuron System Study On Elderly Using Dynamic Causal Modeling Fmri Analysis
2011
Zenodo
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Dynamic Causal Modeling (DCM) functional Magnetic Resonance Imaging (fMRI) is a promising technique to study the connectivity among brain regions and effects of stimuli through modeling neuronal interactions ...
Twenty volunteers were MRI scanned with visual stimuli to study a functional brain network. DCM was employed to determine the mechanism of mirror neuron effects. ...
Moreover, this research is supported by National Research Council of Thailand (NRCT). ...
doi:10.5281/zenodo.1333748
fatcat:s7nzhydvfrcidpebkvgjbtommi
A review of functional magnetic resonance imaging for Brainnetome
2012
Neuroscience Bulletin
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In the general context of Brainnetome, this review focuses on the development of approaches for modeling and analyzing functional brain networks with BOLD fMRI. ...
The functional brain network using blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has revealed the potentials for probing brain architecture, as well as for identifying ...
Methods of brain network analysis using BOLD fMRI Functional connection is one of the primary topics in studies of functional networks. ...
doi:10.1007/s12264-012-1244-4
pmid:22833037
pmcid:PMC5560259
fatcat:sg3xyt6xcvc2xdsjdtrbrbtyly
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