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Probabilistic tractography using Lasso bootstrap
2017
Medical Image Analysis
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In this work, we propose a probabilistic tractography algorithm named Lasso bootstrap tractography (LBT) for the models that incorporate sparsity. ...
Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography. ...
Up to this point, however, probabilistic tractography based on bootstrapping the Lasso model has not been explored. ...
doi:10.1016/j.media.2016.08.013
pmid:27662597
pmcid:PMC5099091
fatcat:45ry6mxipfa2fkejnqopchfigy
INDIVIDUAL CLASSIFICATION OF ALZHEIMER'S DISEASE WITH DIFFUSION MAGNETIC RESONANCE IMAGING
2017
Alzheimer's & Dementia
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Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. ...
All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. ...
Boxplot of the sparse group lasso classification models from 10-fold times 10 repeated cross validation. The bars indicate the spread of the sum of the absolute beta values. ...
doi:10.1016/j.jalz.2017.06.2419
fatcat:4owo2ixbtrgmnfhverbcpwhyti
Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates
2015
PLoS Computational Biology
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Finally, we demonstrate how our approach can be extended in several ways, for instance to achieve data fusion by informing the conditional independence graph with data from probabilistic tractography. ...
In this paper, we propose a probabilistic generative model that allows us to estimate functional connectivity in terms of both partial correlations and a graph representing conditional independencies. ...
Here, we use an existing model of structural connectivity based on probabilistic tractography [39, 40] , defined as follows. ...
doi:10.1371/journal.pcbi.1004534
pmid:26540089
pmcid:PMC4634993
fatcat:ywhz2keycnednl5xnd5sc33dny
NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity
2016
PLoS ONE
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Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional ...
Subsequently, we ran probabilistic tractography, implemented in Trac-toR, for each dataset. ...
These studies demonstrated that probabilistic tractography algorithms favour the shortest, simplest and straightest paths. ...
doi:10.1371/journal.pone.0153404
pmid:27078862
pmcid:PMC4831788
fatcat:vdfossnk2zgadmxxckpi4ecsie
The Severity of Sensorimotor Tracts Degeneration May Predict Motor Performance in Chronic Stroke Patients, While Brain Structural Network Dysfunction May Not
2022
Frontiers in Neurology
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We compared fractional anisotropy (FA) and mean diffusivity (MD) in 60 CST segments using the probabilistic sensorimotor area tract template (SMATT). ...
Least Absolute Shrinkage and Selection Operator (LASSO) regressions were used to select independent predictors of Fugl-Meyer upper extremity (FM-UE) scores among FA and MD values of SMATT regions. ...
A 10-fold crossvalidation approach was used to estimate λ and the optimal LASSO model. Bootstrapping with 1,000 times resampling was used to estimate standard errors of the predictor's coefficients. ...
doi:10.3389/fneur.2022.813763
pmid:35432180
pmcid:PMC9008887
fatcat:6uthpjysvfhlloht5wg3cklsbu
Network communication models improve the behavioral and functional predictive utility of the human structural connectome
2020
Network Neuroscience
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Communication matrices were (i) used to perform predictions of five data-driven behavioral dimensions and (ii) correlated to resting-state functional connectivity (FC). ...
For instance, validation of our results for connectomes mapped using probabilistic tractography and/or larger numbers of streamline seeds would be valuable. ...
The same train and test sets were used for lasso and NBS regressions. ...
doi:10.1162/netn_a_00161
pmid:33195945
pmcid:PMC7655041
fatcat:beqmyezrwfgctcxkvic47zvt64
Network communication models improve the behavioral and functional predictive utility of the human structural connectome
[article]
2020
biorxiv/medrxiv
pre-print
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Communication matrices were (i) used to perform predictions of five data-driven behavioral dimensions and (ii) correlated to interregional resting-state functional connectivity (FC). ...
For instance, validation of our results for connectomes mapped using probabilistic tractography and/or larger numbers of streamline seeds would be valuable. ...
The same train and test sets were used for lasso and NBS regressions. ...
doi:10.1101/2020.04.21.053702
fatcat:ns5tg6l5qjepvbukv4257xgfii
Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation
[article]
2022
bioRxiv
pre-print
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Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations in a total of 550 individuals, to study inter-areal communication in the human brain ...
We used a lasso regression [94] to predict y from X. ...
Whole-brain white matter tractograms of individual participants were mapped using a probabilistic tractography pipeline (MRtrix3 software [78] , multi-shell multi-tissue constrained spherical deconvolution ...
doi:10.1101/2022.07.05.498875
fatcat:dx6qs4w4xnevlezvdyxy7bd5ni
Predictive models of brain connectivity based on transportation on a Riemannian manifold
[article]
2018
arXiv
pre-print
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Subsequently, we ran probabilistic tractography, implemented in TractoR, for each dataset. ...
Towards this end, we use randomised Lasso and bootstrapping with replacement that resample the subjects space and provide an estimation of the probability a connection to be selected. ...
arXiv:1811.08763v1
fatcat:dmlzd6jl3jc4zgskoq2atz5ire
A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks
2013
IEEE Transactions on Medical Imaging
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These probabilistic labels were then used as spatial priors in a subsequent refinement step, where a probabilistic intensity model is solved using the Expectation-Maximization (EM) algorithm. ...
For this purpose, we use the randomized LASSO, a generalization of LASSO with better sparse recovery properties [50] . ...
doi:10.1109/tmi.2013.2276916
pmid:23934663
fatcat:26fl7luy6refzfjywvuc3edzvu
Multidimensional analysis and detection of informative features in human brain white matter
2021
PLoS Computational Biology
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Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. ...
We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level ...
This manuscript was prepared using a limited access dataset obtained from the Child Mind Institute Biobank, The Healthy Brain Network dataset. ...
doi:10.1371/journal.pcbi.1009136
pmid:34181648
fatcat:dllei7jxn5airpjqq4iqobyope
Frontotemporal correlates of impulsivity and machine learning in retired professional athletes with a history of multiple concussions
2015
Brain Structure and Function
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Behavioural changes can emerge after repeated concussion and thus we used MRI to examine the UF and connected gray matter as it relates to impulsivity and aggression in retired professional football players ...
Using machine learning, we found that UF diffusion imaging differentiates athletes from healthy controls with significant classifiers based on UF mean and radial diffusivity showing 79-84 % sensitivity ...
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) ...
doi:10.1007/s00429-015-1012-0
pmid:25721800
pmcid:PMC4853456
fatcat:tno5gioxcbbtvl3btrowzfnuja
Early Imaging Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL
2019
IEEE Access
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tractography data, morphometry statistics, tractography measures, behavioral, and demographic variables. ...
Current state-of-the-art methods mainly use hypothesis-driven statistical testing methods to characterize and model such cognitive events. ...
of the probabilistic fiber tracing technique [20] . ...
doi:10.1109/access.2019.2946240
pmid:32547892
pmcid:PMC7297193
fatcat:gkphqd7s5raxfczna3j6e5e4ji
Structurally-informed Bayesian functional connectivity analysis
2014
NeuroImage
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The model was tested on simulated data as well as resting-state fMRI data and compared with a graphical lasso analysis. ...
In order to make model estimation feasible it is assumed that the sparseness structure of the precision matrices is given by an estimate of structural connectivity obtained using diffusion imaging data ...
One approach would be to use bootstrap procedures together with the graphical lasso . Another approach, as demonstrated in this paper, is to use a Bayesian approach. ...
doi:10.1016/j.neuroimage.2013.09.075
pmid:24121202
fatcat:moxfjofkdfcqxjrahmy4azqrbi
Multi-Link Analysis: Brain Network Comparison via Sparse Connectivity Analysis
[article]
2018
bioRxiv
pre-print
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Discovery is, however, hindered by the prior knowledge used to make hypotheses. On the other hand, exploratory data analysis is made complex by the high dimensionality of data. ...
In our experiments, we automatically identified disease-relevant connections in datasets with unsupervised and anatomy driven parcellation approaches using high-dimensional datasets. ...
26 -we use a multivariate bootstrap-like approach followed by a stability selection step. ...
doi:10.1101/277046
fatcat:vvgtewdkzjcihpqdhooztn4thi
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