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Faster permutation inference in brain imaging

Anderson M. Winkler, Gerard R. Ridgway, Gwenaëlle Douaud, Thomas E. Nichols, Stephen M. Smith
2016 NeuroImage  
Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive.  ...  The methods considered are freely available in the tool PALM -Permutation Analysis of Linear Models.  ...  number of tests usually performed in brain imaging.  ... 
doi:10.1016/j.neuroimage.2016.05.068 pmid:27288322 pmcid:PMC5035139 fatcat:n7dqq5rrk5f6bfr5jzh4becsvi

Towards a faster randomized parcellation based inference

Andres Hoyos-Idrobo, Gael Varoquaux, Bertrand Thirion
2017 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)  
A method designed to tackle this problem is Randomized Parcellation-Based Inference (RPBI), which has shown good empirical performance.  ...  As a main conclusion, we advocate the use of (permuted) RPBI with ReNA, as it yields very fast models, while keeping the performance of slower methods.  ...  Yet, in most of the experiments RPBI and TFCE display better performance. Regarding the computation time, the use of ReNA consistently yields faster inference algorithms.  ... 
doi:10.1109/prni.2017.7981503 dblp:conf/prni/IdroboVT17 fatcat:vtrlujazufhzrgtwhef7nwojcq

Intro to APACE – Accelerated Permutation Inference for ACE models

Thomas Nichols, Xu Chen
2017 Figshare  
Software documentation for APACE (Accelerated Permutation Inference for ACE models)  ...  ACEfit_Par.NoImg Set to 1 to optionally only compute summary measures and suppress image-wise inference (faster when only summaries are of interest).  ...  (REQUIRED) ACEfit_Par.Pmask Brain mask image (if omitted, the whole volume/surface is analyzed).  ... 
doi:10.6084/m9.figshare.5572438.v1 fatcat:shn33eg2yjhyjkzjewuqzamzre

BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

Anders Eklund, Paul Dufort, Mattias Villani, Stephen LaConte
2014 Frontiers in Neuroinformatics  
BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm 3 brain template in 4-6 seconds, and run a second level permutation test with 10,000 permutations in about a minute.  ...  These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines.  ...  Left: Voxel-level inference, the maximum t-test value is saved in each permutation. Right: Cluster-level inference, the extent of the largest cluster is saved in each permutation.  ... 
doi:10.3389/fninf.2014.00024 pmid:24672471 pmcid:PMC3953750 fatcat:5pyi7wjvmzbvnm4zth64rcthmy

Fast and powerful heritability inference for family-based neuroimaging studies

Habib Ganjgahi, Anderson M. Winkler, David C. Glahn, John Blangero, Peter Kochunov, Thomas E. Nichols
2015 NeuroImage  
We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study.  ...  The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk  ...  This work was also supported in part by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative, including the NIBIB and NCI.  ... 
doi:10.1016/j.neuroimage.2015.03.005 pmid:25812717 pmcid:PMC4463976 fatcat:ywalheb3lbdhrgrkrpookaotwm

Causal inference in audiovisual perception

Agoston Mihalik, Uta Noppeney
2020 Journal of Neuroscience  
This functional magnetic resonance imaging (fMRI) study shows that the lateral prefrontal cortex plays a key role in inferring the environment's causal structure.  ...  An unresolved question is how the brain solves this binding or causal inference problem and determines the causal structure of the sensory signals.In this functional magnetic resonance imaging (fMRI) study  ...  which brain systems are critical for solving this causal inference problem.  ... 
doi:10.1523/jneurosci.0051-20.2020 pmid:32669354 pmcid:PMC7486655 fatcat:7iif35viircarndlxjcb7l3lxe

Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification

Bilwaj Gaonkar, Christos Davatzikos
2013 NeuroImage  
In this paper we show that the results of SVM-permutation testing can be analytically approximated.  ...  However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing.  ...  Supplementary data Supplementary materials and code In order to enable readers to replicate results presented in this manuscript and perform further experiments of their own we are releasing the code  ... 
doi:10.1016/j.neuroimage.2013.03.066 pmid:23583748 pmcid:PMC3767485 fatcat:3vb543ktejfnzh4hd4nlcndi4i

Accelerated estimation and permutation inference for ACE modeling

Xu Chen, Elia Formisano, Gabriëlla A. M. Blokland, Lachlan T. Strike, Katie L. McMahon, Greig I. Zubicaray, Paul M. Thompson, Margaret J. Wright, Anderson M. Winkler, Tian Ge, Thomas E. Nichols
2019 Human Brain Mapping  
Combined with permutation, we call this approach "Accelerated Permutation Inference for the ACE Model (APACE)" where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental  ...  There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes.  ...  DATA ACCESSIBILITY We have developed a Matlab-based tool "Accelerated Permutation Inference for the ACE Model (APACE)", which provides different analysis approaches specialized for heritability inference  ... 
doi:10.1002/hbm.24611 pmid:31037793 pmcid:PMC6680147 fatcat:dyyqidmvk5dkfgxbl5weqnzwuu

Exact Topological Inference For Paired Brain Networks Via Persistent Homology [article]

Moo K. Chung, Victoria Villalta-Gil, Hyekyoung Lee, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald
2017 bioRxiv   pre-print
We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology.  ...  The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests.  ...  We thank Yoonsuck Choe of Texas A&M University and and Daniel Rowe of Marquette University for valuable discussions on permutation tests.  ... 
doi:10.1101/140533 fatcat:i55uzwtrsvbxpbqn5oy7eooue4

Exact Topological Inference for Paired Brain Networks via Persistent Homology [chapter]

Moo K. Chung, Victoria Villalta-Gil, Hyekyoung Lee, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald
2017 Lecture Notes in Computer Science  
We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology.  ...  The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests.  ...  We thank Yoonsuck Choe of Texas A&M University and and Daniel Rowe of Marquette University for valuable discussions on permutation tests.  ... 
doi:10.1007/978-3-319-59050-9_24 pmid:29075089 pmcid:PMC5654491 fatcat:hzfj37axmnhozaks4demojhwei

Faster family-wise error control for neuroimaging with a parametric bootstrap

Simon N Vandekar, Theodore D Satterthwaite, Adon Rosen, Rastko Ciric, David R Roalf, Kosha Ruparel, Ruben C Gur, Raquel E Gur, Russell T Shinohara
2017 Biostatistics  
In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image.  ...  To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level.  ...  Support for developing statistical analyses (RTS, TDS) was provided by a seed grant by the Center for Biomedical Computing and Image Analysis (CBICA) at Penn.  ... 
doi:10.1093/biostatistics/kxx051 pmid:29059370 fatcat:5zazib2s6ncbxash4xj7bzuhju

Power in Voxel-based Lesion-Symptom Mapping

Daniel Y. Kimberg, H. Branch Coslett, Myrna F. Schwartz
2007 Journal of Cognitive Neuroscience  
Voxelbased approaches to mapping lesion-behavior correlations in brain-injured populations are increasingly popular, and have the potential to leverage image analysis methods drawn from functional magnetic  ...  Voxelbased approaches to mapping lesion-behavior correlations in brain-injured populations are increasingly popular, and have the potential to leverage image analysis methods drawn from functional magnetic  ...  Acknowledgments This work was supported by the Human Brain Project via R01MH073529 and R01DA014418, by the Neuro-cognitive Rehabilitation Research Network (ncrrn.org) via R24HD050836, P30NS045839, and  ... 
doi:10.1162/jocn.2007.19.7.1067 pmid:17583984 fatcat:ibygeguaxbcmfcuufo7g5jtrou

Brain surface contraction mapped in first-episode schizophrenia: a longitudinal magnetic resonance imaging study

D Sun, G W Stuart, M Jenkinson, S J Wood, P D McGorry, D Velakoulis, T G M van Erp, P M Thompson, A W Toga, D J Smith, T D Cannon, C Pantelis
2008 Molecular Psychiatry  
While these threshold-based permutation tests allow inferences about group differences within the defined regions, they do not control for multiple inference at the level of individual surface points.  ...  In the halfway space, brain-cerebrospinal fluid boundary voxels for both baseline and follow-up brain images were determined by finding the edge voxels of tissue-classified brain images.  ... 
doi:10.1038/mp.2008.34 pmid:18607377 pmcid:PMC2773126 fatcat:awbtjwcgkfbo3ahtcvnbn2nmn4

Rapid Acceleration of the Permutation Test via Slow Random Walks in the Permutation Group [article]

Moo K. Chung, Yixian Wang, Shih-Gu Huang, Ilwoo Lyu
2019 arXiv   pre-print
The permutation test is an often used test procedure in brain imaging.  ...  Unfortunately, generating every possible permutation for large-scale brain image datasets such as HCP and ADNI with hundreds images is not practical.  ...  In various brain imaging applications, computing statistic f for each permutation has been the main computational bottleneck [12, 16, 4] .  ... 
arXiv:1812.06696v2 fatcat:az57ja6kw5aaliqbmdsqequibu

Activation likelihood estimation meta-analysis revisited

Simon B. Eickhoff, Danilo Bzdok, Angela R. Laird, Florian Kurth, Peter T. Fox
2012 NeuroImage  
In summary, we thus replaced the previous permutation algorithm with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison  ...  In this report, we outline how this previous approach may be replaced by a faster and more precise analytical method.  ...  Acknowledgments We acknowledge funding by the Human Brain Project (R01-MH074457-01A1; PTF, ARL, SBE), the DFG (IRTG 1328; SBE, DB) and the Helmholtz Initiative on Systems-Biology "The Human Brain Model  ... 
doi:10.1016/j.neuroimage.2011.09.017 pmid:21963913 pmcid:PMC3254820 fatcat:ivq7q7cbdze7rkxqo55ffhbuci
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