preproc.bib

@article{avantsReproducibleEvaluationANTs2010,
  title = {A {{Reproducible Evaluation}} of {{ANTs Similarity Metric Performance}} in {{Brain Image Registration}}},
  author = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook, Philip A. and Klein, Arno and Gee, James C.},
  year = {2010},
  month = sep,
  journal = {NeuroImage},
  volume = {54},
  number = {3},
  pages = {2033},
  doi = {10.1016/j.neuroimage.2010.09.025},
  urldate = {2024-11-15},
  langid = {english},
  pmid = {20851191},
  file = {/home/foranw/Zotero/storage/EGXJMMXU/Avants et al. - 2010 - A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration.pdf}
}

@article{coxAFNISoftwareAnalysis1996,
  title = {{{AFNI}}: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages},
  shorttitle = {{{AFNI}}},
  author = {Cox, R. W.},
  year = {1996},
  month = jun,
  journal = {Comput Biomed Res},
  volume = {29},
  number = {3},
  pages = {162--173},
  issn = {0010-4809},
  doi = {10.1006/cbmr.1996.0014},
  abstract = {A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.},
  langid = {english},
  pmid = {8812068},
  keywords = {Brain,Computer Systems,Data Display,Humans,Image Processing Computer-Assisted,Magnetic Resonance Imaging,Programming Languages,Software,Stereotaxic Techniques,User-Computer Interface},
  file = {/home/foranw/Zotero/storage/63P4HIZ7/Cox - 1996 - AFNI software for analysis and visualization of functional magnetic resonance neuroimages.pdf}
}

@article{fonovUnbiasedAverageAgeappropriate2011,
  title = {Unbiased Average Age-Appropriate Atlases for Pediatric Studies},
  author = {Fonov, Vladimir and Evans, Alan C. and Botteron, Kelly and Almli, C. Robert and McKinstry, Robert C. and Collins, D. Louis},
  year = {2011},
  month = jan,
  journal = {NeuroImage},
  volume = {54},
  number = {1},
  pages = {313--327},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2010.07.033},
  urldate = {2024-11-15},
  abstract = {Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5--18.5years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5--6.9years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.},
  keywords = {Atlas template,Pediatric image analysis,Registration},
  file = {/home/foranw/Zotero/storage/QPPABLKC/Fonov et al. - 2011 - Unbiased average age-appropriate atlases for pediatric studies.pdf;/home/foranw/Zotero/storage/KZ7QJMZ8/S1053811910010062.html}
}

@misc{foranLabNeuroCogDevelFmri_processing_scriptsInit2023,
  title = {{{LabNeuroCogDevel}}/Fmri\_processing\_scripts: Init},
  shorttitle = {{{LabNeuroCogDevel}}/Fmri\_processing\_scripts},
  author = {Foran, Will and {michaelhallquist} and Will and Hwang, Kai and {mjalbrzikowski} and Wilson, Jonathan},
  year = {2023},
  month = sep,
  doi = {10.5281/zenodo.8320245},
  urldate = {2024-11-15},
  abstract = {initial release for zenodo doi},
  howpublished = {Zenodo},
  file = {/home/foranw/Zotero/storage/LGPJM6WN/8320245.html}
}

@misc{gorgolewskiNipypeFlexibleLightweight2016,
  title = {Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in {{Python}}. 0.12.0-Rc1},
  shorttitle = {Nipype},
  author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Burns, Christopher and Ziegler, Erik and Pinsard, Basile and Madison, Cindee and Waskom, Michael and Ellis, David Gage and Clark, Dav and Dayan, Michael and {Manh{\~a}es-Savio}, Alexandre and Notter, Michael Philipp and Johnson, Hans and Dewey, Blake E and Halchenko, Yaroslav O. and Hamalainen, Carlo and Keshavan, Anisha and Clark, Daniel and Huntenburg, Julia M. and Hanke, Michael and Nichols, B. Nolan and Wassermann, Demian and Eshaghi, Arman and Markiewicz, Christopher and Varoquaux, Gael and Acland, Benjamin and Forbes, Jessica and Rokem, Ariel and Kong, Xiang-Zhen and Gramfort, Alexandre and Kleesiek, Jens and Schaefer, Alexander and Sikka, Sharad and {Perez-Guevara}, Martin Felipe and Glatard, Tristan and Iqbal, Shariq and Liu, Siqi and Welch, David and Sharp, Paul and Warner, Joshua and Kastman, Erik and Lampe, Leonie and Perkins, L. Nathan and Craddock, R. Cameron and K{\"u}ttner, Ren{\'e} and Bielievtsov, Dmytro and Geisler, Daniel and Gerhard, Stephan and Liem, Franziskus and Linkersd{\"o}rfer, Janosch and Margulies, Daniel S. and Andberg, Sami Kristian and Stadler, J{\"o}rg and Steele, Christopher John and Broderick, William and Cooper, Gavin and Floren, Andrew and Huang, Lijie and Gonzalez, Ivan and McNamee, Daniel and Papadopoulos Orfanos, Dimitri and Pellman, John and Triplett, William and Ghosh, Satrajit},
  year = {2016},
  month = apr,
  doi = {10.5281/zenodo.50186},
  urldate = {2024-11-15},
  abstract = {Release 0.12.0-rc1 (April 20, 2016) 	 ENH: Add nipype\_crash\_search command (https://github.com/nipy/nipype/pull/1422) 	 ENH: Created interface for BrainSuite Cortical Surface Extraction command line tools (https://github.com/nipy/nipype/pull/1305) 	 FIX: job execution on systems/approaches where locale is undefined (https://github.com/nipy/nipype/pull/1401) 	 FIX: Clean up byte/unicode issues using subprocess (https://github.com/nipy/nipype/pull/1394) 	 FIX: Prevent crash when tvtk is loaded - ETS\_TOOLKIT=null (https://github.com/nipy/nipype/pull/973) 	 ENH: New interfaces in dipy: RESTORE, EstimateResponseSH, CSD and StreamlineTractography (https://github.com/nipy/nipype/pull/1090) 	 ENH: Added interfaces of AFNI (https://github.com/nipy/nipype/pull/1360, https://github.com/nipy/nipype/pull/1361, https://github.com/nipy/nipype/pull/1382) 	 ENH: Provides a Nipype wrapper for antsJointFusion (https://github.com/nipy/nipype/pull/1351) 	 ENH: Added support for PETPVC (https://github.com/nipy/nipype/pull/1335) 	 ENH: Merge S3DataSink into DataSink, added AWS documentation (https://github.com/nipy/nipype/pull/1316) 	 TST: Cache APT in CircleCI (https://github.com/nipy/nipype/pull/1333) 	 ENH: Add new flags to the BRAINSABC for new features (https://github.com/nipy/nipype/pull/1322) 	 ENH: Provides a Nipype wrapper for ANTs DenoiseImage (https://github.com/nipy/nipype/pull/1291) 	 FIX: Minor bugfix logging hash differences (https://github.com/nipy/nipype/pull/1298) 	 FIX: Use released Prov python library (https://github.com/nipy/nipype/pull/1279) 	 ENH: Support for Python 3 (https://github.com/nipy/nipype/pull/1221) 	 FIX: VTK version check missing when using tvtk (https://github.com/nipy/nipype/pull/1219) 	 ENH: Added an OAR scheduler plugin (https://github.com/nipy/nipype/pull/1259) 	 ENH: New ANTs interface: antsBrainExtraction (https://github.com/nipy/nipype/pull/1231) 	 API: Default model level for the bedpostx workflow has been set to "2" following FSL 5.0.9 lead 	 ENH: New interfaces for interacting with AWS S3: S3DataSink and S3DataGrabber (https://github.com/nipy/nipype/pull/1201) 	 ENH: Interfaces for MINC tools (https://github.com/nipy/nipype/pull/1304) 	 FIX: Use realpath to determine hard link source (https://github.com/nipy/nipype/pull/1388) 	 FIX: Correct linking/copying fallback behavior (https://github.com/nipy/nipype/pull/1391) 	 ENH: Nipype workflow and interfaces for FreeSurfer's recon-all (https://github.com/nipy/nipype/pull/1326) 	 FIX: Permit relative path for concatenated\_file input to Concatenate() (https://github.com/nipy/nipype/pull/1411) 	 ENH: Makes ReconAll workflow backwards compatible with FreeSurfer 5.3.0 (https://github.com/nipy/nipype/pull/1434)},
  howpublished = {Zenodo},
  keywords = {neuroimaging,pipeline,workflow},
  file = {/home/foranw/Zotero/storage/TITRUWMT/50186.html}
}

@article{iglesiasRobustBrainExtraction2011,
  title = {Robust Brain Extraction across Datasets and Comparison with Publicly Available Methods},
  author = {Iglesias, Juan Eugenio and Liu, Cheng-Yi and Thompson, Paul M. and Tu, Zhuowen},
  year = {2011},
  month = sep,
  journal = {IEEE Trans Med Imaging},
  volume = {30},
  number = {9},
  pages = {1617--1634},
  issn = {1558-254X},
  doi = {10.1109/TMI.2011.2138152},
  abstract = {Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. ROBEX was compared with six other popular, publicly available methods (BET, BSE, FreeSurfer, AFNI, BridgeBurner, and GCUT) on three publicly available datasets (IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination.},
  langid = {english},
  pmid = {21880566},
  keywords = {Adult,Aged,Algorithms,Brain,Computer Simulation,Database Management Systems,Databases Factual,Discriminant Analysis,Electronic Data Processing,Female,Humans,Image Processing Computer-Assisted,Magnetic Resonance Imaging,Male,Middle Aged,Models Anatomic,Pattern Recognition Automated,Reproducibility of Results,Sensitivity and Specificity,Skull}
}

@article{jenkinsonFSL2012,
  title = {{{FSL}}},
  author = {Jenkinson, Mark and Beckmann, Christian F. and Behrens, Timothy E. J. and Woolrich, Mark W. and Smith, Stephen M.},
  year = {2012},
  month = aug,
  journal = {Neuroimage},
  volume = {62},
  number = {2},
  pages = {782--790},
  issn = {1095-9572},
  doi = {10.1016/j.neuroimage.2011.09.015},
  abstract = {FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.},
  langid = {english},
  pmid = {21979382},
  keywords = {Brain,Brain Mapping,Diffusion Magnetic Resonance Imaging,History 20th Century,History 21st Century,Humans,Image Processing Computer-Assisted,Software},
  file = {/home/foranw/Zotero/storage/IHVLKDCH/Jenkinson et al. - 2012 - FSL.pdf}
}

@article{patelWaveletbasedEstimatorDegrees2016,
  title = {A Wavelet-Based Estimator of the Degrees of Freedom in Denoised {{fMRI}} Time Series for Probabilistic Testing of Functional Connectivity and Brain Graphs},
  author = {Patel, Ameera X. and Bullmore, Edward T.},
  year = {2016},
  month = nov,
  journal = {NeuroImage},
  volume = {142},
  pages = {14--26},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2015.04.052},
  urldate = {2024-11-15},
  abstract = {Connectome mapping using techniques such as functional magnetic resonance imaging (fMRI) has become a focus of systems neuroscience. There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series. One key statistic for any time series is its (effective) degrees of freedom, df, which will generally be less than the number of time points (or nominal degrees of freedom, N). If we know the df, then probabilistic inference on other fMRI statistics, such as the correlation between two voxel or regional time series, is feasible. However, we currently lack good estimators of df in fMRI time series, especially after the degrees of freedom of the ``raw'' data have been modified substantially by denoising algorithms for head movement. Here, we used a wavelet-based method both to denoise fMRI data and to estimate the (effective) df of the denoised process. We show that seed voxel correlations corrected for locally variable df could be tested for false positive connectivity with better control over Type I error and greater specificity of anatomical mapping than probabilistic connectivity maps using the nominal degrees of freedom. We also show that wavelet despiked statistics can be used to estimate all pairwise correlations between a set of regional nodes, assign a P value to each edge, and then iteratively add edges to the graph in order of increasing P. These probabilistically thresholded graphs are likely more robust to regional variation in head movement effects than comparable graphs constructed by thresholding correlations. Finally, we show that time-windowed estimates of df can be used for probabilistic connectivity testing or dynamic network analysis so that apparent changes in the functional connectome are appropriately corrected for the effects of transient noise bursts. Wavelet despiking is both an algorithm for fMRI time series denoising and an estimator of the (effective) df of denoised fMRI time series. Accurate estimation of df offers many potential advantages for probabilistically thresholding functional connectivity and network statistics tested in the context of spatially variant and non-stationary noise. Code for wavelet despiking, seed correlational testing and probabilistic graph construction is freely available to download as part of the BrainWavelet Toolbox at www.brainwavelet.org.},
  keywords = {Connectivity,Degrees of freedom,Despiking,fMRI,Graph theory,Inference,Probabilistic,Statistic,Wavelet despike},
  file = {/home/foranw/Zotero/storage/D8TVQSNV/Patel and Bullmore - 2016 - A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic t.pdf;/home/foranw/Zotero/storage/JY8NNT2J/S1053811915003523.html}
}

@article{yushkevichUserguided3DActive2006,
  title = {User-Guided {{3D}} Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability},
  shorttitle = {User-Guided {{3D}} Active Contour Segmentation of Anatomical Structures},
  author = {Yushkevich, Paul A. and Piven, Joseph and Hazlett, Heather Cody and Smith, Rachel Gimpel and Ho, Sean and Gee, James C. and Gerig, Guido},
  year = {2006},
  month = jul,
  journal = {Neuroimage},
  volume = {31},
  number = {3},
  pages = {1116--1128},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2006.01.015},
  abstract = {Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.},
  langid = {english},
  pmid = {16545965},
  keywords = {Brain,Caudate Nucleus,Dominance Cerebral,Humans,Image Processing Computer-Assisted,Imaging Three-Dimensional,Magnetic Resonance Imaging,Mathematical Computing,Software,Software Validation,User-Computer Interface},
  file = {/home/foranw/Zotero/storage/3BKKET33/Yushkevich et al. - 2006 - User-guided 3D active contour segmentation of anatomical structures significantly improved efficien.pdf}
}