text:
},
doi = {10.1119/1.19329},
issn = {00029505},
journal = {American Journal of Physics},
number = {7},
title = {{Measuring the electrical conductivity of the earth}},
volume = {67},
year = {1999}
}
@article{Avants2006b,
abstract = {We develop a novel Lagrangian reference frame diffeomorphic image and landmark registration method. The algorithm uses the fixed Langrangian reference frame to define the map between coordinate systems, but also generates and stores the inverse map from the Eulerian to the Lagrangian frame. Computing both maps allows facile computation of both Eulerian and Langrangian quantities. We apply this algorithm to estimating a putative evolutionary change of coordinates between a population of chimpanzee and human cortices. Inter-species functional homologues fix the map explicitly, where they are known, while image similarities guide the alignment elsewhere. This map allows detailed study of the volumetric change between chimp and human cortex. Instead of basing the inter-species study on a single species atlas, we diffeomorphically connect the mean shape and intensity templates for each group. The human statistics then map diffeomorphically into the space of the chimpanzee cortex providing a comparison between species. The population statistics show a significant doubling of the relative prefrontal lobe size in humans, as compared to chimpanzees. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
author = {Avants, B.B. and Schoenemann, P.T. and Gee, J.C.},
doi = {10.1016/j.media.2005.03.005},
issn = {13618415},
journal = {Medical Image Analysis},
keywords = {Deformable image registration,Diffeomorphic,Evolution,Morphometry,Primate cortex},
number = {3 SPEC. ISS.},
title = {{Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex}},
volume = {10},
year = {2006}
}
@article{Rohlfing2012,
abstract = {Background: Image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing  {\&}  Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081-2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map. Results: Using data obtained from its authors, we show experimentally that the method proposed by Xing  {\&}  Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations. Conclusions: This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities. {\textcopyright} 2012 Rohlfing and Avants; licensee BioMed Central Ltd.},
author = {Rohlfing, T. and Avants, B.},
doi = {10.1186/1756-0500-5-610},
issn = {17560500},
journal = {BMC Research Notes},
keywords = {Accuracy,Correspondence,Image registration},
title = {{"Nonparametric Local Smoothing" is not image registration}},
volume = {5},
year = {2012}
}
@article{Legant2016,
abstract = {{\textcopyright} 2016 Nature America, Inc. All rights reserved. Extending three-dimensional (3D) single-molecule localization microscopy away from the coverslip and into thicker specimens will greatly broaden its biological utility. However, because of the limitations of both conventional imaging modalities and conventional labeling techniques, it is a challenge to localize molecules in three dimensions with high precision in such samples while simultaneously achieving the labeling densities required for high resolution of densely crowded structures. Here we combined lattice light-sheet microscopy with newly developed, freely diffusing, cell-permeable chemical probes with targeted affinity for DNA, intracellular membranes or the plasma membrane. We used this combination to perform high-localization precision, ultrahig h-labeling density, multicolor localization microscopy in samples up to 20 $\mu$m thick, including dividing cells and the neuromast organ of a zebrafish embryo. We also demonstrate super-resolution correlative imaging with protein-specific photoactivable fluorophores, providing a mutually compatible, single-platform alternative to correlative light-electron microscopy over large volumes.},
author = {Legant, W.R. and Shao, L. and Grimm, J.B. and Brown, T.A. and Milkie, D.E. and Avants, B.B. and Lavis, L.D. and Betzig, E.},
doi = {10.1038/nmeth.3797},
issn = {15487105},
journal = {Nature Methods},
number = {4},
title = {{High-density three-dimensional localization microscopy across large volumes}},
volume = {13},
year = {2016}
}
@article{Avants2010c,
abstract = {We use a new, unsupervised multivariate imaging and analysis strategy to identify related patterns of reduced white matter integrity, measured with the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI), and decreases in cortical thickness, measured by high resolution T1-weighted imaging, in Alzheimer's disease (AD) and frontotemporal dementia (FTD). This process is based on a novel computational model derived from sparse canonical correlation analysis (SCCA) that allows us to automatically identify mutually predictive, distributed neuroanatomical regions from different imaging modalities. We apply the SCCA model to a dataset that includes 23 control subjects that are demographically matched to 49 subjects with autopsy or CSF-biomarker-diagnosed AD (n = 24) and FTD (n = 25) with both DTI and T1-weighted structural imaging. SCCA shows that the FTD-related frontal and temporal degeneration pattern is correlated across modalities with permutation corrected p  {\textless}  0.0005. In AD, we find significant association between cortical thinning and reduction in white matter integrity within a distributed parietal and temporal network (p  {\textless}  0.0005). Furthermore, we show that-within SCCA identified regions-significant differences exist between FTD and AD cortical-connective degeneration patterns. We validate these distinct, multimodal imaging patterns by showing unique relationships with cognitive measures in AD and FTD. We conclude that SCCA is a potentially valuable approach in image analysis that can be applied productively to distinguishing between neurodegenerative conditions. {\textcopyright} 2010 Elsevier Inc. All rights reserved.},
author = {Avants, B.B. and Cook, P.A. and Ungar, L. and Gee, J.C. and Grossman, M.},
doi = {10.1016/j.neuroimage.2010.01.041},
issn = {10538119},
journal = {NeuroImage},
keywords = {AD,Canonical correlation,Correlation,Cortical thickness,Dementia,Diffusion tensor,FTD,Multivariate},
number = {3},
title = {{Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis}},
volume = {50},
year = {2010}
}
@article{Rao2007,
abstract = {OBJECTIVES. Animal studies have clearly demonstrated the effects of in utero cocaine exposure on neural ontogeny, especially in dopamine-rich areas of cerebral cortex; however, less is known about how in utero cocaine exposure affects longitudinal neurocognitive development of the human brain. We used continuous arterial spin-labeling perfusion functional MRI to measure the effect of in utero cocaine exposure on resting brain function by comparing resting cerebral blood flow of cocaine-exposed adolescents with non-cocaine-exposed control subjects. PATIENTS AND METHODS. Twenty-four cocaine-exposed adolescents and 25 matched non-cocaine-exposed control subjects underwent structural and perfusion functional MRI during resting states. Direct subtraction, voxel-wise general linear modeling, and region-of-interest analyses were performed on the cerebral blood flow images to compare the resting cerebral blood flow between the 2 groups. RESULTS. Compared with control subjects, cocaine-exposed adolescents showed significantly reduced global cerebral blood flow. The decrease of cerebral blood flow in cocaine-exposed adolescents was observed mainly in posterior and inferior brain regions, including the occipital cortex and thalamus. After adjusting for global cerebral blood flow, however, a significant increase in relative cerebral blood flow in cocaine-exposed adolescents was found in anterior and superior brain regions, including the prefrontal, cingulate, insular, amygdala, and superior parietal cortex. Furthermore, the functional modulations by in utero cocaine exposure on all of these regions except amygdala cannot be accounted for by the variation in brain anatomy. CONCLUSIONS. In utero cocaine exposure may reduce global cerebral blood flow, and this reduction may persist into adolescence. The relative increase of cerebral blood flow in anterior and superior brain regions in cocaine-exposed adolescent participants suggests that compensatory mechanisms for reduced global cerebral blood flow may develop during neural ontogeny. Arterial spin-labeling perfusion MRI may be a valuable tool for investigating the long-term effects of in utero drug exposure. Copyright {\textcopyright} 2007 by the American Academy of Pediatrics.},
author = {Rao, H. and Wang, J. and Giannetta, J. and Korczykowski, M. and Shera, D. and Avants, B.B. and Gee, J. and Detre, J.A. and Hurt, H.},
doi = {10.1542/peds.2006-2596},
issn = {00314005},
journal = {Pediatrics},
keywords = {ASL perfusion fMRI,Adolescence,Cerebral blood flow,Frontal lobe,In utero cocaine exposure},
number = {5},
title = {{Altered resting cerebral blood flow in adolescents with in utero cocaine exposure revealed by perfusion functional MRI}},
volume = {120},
year = {2007}
}
@article{Farag2010,
abstract = {The present study examined the organization of complex familiar activities, known as "scripts" (e.g., "going fishing"). We assessed whether events in a script are processed in a linear-sequential manner or clustered-hierarchical manner, and we evaluated the neural basis for this processing capacity. Converging evidence was obtained from functional neuroimaging in healthy young adults and from behavioral and structural magnetic resonance imaging (MRI) data in patients with focal neurodegenerative disease. In both studies, participants judged the order of consecutive event pairs taken from a script. Event pairs either were clustered together within a script or were from different clusters within the script. Controls judged events more accurately and quickly if taken from the same cluster within a script compared with different clusters, even though all event pairs were consecutive, consistent with the hierarchical organization of a script. Functional magnetic resonance imaging associated this with bilateral inferior frontal activation. Patients with progressive nonfluent aphasia or behavior-variant frontotemporal dementia did not distinguish between event pairs from the same cluster or from different clusters within a script. Structural MRI associated this deficit with significant frontal cortical atrophy. Our findings suggest that frontal cortex contributes to clustering events during script comprehension, underlining the role of frontal cortex in the hierarchical organization of a script. {\textcopyright} The Authors 2010.},
author = {Farag, C. and Troiani, V. and Bonner, M. and Powers, C. and Avants, B. and Gee, J. and Grossman, M.},
doi = {10.1093/cercor/bhp313},
issn = {10473211},
journal = {Cerebral Cortex},
keywords = {fMRI,frontal,frontotemporal dementia,narrative,organization},
number = {10},
title = {{Hierarchical organization of scripts: Converging evidence from fmri and frontotemporal degeneration}},
volume = {20},
year = {2010}
}
@article{Pustina2017a,
abstract = {{\textcopyright} 2017 Elsevier Ltd. Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N = 20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.},
author = {Pustina, D. and Avants, B. and Faseyitan, O.K. and Medaglia, J.D. and Coslett, H.B.},
doi = {10.1016/j.neuropsychologia.2017.08.027},
issn = {18733514},
journal = {Neuropsychologia},
keywords = {Aphasia,Brunner,Motor,Nonparametric,SCCAN,Sparse,Stroke},
title = {{Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations}},
year = {2017}
}
@article{Kim2010,
abstract = {Non-invasive measurement of resting state cerebral blood flow (CBF) may reflect alterations of brain structure and function after traumatic brain injury (TBI). However, previous imaging studies of resting state brain in chronic TBI have been limited by several factors, including measurement in relative rather than absolute units, use of crude spatial registration methods, exclusion of subjects with substantial focal lesions, and exposure to ionizing radiation, which limits repeated assessments. This study aimed to overcome those obstacles by measuring absolute CBF with an arterial spin labeling perfusion fMRI technique, and using an image preprocessing protocol that is optimized for brains with mixed diffuse and focal injuries characteristic of moderate and severe TBI. Resting state CBF was quantified in 27 individuals with moderate to severe TBI in the chronic stage, and 22 demographically matched healthy controls. In addition to global CBF reductions in the TBI subjects, more prominent regional hypoperfusion was found in the posterior cingulate cortices, the thalami, and multiple locations in the frontal cortices. Diffuse injury, as assessed by tensor-based morphometry, was mainly associated with reduced CBF in the posterior cingulate cortices and the thalami, where the greatest volume losses were detected. Hypoperfusion in superior and middle frontal cortices, in contrast, was associated with focal lesions. These results suggest that structural lesions, both focal and diffuse, are the main contributors to the absolute CBF alterations seen in chronic TBI, and that CBF may serve as a tool to assess functioning neuronal volume. We also speculate that resting reductions in posterior cingulate perfusion may reflect alterations in the default-mode network, and may contribute to the attentional deficits common in TBI. Copyright 2010, Mary Ann Liebert, Inc.},
author = {Kim, J. and Whyte, J. and Patel, S. and Avants, B. and Europa, E. and Wang, J. and Slattery, J. and Gee, J.C. and Coslett, H.B. and Detre, J.A.},
doi = {10.1089/neu.2009.1215},
issn = {08977151},
journal = {Journal of Neurotrauma},
keywords = {arterial spin labeling,cerebral blood flow,lesion,magnetic resonance imaging,traumatic brain injury},
number = {8},
title = {{Resting cerebral blood flow alterations in chronic traumatic brain injury: An arterial spin labeling perfusion fMRI study}},
volume = {27},
year = {2010}
}
@article{Wang2010a,
abstract = {We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.},
author = {Wang, H. and Das, S. and Pluta, J. and Craige, C. and Altinay, M. and Avants, B. and Weiner, M. and Mueller, S. and Yushkevich, P.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 3},
title = {{Standing on the shoulders of giants: improving medical image segmentation via bias correction.}},
volume = {13},
year = {2010}
}
@inproceedings{dhillon_icml12_tscca,
author = {{Paramveer S. Dhillon Jordan Rodu}, Dean P Foster and Ungar, Lyle H},
booktitle = {Proceedings of the 29th International Conference on Machine learning},
series = {ICML'12},
title = {{Two Step CCA: A new spectral method for estimating vector models of words}},
year = {2012}
}
@book{Avants2006c,
abstract = {Medical image analysis based on diffeomorphisms (differentiable one to one and onto maps with differentiable inverse) has placed computational analysis of anatomy and physiology on firm theoretical ground. We detail our approach to diffeomorphic computational anatomy while highlighting both theoretical and practical benefits. We first introduce the metric used to locate geodesics in the diffeomorphic space. Second, we give a variational energy that parameterizes the image normalization problem in terms of a geodesic diffeomorphism, enabling a fundamentally symmetric solution. This approach to normalization is extended for optimal template population studies using general imaging data. Finally, we show how the temporal parameterization and large deformation capabilities of diffeomorphisms make them appropriate for longitudinal analysis, particularly of neurodegenerative data. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
author = {Avants, B.B. and Epstein, C.L. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
isbn = {3540372202},
issn = {16113349},
title = {{Geodesic image normalization and temporal parameterization in the space of diffeomorphisms}},
volume = {4091 LNCS},
year = {2006}
}
@book{Avants2012a,
abstract = {{\textcopyright} Springer-Verlag Berlin Heidelberg 2012. We contribute a novel and interpretable dimensionality reduction strategy, eigenanatomy, that is tuned for neuroimaging data. The method approximates the eigendecomposition of an image set with basis functions (the eigenanatomy vectors) that are sparse, unsigned and are anatomically clustered. We employ the eigenanatomy vectors as anatomical predictors to improve detection power in morphometry. Standard voxel-based morphometry (VBM) analyzes imaging data voxel-by-voxel—and follows this with cluster-based or voxel-wise multiple comparisons correction methods to determine significance. Eigenanatomy reverses the standard order of operations by first clustering the voxel data and then using standard linear regression in this reduced dimensionality space. As with traditional region-of-interest (ROI) analysis, this strategy can greatly improve detection power. Our results show that eigenanatomy provides a principled objective function that leads to localized, data-driven regions of interest. These regions improve our ability to quantify biologically plausible rates of cortical change in two distinct forms of neurodegeneration. We detail the algorithm and show experimental evidence of its efficacy.},
author = {Avants, B. and Dhillon, P. and Kandel, B.M. and Cook, P.A. and McMillan, C.T. and Grossman, M. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
isbn = {9783642334535},
issn = {16113349},
title = {{Eigenanatomy improves detection power for longitudinal cortical change}},
volume = {7512 LNCS},
year = {2012}
}
@article{Ghosh2012,
abstract = {Peer-reviewed publications are the primary mechanism for sharing scientific results. The current peer-review process is, however, fraught with many problems that undermine the pace, validity, and credibility of science. We highlight five salient problems: (1) reviewers are expected to have comprehensive expertise; (2) reviewers do not have sufficient access to methods and materials to evaluate a study; (3) reviewers are neither identified nor acknowledged; (4) there is no measure of the quality of a review; and (5) reviews take a lot of time, and once submitted cannot evolve. We propose that these problems can be resolved by making the following changes to the review process. Distributing reviews to many reviewers would allow each reviewer to focus on portions of the article that reflect the reviewer's specialty or area of interest and place less of a burden on any one reviewer. Providing reviewers materials and methods to perform comprehensive evaluation would facilitate transparency, greater scrutiny, and replication of results. Acknowledging reviewers makes it possible to quantitatively assess reviewer contributions, which could be used to establish the impact of the reviewer in the scientific community. Quantifying review quality could help establish the importance of individual reviews and reviewers as well as the submitted article. Finally, we recommend expediting post-publication reviews and allowing for the dialogue to continue and flourish in a dynamic and interactive manner. We argue that these solutions can be implemented by adapting existing features from open-source software management and social networking technologies. We propose a model of an open, interactive review system that quantifies the significance of articles, the quality of reviews and the reputation of reviewers. {\textcopyright} 2012 Ghosh, Klein, Avants and Millman.},
author = {Ghosh, S.S. and Klein, A. and Avants, B. and {Jarrod Millman}, K.},
doi = {10.3389/fncom.2012.00018},
issn = {16625188},
journal = {Frontiers in Computational Neuroscience},
keywords = {Code review systems,Distributed peer review,Open source software development,Post-publication peer review,Reputation assessment,Review quality},
number = {MARCH 2012},
title = {{Learning from open source software projects to improve scientific review}},
year = {2012}
}
@book{Song2006,
abstract = {Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
author = {Song, Z. and Tustison, N. and Avants, B. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
isbn = {354044727X},
issn = {16113349},
title = {{Integrated graph cuts for brain MRI segmentation}},
volume = {4191 LNCS },
year = {2006}
}
@inproceedings{Tustison2013b,
abstract = {The recent discovery of methodological aws in experimental design and analysis in neuroscience research has raised concerns over the validity of certain techniques used in routine analyses and their corresponding findings. Such concerns have centered around selection bias whereby data is inadvertently manipulated such that the resulting analysis produces falsely increased statistical significance, i.e. type I errors. This has been illustrated recently in fMRI studies, 1 with excessive exibility in data collection, 2 and general experimental design issues. 3 Current work from our group has shown how this problem extends to generic voxel-based analysis (and certain technique derivatives such as tract-based spatial statistics 4 ) using fractional anisotropy images derived from diffusion tensor imaging. 5 In this work, we demonstrate how this circularity principle can potentially extend to the well-known optimized voxel-based morphometry technique6 for assessing cortical density differences whereby the principal cause of experimental corruption is due to normalization strategy. Specifically, the popular sum- of-squared-differences (SSD) metric explicitly optimizes statistical findings potentially inating type I errors. Additional experimentation demonstrates that this problem is not restricted to the SSD metric but extends to other commonly used metrics such as mutual information, neighborhood cross correlation, and Demons. {\textcopyright} 2013 SPIE.},
author = {Tustison, N.J. and Avants, B.B. and Cook, P.A. and Gee, J.C. and Stone, J.R.},
booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},
doi = {10.1117/12.2006377},
isbn = {9780819494467},
issn = {16057422},
keywords = {Circularity,Cortical thickness,Normalization},
title = {{Statistical bias in optimized VBM}},
volume = {8672},
year = {2013}
}
@book{Cook2005,
abstract = {We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres in vivo, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
author = {Cook, P.A. and Zhang, H. and Avants, B.B. and Yushkevich, P. and Alexander, B.C. and Gee, J.C. and Ciccarelli, O. and Thompson, A.J.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
isbn = {3540293272},
issn = {03029743},
title = {{An automated approach to connectivity-based partitioning of brain structures}},
volume = {3749 LNCS},
year = {2005}
}
@article{Avants2015b,
author = {Avants, B.B. and Johnson, H.J. and Tustison, N.J.},
doi = {10.3389/fninf.2015.00005},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {C++,ITK,Open source,Registration,Segmentation},
number = {MAR},
title = {{Neuroinformatics and the the insight toolkit}},
volume = {9},
year = {2015}
}
@book{Yushkevich2006,
abstract = {The Allen Brain Atlas project aims to bridge the divide between genomics and neuroanatomy by mapping the expression of the entire C57BL/6J mouse genome onto a high-resolution 3D anatomical reference atlas of the mouse brain. We present the image registration approach used to generate this anatomical reference from histological sections. Due to the large number of sections (525) and the presence of debris and distortions, a straightforward alignment of each slice to its neighbors fails to accurately recover the 3D shape of the brain. On the other hand, multimodality registration of histology slices to an MRI reference compromises correspondences between neighboring slices. Our approach combines the high-frequency component of slice-to-slice histology registration with the low-frequency component of the histology-to-MRI registration to produce a coarse-to-fine reconstruction that is accurate both in its global shape and in the alignment of local features. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
author = {Yushkevich, P.A. and Avants, B.B. and Ng, L. and Hawrylycz, M. and Burstein, P.D. and Zhang, H. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/11784012_28},
isbn = {3540356487},
issn = {16113349},
title = {{3D mouse brain reconstruction from histology using a coarse-to-fine approach}},
volume = {4057 LNCS},
year = {2006}
}
@article{Cook2014,
abstract = {Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network comp onents that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal-occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis. {\textcopyright} 2014 Elsevier Inc.},
author = {Cook, P.A. and McMillan, C.T. and Avants, B.B. and Peelle, J.E. and Gee, J.C. and Grossman, M.},
doi = {10.1016/j.neuroimage.2014.05.008},
issn = {10959572},
journal = {NeuroImage},
keywords = {FTD,Language,Multimodal,Verbal fluency},
title = {{Relating brain anatomy and cognitive ability using a multivariate multimodal framework}},
volume = {99},
year = {2014}
}
@article{McMillan2013a,
abstract = {Objective: Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD) may have overlapping clinical presentations despite distinct underlying neuropathologies, thus making in vivo diagnosis challenging. In this study, we evaluate the utility of MRI as a noninvasive screening procedure for the differential diagnosis of AD and FTLD. Methods: We recruited 185 patients with a clinically diagnosed neurodegenerative disease consistent with AD or FTLD who had a lumbar puncture and a volumetricMRI. A subset of 32 patients had genetic or autopsy-confirmed AD or FTLD. We used singular value decomposition to decompose MRI volumes and linear regression and cross-validation to predict CSF total tau (tt) and b-amyloid (A$\beta$ 1-42 ) ratio (tt/A$\beta$) in patients with AD and patients with FTLD. We then evaluated accuracy of MRI-based predicted tt/A$\beta$ using 4 converging sources including neuroanatomic visualization and categorization of a subset of patients with genetic or autopsy-confirmed AD or FTLD. Results: Regression analyses showed that MRI-predicted tt/A$\beta$ is highly related to actual CSF tt/A$\beta$. In each group, both predicted and actual CSF tt/A$\beta$ have extensively overlapping neuroanatomic correlates: low tt/A$\beta$ consistent with FTLD is related to ventromedial prefrontal regions while high tt/A$\beta$ consistent with AD is related to posterior cortical regions. MRI-predicted tt/A$\beta$ is 75{\%} accurate at identifying underlying diagnosis in patients with known pathology and in clinically diagnosed patients with known CSF tt/A$\beta$ levels. Conclusion: MRI may serve as a noninvasive procedure that can screen for AD and FTLD pathology as a surrogate for CSF biomarkers. {\textcopyright} 2013 American Academy of Neurology.},
author = {McMillan, C.T. and Avants, B. and Irwin, D.J. and Toledo, J.B. and Wolk, D.A. and {Van Deerlin}, V.M. and Shaw, L.M. and Trojanoswki, J.Q. and Grossman, M.},
doi = {10.1212/WNL.0b013e31827b9147},
issn = {00283878},
journal = {Neurology},
number = {2},
title = {{Can MRI screen for CSF biomarkers in neurodegenerative disease?}},
volume = {80},
year = {2013}
}
@inproceedings{Kim2011,
abstract = {The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children. {\textcopyright} 2011 IEEE.},
author = {Kim, S.-G. and Chung, M.K. and Hanson, J.L. and Avants, B.B. and Gee, J.C. and Davidson, R.J. and Pollak, S.D.},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2011.5872528},
isbn = {9781424441280},
issn = {19457928},
keywords = {Jacobian determinant,brain network,maltreatment,structural connectivity,tensor-based morphometry},
title = {{Structural connectivity via the tensor-based morphometry}},
year = {2011}
}
@article{Hanson2012,
abstract = {Here, we describe a novel method for volumetric segmentation of the amygdala from MRI images collected from 35 human subjects.This approach is adapted from open-source techniques employed previously with the hippocampus (Suh et al., 2011;Wang et al., 2011a,b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high Dice (Mean=0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean=0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class correlations (consistency=0.830, absolute agreement=0.819 for the left; consistency=0.786, absolute agreement=0.783 for the right) and bivariate (r =0.831 for the left; r =0.797 for the right) compared to handdrawn amygdala. Our results are discussed in relation to other cutting-edge segmentation techniques, as well as commonly available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed. {\textcopyright} 2012 Hanson, Suh, Nacewicz, Sutterer, Cayo, Stodola, Burghy, Wang, Avants, Yushkevich, Essex, Pollak and Davidson.},
author = {Hanson, J.L. and Suh, J.W. and Nacewicz, B.M. and Sutterer, M.J. and Cayo, A.A. and Stodola, D.E. and Burghy, C.A. and Wang, H. and Avants, B.B. and Yushkevich, P.A. and Essex, M.J. and Pollak, S.D. and Davidson, R.J.},
doi = {10.3389/fnins.2012.00166},
issn = {16624548},
journal = {Frontiers in Neuroscience},
keywords = {Amygdala,Amygdala volume,Automated segmentation,Diffeomorphic warping,Freesurfer,Hand-tracing,Medial temporal lobe,Structural MRI},
number = {NOV},
title = {{Robust automated amygdala segmentation via multi-atlas diffeomorphic registration}},
year = {2012}
}
@inproceedings{Avants2002,
abstract = {We develop a softassign method for application to curve matching. Softassign uses deterministic annealing to iteratively optimize the parameters of an energy function. It also incorporates outlier rejection by converting the energy into a stochastic matrix with entries for rejection probability. Previous applications of the method focused on finding transformations between unordered point sets. Thus, no topological constraints were required. In our application, we must consider the topology of the matching between the reference and the target curve. Our energy function also depends upon the rotation and scaling between the curves. Thus, we develop a topologically correct algorithm to update the are length correspondence, which is then used to update the similarity transformation. We further enhance robustness by using a scale-space description of the curves. This results in a curve-matching tool that, given an approximate initialization, is invariant to similarity transformations. We demonstrate the reliability of the technique by applying it to open and closed curves extracted from real patient images (cortical sulci in three dimensions and corpora callosa in two dimensions). The set of transformations is then used to compute anatomical atlases.},
author = {Avants, B. and Gee, J.},
booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
doi = {10.1117/12.467071},
issn = {0277786X},
keywords = {Anatomical atlases,Curve matching,Deterministic annealing,Dynamic programming,Non-linear deformations,Softassign},
title = {{Soft parametric curve matching in scale-space}},
volume = {4684 II},
year = {2002}
}
@article{Tustison:2006aa,
abstract = {We present research in which both left and right ventricular deformation is estimated from tagged cardiac magnetic resonance imaging using volumetric deformable models constructed from nonuniform rational B-splines (NURBS). The four model types considered and compared for the left ventricle include two Cartesian NURBS models--one with a cylindrical parameter assignment and one with a prolate spheroidal parameter assignment. The remaining two are non-Cartesian, i.e., prolate spheroidal and cylindrical each with their respective prolate spheroidal and cylindrical parameter assignment regimes. These choices were made based on the typical shape of the left ventricle. For each frame starting with end-diastole, a NURBS model is constructed by fitting two surfaces with the same parameterization to the corresponding set of epicardial and endocardial contours from which a volumetric model is created. Using normal displacements of the three sets of orthogonal tag planes as well as displacements of contour/tag line intersection points and tag plane intersection points, one can solve for the optimal homogeneous coordinates, in a weighted least squares sense, of the control points of the deformed NURBS model at end-diastole using quadratic programming. This allows for subsequent nonrigid registration of the biventricular model at end-diastole to all later time frames. After registration of the model to all later time points, the registered NURBS models are temporally lofted in order to create a comprehensive four-dimensional NURBS model. From the lofted model, we can extract three-dimensional myocardial deformation fields and corresponding Lagrangian and Eulerian strain maps which are local measures of nonrigid deformation. The results show that, in the case of simulated data, the quadratic Cartesian NURBS models with the cylindrical and prolate spheroidal parameter assignments outperform their counterparts in predicting normal strain. The decreased complexity associated with the Cartesian model with the cylindrical parameter assignment prompted its use for subsequent calculations. Lagrangian strains in three canine data, a normal human, and a patient with history of myocardial infarction are presented. Eulerian strains for the normal human data are also included.},
author = {Tustison, Nicholas J and Amini, Amir A},
doi = {10.1109/TMI.2005.861015},
journal = {IEEE Trans Med Imaging},
month = {jan},
number = {1},
pages = {94--112},
pmid = {16398418},
title = {{Biventricular myocardial strains via nonrigid registration of anatomical {\{}NURBS{\}} model [corrected]}},
volume = {25},
year = {2006}
}
@article{Wray2018,
abstract = {Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.},
author = {Wray, Naomi R. and Ripke, Stephan and Mattheisen, Manuel and Trzaskowski, MacIej and Byrne, Enda M. and Abdellaoui, Abdel and Adams, Mark J. and Agerbo, Esben and Air, Tracy M. and Andlauer, Till M.F. and Bacanu, Silviu Alin and B{\ae}kvad-Hansen, Marie and Beekman, Aartjan F.T. and Bigdeli, Tim B. and Binder, Elisabeth B. and Blackwood, Douglas R.H. and Bryois, Julien and Buttensch{\o}n, Henriette N. and Bybjerg-Grauholm, Jonas and Cai, Na and Castelao, Enrique and Christensen, Jane Hvarregaard and Clarke, Toni Kim and Coleman, Jonathan I.R. and Colodro-Conde, Luc{\'{i}}a and Couvy-Duchesne, Baptiste and Craddock, Nick and Crawford, Gregory E. and Crowley, Cheynna A. and Dashti, Hassan S. and Davies, Gail and Deary, Ian J. and Degenhardt, Franziska and Derks, Eske M. and DIrek, Nese and Dolan, Conor V. and Dunn, Erin C. and Eley, Thalia C. and Eriksson, Nicholas and Escott-Price, Valentina and Kiadeh, Farnush Hassan Farhadi and Finucane, Hilary K. and Forstner, Andreas J. and Frank, Josef and Gaspar, H{\'{e}}l{\'{e}}na A. and Gill, Michael and Giusti-Rodr{\'{i}}guez, Paola and Goes, Fernando S. and Gordon, Scott D. and Grove, Jakob and Hall, Lynsey S. and Hannon, Eilis and Hansen, Christine S{\o}holm and Hansen, Thomas F. and Herms, Stefan and Hickie, Ian B. and Hoffmann, Per and Homuth, Georg and Horn, Carsten and Hottenga, Jouke Jan and Hougaard, David M. and Hu, Ming and Hyde, Craig L. and Ising, Marcus and Jansen, Rick and Jin, Fulai and Jorgenson, Eric and Knowles, James A. and Kohane, Isaac S. and Kraft, Julia and Kretzschmar, Warren W. and Krogh, Jesper and Kutalik, Zolt{\'{a}}n and Lane, Jacqueline M. and Li, Yihan and Li, Yun and Lind, Penelope A. and Liu, Xiaoxiao and Lu, Leina and MacIntyre, Donald J. and MacKinnon, Dean F. and Maier, Robert M. and Maier, Wolfgang and Marchini, Jonathan and Mbarek, Hamdi and McGrath, Patrick and McGuffin, Peter and Medland, Sarah E. and Mehta, DIvya and Middeldorp, Christel M. and Mihailov, Evelin and Milaneschi, Yuri and Milani, Lili and Mill, Jonathan and Mondimore, Francis M. and Montgomery, Grant W. and Mostafavi, Sara and Mullins, Niamh and Nauck, Matthias and Ng, Bernard and Nivard, Michel G. and Nyholt, Dale R. and O'Reilly, Paul F. and Oskarsson, Hogni and Owen, Michael J. and Painter, Jodie N. and Pedersen, Carsten B{\o}cker and Pedersen, Marianne Gi{\o}rtz and Peterson, Roseann E. and Pettersson, Erik and Peyrot, Wouter J. and Pistis, Giorgio and Posthuma, Danielle and Purcell, Shaun M. and Quiroz, Jorge A. and Qvist, Per and Rice, John P. and Riley, Brien P. and Rivera, Margarita and {Saeed Mirza}, Saira and Saxena, Richa and Schoevers, Robert and Schulte, Eva C. and Shen, Ling and Shi, Jianxin and Shyn, Stanley I. and Sigurdsson, Engilbert and Sinnamon, Grant B.C. and Smit, Johannes H. and Smith, Daniel J. and Stefansson, Hreinn and Steinberg, Stacy and Stockmeier, Craig A. and Streit, Fabian and Strohmaier, Jana and Tansey, Katherine E. and Teismann, Henning and Teumer, Alexander and Thompson, Wesley and Thomson, Pippa A. and Thorgeirsson, Thorgeir E. and Tian, Chao and Traylor, Matthew and Treutlein, Jens and Trubetskoy, Vassily and Uitterlinden, Andr{\'{e}} G. and Umbricht, Daniel and {Van Der Auwera}, Sandra and {Van Hemert}, Albert M. and Viktorin, Alexander and Visscher, Peter M. and Wang, Yunpeng and Webb, Bradley T. and Weinsheimer, Shantel Marie and Wellmann, J{\"{u}}rgen and Willemsen, Gonneke and Witt, Stephanie H. and Wu, Yang and Xi, Hualin S. and Yang, Jian and Zhang, Futao and Arolt, Volker and Baune, Bernhard T. and Berger, Klaus and Boomsma, Dorret I. and Cichon, Sven and Dannlowski, Udo and {De Geus}, E. C.J. and Depaulo, J. Raymond and Domenici, Enrico and Domschke, Katharina and Esko, T{\~{o}}nu and Grabe, Hans J. and Hamilton, Steven P. and Hayward, Caroline and Heath, Andrew C. and Hinds, David A. and Kendler, Kenneth S. and Kloiber, Stefan and Lewis, Glyn and Li, Qingqin S. and Lucae, Susanne and Madden, Pamela F.A. and Magnusson, Patrik K. and Martin, Nicholas G. and McIntosh, Andrew M. and Metspalu, Andres and Mors, Ole and Mortensen, Preben Bo and M{\"{u}}ller-Myhsok, Bertram and Nordentoft, Merete and N{\"{o}}then, Markus M. and O'Donovan, Michael C. and Paciga, Sara A. and Pedersen, Nancy L. and Penninx, Brenda W.J.H. and Perlis, Roy H. and Porteous, David J. and Potash, James B. and Preisig, Martin and Rietschel, Marcella and Schaefer, Catherine and Schulze, Thomas G. and Smoller, Jordan W. and Stefansson, Kari and Tiemeier, Henning and Uher, Rudolf and V{\"{o}}lzke, Henry and Weissman, Myrna M. and Werge, Thomas and Winslow, Ashley R. and Lewis, Cathryn M. and Levinson, Douglas F. and Breen, Gerome and B{\o}rglum, Anders D. and Sullivan, Patrick F.},
doi = {10.1038/s41588-018-0090-3},
isbn = {1804261300},
issn = {15461718},
journal = {Nature Genetics},
pmid = {29700475},
title = {{Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression}},
year = {2018}
}
@article{Avants2014b,
abstract = {This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer's disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, non-fluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data. {\textcopyright} 2013 Elsevier Inc.},
author = {Avants, B.B. and Libon, D.J. and Rascovsky, K. and Boller, A. and McMillan, C.T. and Massimo, L. and Coslett, H.B. and Chatterjee, A. and Gross, R.G. and Grossman, M.},
doi = {10.1016/j.neuroimage.2013.09.048},
issn = {10959572},
journal = {NeuroImage},
keywords = {Alzheimer disease,Frontotemporal lobar degeneration,MRI,PBAC,Philadelphia Brief Assessment of Cognition,Sparse canonical correlation analysis},
title = {{Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population}},
volume = {84},
year = {2014}
}
@article{Avants2003b,
abstract = {This work provides a new technique for surface oriented volumetric image analysis. The method makes no assumptions about topology, instead constructing a local neighborhood from image information, such as a segmentation or edge map, to define a surface patch. Neighborhood constructions using extrinsic and intrinsic distances are given. This representation allows one to estimate differential properties directly from the image's Gauss map. We develop a novel technique for this purpose which estimates the shape operator and yields both principal directions and curvatures. Only first derivatives need be estimated, making the method numerically stable. We show the use of these measures for multi-scale classification of image structure by the mean and Gaussian curvatures. Finally, we propose to register image volumes by surface curvature. This is particularly useful when geometry is the only variable. To illustrate this, we register binary segmented data by surface curvature, both rigidly and non-rigidly. A novel variant of Demons registration, extensible for use with differentiable similarity metrics, is also applied for deformable curvature-driven registration of medical images.},
author = {Avants, B. and Gee, J.},
issn = {10112499},
journal = {Information processing in medical imaging : proceedings of the ... conference},
title = {{The shape operator for differential analysis of images.}},
volume = {18},
year = {2003}
}
@article{Pierrefeu2018,
abstract = {Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., -dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach (such as GraphNet PCA) are significant, since SPCA-TV reveals the variability within a data set in the form of intelligible brain patterns that are easier to interpret and more stable across different samples.},
author = {de Pierrefeu, Amicie and Lofstedt, Tommy and Hadj-Selem, Fouad and Dubois, Mathieu and Jardri, Renaud and Fovet, Thomas and Ciuciu, Philippe and Frouin, Vincent and Duchesnay, Edouard},
doi = {10.1109/TMI.2017.2749140},
issn = {1558-254X},
journal = {IEEE transactions on medical imaging},
month = {feb},
number = {2},
pages = {396--407},
pmid = {28880163},
title = {{Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.}},
volume = {37},
year = {2018}
}
@inproceedings{Song2007,
abstract = {We propose an efficient image registration strategy that is based on learned prior distributions of transformation parameters. These priors are used to constrain a finite-time multi-start optimization method. Motivation for this approach comes from the fact that standard affine brain image registration methods, especially those based on gradient descent optimization alone, are affected by the initial search position. While global optimization methods can resolve this problem, they are are often very time consuming. Our goal is to build an explicit prior model of the gap between a typical registration solution and the solution gained by a global optimization method. We use this learned prior model to restrict randomized search in the relevant parameter space surrounding the initial solution. Global optimization in this restricted parameter space provides, in finite time, results that are superior to both gradient descent and the general multi-start strategy. The performance of our method is illustrated on a data set of 67 elderly and neurodegenerative brains. Our novel learning strategy and the associated registration method are shown to outperform other approaches. Theoretical, synthetic and real-world examples illustrate this improvement. {\textcopyright}2007 IEEE.},
author = {Song, G. and Avants, B.B. and Gee, J.C.},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2007.4409159},
title = {{Multi-start method with prior learning for image registration}},
year = {2007}
}
@article{Avants2014c,
abstract = {Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK 4 ) seeks to establish new standards in publicly available image registration methodology. ITK 4 makes several advances in comparison to previous versions of ITK. ITK 4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK 4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK 4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling. {\textcopyright} 2014 Avants, Tustison, Stauffer, Song, Wuand Gee.},
author = {Avants, B.B. and Tustison, N.J. and Stauffer, M. and Song, G. and Wu, B. and Gee, J.C.},
doi = {10.3389/fninf.2014.00044},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {Brain,Death,MRI,Open-source,Registration},
number = {APR},
title = {{The Insight ToolKit image registration framework}},
volume = {8},
year = {2014}
}
@book{Kandel2014,
abstract = {Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns. {\textcopyright} 2014 Springer International Publishing.},
author = {Kandel, B.M. and Wang, D.J.J. and Gee, J.C. and Avants, B.B.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-10443-0_18},
isbn = {9783319104423},
issn = {16113349},
number = {PART 3},
title = {{Single-subject structural networks with closed-form rotation invariant matching improve power in developmental studies of the cortex}},
volume = {8675 LNCS},
year = {2014}
}
@book{Yushkevich2008b,
abstract = {This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in postmortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01,mm 3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
author = {Yushkevich, P.A. and Avants, B.B. and Pluta, J. and Minkoff, D. and Detre, J.A. and Grossman, M. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-540-85988-8_61},
isbn = {354085987X},
issn = {03029743},
number = {PART 1},
title = {{Shape-based alignment of hippocampal subfields: evaluation in postmortem MRI}},
volume = {5241 LNCS},
year = {2008}
}
@inproceedings{Ronneberger2015,
author = {Ronneberger, O and Fischer, P and Brox, T},
booktitle = {Medical Image Computing and Computer-Assisted Intervention},
editor = {{Navab N. Hornegger J.}, Wells W Frangi A},
title = {{U-Net: Convolutional Networks for Biomedical Image Segmentation}},
volume = {9351},
year = {2015}
}
@article{Avants2010a,
abstract = {We evaluate the impact of template choice on template-based segmentation of the hippocampus in epilepsy. Four dataset-specific strategies are quantitatively contrasted: the "closest to average" individual template, the average shape version of the closest to average template, a best appearance template and the best appearance and shape template proposed here and implemented in the open source toolkit Advanced Normalization Tools (ANTS). The cross-correlation similarity metric drives the correspondence model and is used consistently to determine the optimal appearance. Minimum shape distance in the diffeomorphic space determines optimal shape. Our evaluation results show that, with respect to gold-standard manual labeling of hippocampi in epilepsy, optimal shape and appearance template construction outperforms the other strategies for gaining data-derived templates. Our results also show the improvement is most significant on the diseased side and insignificant on the healthy side. Thus, the importance of the template increases when used to study pathology and may be less critical for normal control studies. Furthermore, explicit ge ometric optimization of the shape component of the unbiased template positively impacts the study of diseased hippocampi. {\textcopyright} 2009 Elsevier Inc. All rights reserved.},
author = {Avants, B.B. and Yushkevich, P. and Pluta, J. and Minkoff, D. and Korczykowski, M. and Detre, J. and Gee, J.C.},
doi = {10.1016/j.neuroimage.2009.09.062},
issn = {10538119},
journal = {NeuroImage},
keywords = {Diffeomorphic Deformable image registration,Epilepsy,Human cortex,Template},
number = {3},
title = {{The optimal template effect in hippocampus studies of diseased populations}},
volume = {49},
year = {2010}
}
@article{Massimo2009,
abstract = {Objective: To investigate the neural basis for the behavioral symptoms of frontotemporal lobar degeneration (FTLD) that cause the greatest caregiver distress. Background: FTLD is a progressive neurodegenerative disease associated with behavioral disturbances. Group studies have related these behaviors to volume loss on MRI. Methods: Forty caregivers of patients with the clinical diagnosis of FTLD completed the Neuropsychiatric Inventory. Twelve neuropsychiatric symptoms and the associated caregiver distress were assessed. Optimized voxel-based morphometry identified significant atrophy in subgroups of FTLD patients with isolated behavioral symptoms corresponding to the most distressing behaviors, and we correlated cortical atrophy directly with these distressing behavioral disorders in an unbiased group analysis. Results: The greatest stressors for caregivers were apathy and disinhibition (p  {\textless}  0.005 for both contrasts). Partially distinct areas of cortical atrophy were associated with these behaviors in both individual patients with these symptoms and group-wide analyses, including the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in apathetic patients, and the medial orbital frontal cortex in disinhibited patients. Conclusions: Caregiver stress in families of FTLD patients is due in large part to apathy and disinhibition . The anatomic distribution of cortical loss corresponding to these distressing social behaviors includes partially distinct areas within the frontal lobe. Copyright {\textcopyright} 2009 S. Karger AG.},
author = {Massimo, L. and Powers, C. and Moore, P. and Vesely, L. and Avants, B. and Gee, J. and Libon, D.J. and Grossman, M.},
doi = {10.1159/000194658},
issn = {14208008},
journal = {Dementia and Geriatric Cognitive Disorders},
keywords = {Apathy,Disinhibition,Frontotemporal lobar degeneration,Magnetic resonance imaging,Neuropsychiatric inventory},
number = {1},
title = {{Neuroanatomy of apathy and disinhibition in frontotemporal lobar degeneration}},
volume = {27},
year = {2009}
}
@article{Betancourt2016,
abstract = {{\textcopyright} 2015 John Wiley  {\&}  Sons Ltd There is increasing interest in both the cumulative and long-term impact of early life adversity on brain structure and function, especially as the brain is both highly vulnerable and highly adaptive during childhood. Relationships between SES and neural development have been shown in children older than age 2 years. Less is known regarding the impact of SES on neural development in children before age 2. This paper examines the effect of SES, indexed by income-to-needs (ITN) and maternal education, on cortical gray, deep gray, and white matter volumes in term, healthy, appropriate for gestational age, African-American, female infants. At 5 weeks postnatal age, unsedated infants underwent MRI (3.0T Siemens Verio scanner, 32-channel head coil). Images were segmented based on a locally constructed template. Utilizing hierarchical linear regression, SES effects on MRI volumes were examined. In this cohort of healthy African-American female infants of varying SES, lower SES was associated with smaller cortical gray and deep gray matter volumes. These SES effects on neural outcome at such a young age build on similar studies of older children, suggesting that the biological embedding of adversity may occur very early in development.},
author = {Betancourt, L.M. and Avants, B. and Farah, M.J. and Brodsky, N.L. and Wu, J. and Ashtari, M. and Hurt, H.},
doi = {10.1111/desc.12344},
issn = {14677687},
journal = {Developmental Science},
number = {6},
title = {{Effect of socioeconomic status (SES) disparity on neural development in female African-American infants at age 1 month}},
volume = {19},
year = {2016}
}
@article{Xie2014a,
abstract = {The entorhinal cortex (ERC) and the perirhinal cortex (PRC) are subregions of the medial temporal lobe (MTL) that play important roles in episodic memory representations, as well as serving as a conduit between other neocortical areas and the hippocampus. They are also the sites where neuronal damage first occurs in Alzheimer's disease (AD). The ability to automatically quantify the volume and thickness of the ERC and PRC is desirable because these localized measures can potentially serve as better imaging biomarkers for AD and other neurodegenerative diseases. However, large anatomical variation in the PRC makes it a challenging area for analysis. In order to address this problem, we propose an automatic segmentation, clustering, and thickness measurement approach that explicitly accounts for anatomical variation. The approach is targeted to highly anisotropic (0.4x0.4x2.0mm3 ) T2-weighted MRI scans that are preferred by many authors for detailed imaging of the MTL, but which pose challenges for segmentation and shape analysis. After automatically labeling MTL substructures using multi-atlas segmentation, our method clusters subjects into groups based on the shape of the PRC, constructs unbiased population templates for each group, and uses the smooth surface representations obtained during template construction to extract regional thickness measurements in the space of each subject. The proposed thickness measures are evaluated in the context of discrimination between patients with Mild Cognitive Impairment (MCI) and normal controls (NC).},
author = {Xie, L. and Pluta, J. and Wang, H. and Das, S.R. and Mancuso, L. and Kliot, D. and Avants, B.B. and Ding, S.L. and Wolk, D.A. and Yushkevich, P.A.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 3},
title = {{Automatic clustering and thickness measurement of anatomical variants of the human perirhinal cortex.}},
volume = {17},
year = {2014}
}
@article{Kim2008,
abstract = {Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid- and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume  {\textgreater}  1.5 cm 3 ). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain-behavior relationships in the TBI population. {\textcopyright} 2007 Elsevier Inc. All rights rese rved.},
author = {Kim, J. and Avants, B. and Patel, S. and Whyte, J. and Coslett, B.H. and Pluta, J. and Detre, J.A. and Gee, J.C.},
doi = {10.1016/j.neuroimage.2007.10.005},
issn = {10538119},
journal = {NeuroImage},
keywords = {Atrophy,Diffeomorphic,Magnetic resonance imaging,Tensor-based morphometry,Traumatic brain injury},
number = {3},
title = {{Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study}},
volume = {39},
year = {2008}
}
@article{Maguire:2000aa,
abstract = {Structural MRIs of the brains of humans with extensive navigation experience, licensed London taxi drivers, were analyzed and compared with those of control subjects who did not drive taxis. The posterior hippocampi of taxi drivers were significantly larger relative to those of control subjects. A more anterior hippocampal region was larger in control subjects than in taxi drivers. Hippocampal volume correlated with the amount of time spent as a taxi driver (positively in the posterior and negatively in the anterior hippocampus). These data are in accordance with the idea that the posterior hippocampus stores a spatial representation of the environment and can expand regionally to accommodate elaboration of this representation in people with a high dependence on navigational skills. It seems that there is a capacity for local plastic change in the structure of the healthy adult human brain in response to environmental demands.},
author = {Maguire, E A and Gadian, D G and Johnsrude, I S and Good, C D and Ashburner, J and Frackowiak, R S and Frith, C D},
doi = {10.1073/pnas.070039597},
journal = {Proc Natl Acad Sci U S A},
month = {apr},
number = {8},
pages = {4398--4403},
pmid = {10716738},
title = {{Navigation-related structural change in the hippocampi of taxi drivers}},
volume = {97},
year = {2000}
}
@article{Gunawardena2010,
abstract = {Objective: To investigate the cognitive and neural basis for nonfluent speech in progressive nonfluent aphasia (PNFA). Background: Nonfluent speech is the hallmark feature of PNFA, and this has been attributed to impairments in syntactic processing, motor-speech planning, and executive functioning that also occur in these patients. Patients with PNFA have left inferior frontal atrophy. Methods: A large semi-structured speech sample and neuropsychological measures of language and executive functioning were examined in 16 patients with PNFA, 12 patients with behavioral-variant frontotemporal dementia (bvFTD), and 13 age-matched controls. Speech fluency was quantified as words per minute (WPM) in the semi-structured speech sample. Stepwise linear regression analyses were used to relate WPM to grammatic, motor-speech planning, and executive aspects of patient functioning. These measures were then related to cortical thickness in 8 patients with PNFA and 7 patients with bvFTD using structural MRI. Results: WPM was significantly reduced in patients with PNFA relative to controls and patients with bvFTD. Regression analyses revealed that only grammatic measures predicted WPM in PNFA, whereas executive measures were the only significant predictor of WPM in bvFTD. Cortical thinning was significant in PNFA relative to controls in left inferior frontal and anterior-superior temporal regions, and a regression analysis related this area to reduced WPM in PNFA. Significant cortical thinning associated with limited grammatic processing also was seen in the left inferior frontal-superior temporal region in PNFA, and this overlapped with the area of frontal-temporal thinning related to reduced WPM. Conclusion: Nonfluent speech in PNFA may be due in part to difficulty with grammatic processing associated with left inferior frontal and anterior-superior temporal disease. Copyright {\textcopyright} 2010 by AAN Enterprises, Inc. All rights reserved.},
author = {Gunawardena, D. and Ash, S. and McMillan, C. and Avants, B. and Gee, J. and Grossman, M.},
doi = {10.1212/WNL.0b013e3181ed9c7d},
issn = {00283878},
journal = {Neurology},
number = {7},
title = {{Why are patients with progressive nonfluent aphasia nonfluent?}},
volume = {75},
year = {2010}
}
@article{Klein2009,
abstract = {All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website. {\textcopyright} 2009.},
author = {Klein, A. and Andersson, J. and Ardekani, B.A. and Ashburner, J. and Avants, B. and Chiang, M.-C. and Christensen, G.E. and Collins, D.L. and Gee, J. and Hellier, P. and Song, J.H. and Jenkinson, M. and Lepage, C. and Rueckert, D. and Thompson, P. and Vercauteren, T. and Woods, R.P. and Mann, J.J. and Parsey, R.V.},
doi = {10.1016/j.neuroimage.2008.12.037},
issn = {10538119},
journal = {NeuroImage},
number = {3},
title = {{Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration}},
volume = {46},
year = {2009}
}
@inproceedings{Avants2007e,
abstract = {We provide a method for symmetric (unbiased) computation of optimal shape templates in the diffeomorphic space (Diff). We explicitly minimize the Diff metric distance by allowing an unbiased statistical evolution of the initial conditions defining the set of diffeomorphisms at time zero. This procedure is distinct from related methods that do not explicitly compute symmetric shape averages or only average the appearance component (intensity) in an unbiased way. Explicitly minimizing the diffeomorphic metric distance improves the sharpness and accuracy of the derived template. We illustrate this accuracy by computing optimal templates from a simple dataset of ellipses. Our shape optimizing method yields results that reproduce the analytically known optimal template. A related method that uses diffeomorphisms to find an unbiased optimal intensity template does not yield the theoretical result. We show that the improved performance occurs not only in synthetic studies but also when deriving templates from real data. {\textcopyright} 2007 IEEE.},
author = {Avants, B. and Epstein, C.L. and Gee, J.C.},
booktitle = {2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings},
doi = {10.1109/ISBI.2007.356932},
isbn = {1424406722},
title = {{Symmetric shape averaging in the diffeomorphic space}},
year = {2007}
}
@article{Yushkevich2009,
abstract = {This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated. The atlas is provided as an online resource with the aim of supporting subfield segmentation in emerging hippocampus imaging and image analysis techniques. An example application examining subfield-level hippocampal atrophy in temporal lobe epilepsy demonstrates the application of the atlas to in vivo studies. {\textcopyright} 2008 Elsevier Inc. All rights reserved.},
author = {Yushkevich, P.A. and Avants, B.B. and Pluta, J. and Das, S. and Minkoff, D. and Mechanic-Hamilton, D. and Glynn, S. and Pickup, S. and Liu, W. and Gee, J.C. and Grossman, M. and Detre, J.A.},
doi = {10.1016/j.neuroimage.2008.08.042},
issn = {10538119},
journal = {NeuroImage},
number = {2},
title = {{A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T}},
volume = {44},
year = {2009}
}
@inproceedings{Yushkevich2012,
abstract = {Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13]  substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate than those based on registration to a population template. In a 2D example in real MRI data, fusion methods result in more consistent mappings between manual segmentations of the hippocampus. {\textcopyright} 2012 IEEE.},
author = {Yushkevich, P.A. and Wang, H. and Pluta, J. and Avants, B.B.},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2012.6247771},
isbn = {9781467312264},
issn = {10636919},
title = {{From label fusion to correspondence fusion: A new approach to unbiased groupwise registration}},
year = {2012}
}
@article{Tustison:2010ab,
abstract = {A computational framework is described that was developed for quantitative analysis of hyperpolarized helium-3 MR lung ventilation image data. This computational framework was applied to a study consisting of 55 subjects (47 asthmatic and eight normal). Each subject was imaged before and after respiratory challenge and also underwent spirometry. Approximately 1600 image features were calculated from the lungs in each image. Both the image and 27 spirometric features were ranked based on their ability to characterize clinical diagnosis using a mutual information-based feature subset selection algorithm. It was found that the top image features perform much better compared with the current clinical gold-standard spirometric values when considered individually. Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content.},
author = {Tustison, Nicholas J and Altes, Talissa A and Song, Gang and de Lange, Eduard E and {Mugler 3rd}, John P and Gee, James C},
doi = {10.1002/mrm.22390},
journal = {Magn Reson Med},
month = {jun},
number = {6},
pages = {1448--1455},
pmid = {20512846},
title = {{Feature analysis of hyperpolarized helium-3 pulmonary {\{}MRI{\}}: a study of asthmatics versus nonasthmatics}},
volume = {63},
year = {2010}
}
@book{Duda2013a,
abstract = {We contribute a novel multivariate strategy for computing the structure of functional networks in the brain from arterial spin labeling (ASL) MRI. Our method fuses and correlates multiple functional signals by employing an interpretable dimensionality reduction method, sparse canonical correlation analysis (SCCA). There are two key aspects of this contribution. First, we show how SCCA may be used to compute a multivariate correlation between different regions of interest (ROI). In contrast to averaging the signal over the ROI, this approach exploits the full information within the ROI. Second, we show how SCCA may simultaneously exploit both the ASL-BOLD and ASL-based cerebral blood flow (CBF) time series to produce network measurements. Our approach to fusing multiple time signals in network studies improves reproducibility over standard approaches while retaining the interpretability afforded by the classic ROI region-averaging methods. We show experimentally in test-retest data that our sparse CCA method extracts biologically plausible and stable functional network structures from ASL. We compare the ROI approach to the CCA approach while using CBF measurements alone. We then compare these results to the joint BOLD-CBF networks in a reproducibility study and in a study of functional network structure in traumatic brain injury (TBI). Our results show that the SCCA approach provides significantly more reproducible results compared to region-averaging, and in TBI the SCCA approach reveals connectivity differences not seen with the region averaging approach. {\textcopyright} 2013 Springer-Verlag.},
author = {Duda, J.T. and Detre, J.A. and Kim, J. and Gee, J.C. and Avants, B.B.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-40760-4_79},
isbn = {9783642407598},
issn = {03029743},
number = {PART 3},
title = {{Fusing functional signals by sparse canonical correlation analysis improves network reproducibility}},
volume = {8151 LNCS},
year = {2013}
}
@article{Avants2007c,
abstract = {We present a unified method, based on symmetric diffeomorphisms, for studying longitudinal neurodegeneration. Our method first uses symmetric diffeomorphic normalization to find a spatiotemporal parameterization of an individual's image time series. The second step involves mapping a representative image or set of images from the time series into an optimal template space. The template mapping is then combined with the intrasubject spatiotemporal map to enable pairwise statistical tests to be performed on a population of normalized time series images. Here, we apply this longitudinal analysis protocol to study the gray matter atrophy patterns induced by frontotemporal dementia (FTD). We sample our normalized spatiotemporal maps at baseline (time zero) and time one year to generate an annualized atrophy map (AAM) that estimates the annual effect of FTD. This spatiotemporal normalization enables us to locate neuroanatomical regions that consistently undergo significant annual gray matter atrophy across the population. We found the majority of annual atrophy to occur in the frontal and temporal lobes in our population of 20 subjects. We also found significant effects in the hippocampus, insula and cingulate gyrus. Our novel results, significant at p  {\textless}  0.05 after false discovery rate correction, are represented in local template space but also assigned Talairach coordinates and Brodmann and Anatomical Automatic Labeling (AAL) labels. This paper shows the statistical power of symmetric diffeomorphic normalization for performing deformation-based studies of longitudinal atrophy.},
author = {Avants, B. and Anderson, C. and Grossman, M. and Gee, J.C.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 2},
title = {{Spatiotemporal normalization for longitudinal analysis of gray matter atrophy in frontotemporal dementia.}},
volume = {10},
year = {2007}
}
@article{Tustison2014a,
abstract = {Recent discussions within the neuroimaging community have highlighted the problematic presence of selection bias in experimental design. Although initially centering on the selection of voxels during the course of fMRI studies, we demonstrate how this bias can potentially corrupt voxel-based analyses. For such studies, template-based registration plays a critical role in which a representative template serves as the normalized space for group alignment. A standard approach maps each subject's image to a representative template before performing statistical comparisons between different groups. We analytically demonstrate that in these scenarios the popular sum of squared difference (SSD) intensity metric, implicitly surrogating as a quantification of anatomical alignment, instead explicitly maximizes effect size-an experimental design flaw referred to as "circularity bias." We illustrate how this selection bias varies in strength with the similarity metric used during registration under the hypothesis that while SSD-related metrics, such as Demons, will manifest similar effects, other metrics which are not formulated based on absolute intensity differences will produce less of an effect. Consequently, given the variability in voxel-based analysis outcomes with similarity metric choice, we caution researchers specifically in the use of SSD and SSD-related measures where normalization and statistical analysis involve the same image set. Instead, we advocate a more cautious approach where normalization of the individual subject images to the reference space occur s through corresponding image sets which are independent of statistical testing. Alternatively, one can use similarity terms that are less sensitive to this bias. {\textcopyright} 2012 Wiley Periodicals, Inc.},
author = {Tustison, N.J. and Avants, B.B. and Cook, P.A. and Kim, J. and Whyte, J. and Gee, J.C. and Stone, J.R.},
doi = {10.1002/hbm.22211},
issn = {10659471},
journal = {Human Brain Mapping},
keywords = {Image registration,Methodological bias,Morphometry,Nonindependent analysis},
number = {3},
title = {{Logical circularity in voxel-based analysis: Normalization strategy may induce statistical bias}},
volume = {35},
year = {2014}
}
@article{Das2012,
abstract = {The hippocampal formation (HF) is a brain structure of great interest because of its central role in learning and memory, and its associated vulnerability to several neurological disorders. In vivo oblique coronal T2-weighted MRI with high in-plane resolution ({\~{}}0.5mm×0.5mm), thick slices ({\~{}}2.0mm), and a field of view tailored to imaging the hippocampal formation (denoted HF-MRI in this paper) has been advanced as a useful imaging modality for detailed hippocampal morphometry. Cross-sectional analysis of volume measurements derived from HF-MRI has shown the modality's promise to yield sensitive imaging-based biomarker for neurological disorders such as Alzheimer's disease. However, the utility of this modality for making measurements of longitudinal change has not yet been demonstrated. In this paper, using an unbiased deformation-based morphometry (DBM) pipeline, we examine the suitability of HF-MRI for estimating longitudinal change by comparing atrophy rates measured in the whole hippocampus from this modality with those measured from more common isotropic ({\~{}}1mm  3 ) T1-weighted MRI in the same set of individuals, in a cohort of healthy controls and patients with cognitive impairment. While measurements obtained from HF-MRI were largely consistent with those obtained from T1-MRI, HF-MRI yielded slightly larger group effect of greater atrophy rates in patients than in controls. The estimated minimum sample size required for detecting a 25{\%} change in patients' atrophy rate in the hippocampus compared to the control group with a statistical power $\beta$=0.8 was N=269. For T1-MRI, the equivalent sample size was N=325. Using a dataset of test-retest scans, we show that the measurements were free of additive bias. We also demonstrate that these results were not a confound of certain methodological choices made in the DBM pipeline to address the challenges of making longitudinal measurements from HF-MRI, using a region of interest (ROI) around the HF to globally align serial images, followed by slice-by-slice deformable registration to measure local volume change. Additionally, we present a preliminary study of atrophy rate measurements within hippocampal subfields using HF-MRI. Cross-sectional differences in atrophy rates were detected in several subfields. {\textcopyright} 2012 Elsevier Inc.},
author = {Das, S.R. and Avants, B.B. and Pluta, J. and Wang, H. and Suh, J.W. and Weiner, M.W. and Mueller, S.G. and Yushkevich, P.A.},
doi = {10.1016/j.neuroimage.2012.01.098},
issn = {10538119},
journal = {NeuroImage},
keywords = {Atrophy,DBM sample size,Deformation-based morphometry,Evaluation,Hippocampus,Longitudinal,MCI,MRI,Medial temporal lobe,Subfields,T1-weighted,T2-weighted},
number = {2},
title = {{Measuring longitudinal change in the hippocampal formation from in vivo high-resolution T2-weighted MRI}},
volume = {60},
year = {2012}
}
@article{Grossman2010,
abstract = {The ventral medial prefrontal cortex (vmPFC) has been implicated in social and affectively influenced decision-making. Disease in this region may have clinical consequences for social judgments in patients with frontotemporal lobar degeneration (FTLD). To test this hypothesis, regional cortical activation was monitored with fMRI while healthy adults judged the acceptability of brief social scenarios such as cutting into a movie ticket line or going through a red light at 2. AM. The scenarios described: (i) a socially neutral condition, (ii) a variant of each scenario containing a negatively valenced feature, and (iii) a variant containing a positively valenced feature. Results revealed that healthy adults activated vmPFC during judgments of negatively valenced scenarios relative to positive scenarios and neutral scenarios. In a comparative behavioral study, the same social decision-making paradigm was administered to patients with a social disorder due to FTLD. Patients differed significantly from healthy controls, specifically showing less sensitivity to negatively valenced features. Comparative anatomical analysis revealed considerable overlap of vmPFC activation in healthy adults and vmPFC cortical atrophy in FTLD patients. These converging results support the role of vmPFC in social decision-making where potentially negative consequences must be considered. {\textcopyright} 2010 Elsevier Ltd.},
author = {Grossman, M. and Eslinger, P.J. and Troiani, V. and Anderson, C. and Avants, B. and Gee, J.C. and McMillan, C. and Massimo, L. and Khan, A. and Antani, S.},
doi = {10.1016/j.neuropsychologia.2010.07.036},
issn = {00283932},
journal = {Neuropsychologia},
keywords = {Frontotemporal dementia,Social decision-making,Ventromedial prefrontal cortex},
number = {12},
title = {{The role of ventral medial prefrontal cortex in social decisions: Converging evidence from fMRI and frontotemporal lobar degeneration}},
volume = {48},
year = {2010}
}
@article{Wang2011,
abstract = {We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72{\%}, 14{\%}, 29{\%} and 21{\%} fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively. {\textcopyright} 2011 Elsevier Inc.},
author = {Wang, H. and Das, S.R. and Suh, J.W. and Altinay, M. and Pluta, J. and Craige, C. and Avants, B. and Yushkevich, P.A.},
doi = {10.1016/j.neuroimage.2011.01.006},
issn = {10538119},
journal = {NeuroImage},
keywords = {AdaBoost,Brain extraction,Brain tissue segmentation,Error correction,Hippocampal segmentation,Medical image segmentation},
number = {3},
title = {{A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation}},
volume = {55},
year = {2011}
}
@article{Avants2004a,
abstract = {The goal of this research is to promote variational methods for anatomical averaging that operate within the space of the underlying image registration problem. This approach is effective when using the large deformation viscous framework, where linear averaging is not valid, or in the elastic case. The theory behind this novel atlas building algorithm is similar to the traditional pairwise registration problem, but with single image forces replaced by average forces. These group forces drive an average transport ordinary differential equation allowing one to estimate the geodesic that moves an image toward the mean shape configuration. This model gives large deformation atlases that are optimal with respect to the shape manifold as defined by the data and the image registration assumptions. We use the techniques in the large deformation context here, but they also pertain to small deformation atlas construction. Furthermore, a natural, inherently inverse consistent image registration is gained for free, as is a tool for constant arc length geodesic shape interpolation. The geodesic atlas creation algorithm is quantitatively compared to the Euclidean anatomical average to elucidate the need for optimized atlases. The procedures generate improved average representations of highly variable anatomy from distinct populations. {\textcopyright} 2004 Elsevier Inc. All rights reserved.},
author = {Avants, B. and Gee, J.C.},
doi = {10.1016/j.neuroimage.2004.07.010},
issn = {10538119},
journal = {NeuroImage},
keywords = {Atlas creation,Geodesic averaging,Inverse consistent,Morphometry,Nonrigid image registration},
number = {SUPPL. 1},
title = {{Geodesic estimation for large deformation anatomical shape averaging and interpolation}},
volume = {23},
year = {2004}
}
@article{Sun2008b,
abstract = {The medial model is a powerful shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. It has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3D. This paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. The results show that the medial model can capture the heart shape accurately. To demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. We calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases.},
author = {Sun, H. and Avants, B.B. and Frangi, A.F. and Sukno, F. and Geel, J.C. and Yushkevich, P.A.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 2},
title = {{Cardiac medial modeling and time-course heart wall thickness analysis.}},
volume = {11},
year = {2008}
}
@article{Schoenemann2007,
abstract = {A crucial component of research on brain evolution has been the comparison of fossil endocranial surfaces with modern human and primate endocrania. The latter have generally been obtained by creating endocasts out of rubber latex shells filled with plaster. The extent to which the method of production introduces errors in endocast replicas is unknown. We demonstrate a powerful method of comparing complex shapes in 3-dimensions (3D) that is broadly applicable to a wide range of paleoanthropological quest ions. Pairs of virtual endocasts (VEs) created from high-resolution CT scans of corresponding latex/plaster endocasts and their associated crania were rigidly registered (aligned) in 3D space for two Homo sapiens and two Pan troglodytes specimens. Distances between each cranial VE and its corresponding latex/plaster VE were then mapped on a voxel-by-voxel basis. The results show that between 79.7{\%} and 91.0{\%} of the voxels in the four latex/plaster VEs are within 2 mm of their corresponding cranial VEs surfaces. The average error is relatively small, and variation in the pattern of error across the surfaces appears to be generally random overall. However, inferior areas around the cranial base and the temporal poles were somewhat overestimated in both human and chimpanzee specimens, and the area overlaying Broca's area in humans was somewhat underestimated. This study gives an idea of the size of possible error inherent in latex/plaster endocasts, indicating the level of confidence we can have with studies relying on comparisons between them and, e.g., hominid fossil endocasts. {\textcopyright} 2006 Wiley-Liss, Inc.},
author = {Schoenemann, P.T. and Gee, J. and Avants, B. and Holloway, R.L. and Monge, J. and Lewis, J.},
doi = {10.1002/ajpa.20499},
issn = {00029483},
journal = {American Journal of Physical Anthropology},
keywords = {CT,Computed tomography,Endocast,Validation},
number = {2},
title = {{Validation of plaster endocast morphology through 3D CT image analysis}},
volume = {132},
year = {2007}
}
@article{Avants2011b,
abstract = {The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard = 0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard = 0.669. ±. 0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. {\textcopyright} 2010 Elsevier Inc.},
author = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook, Philip A. and Klein, Arno and Gee, James C.},
doi = {10.1016/j.neuroimage.2010.09.025},
isbn = {1095-9572 (Electronic) 1053-8119 (Linking)},
issn = {10538119},
journal = {NeuroImage},
pmid = {20851191},
title = {{A reproducible evaluation of ANTs similarity metric performance in brain image registration}},
year = {2011}
}
@article{Das2009a,
abstract = {Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray matter-cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported. {\textcopyright} 2008 Elsevier Inc. All rights reserved.},
author = {Das, S.R. and Avants, B.B. and Grossman, M. and Gee, J.C.},
doi = {10.1016/j.neuroimage.2008.12.016},
issn = {10538119},
journal = {NeuroImage},
keywords = {Cortex,Deformable models,Diffeomorphism,Euclidean distance,FTD,Longitudinal,Thickness},
number = {3},
title = {{Registration based cortical thickness measurement}},
volume = {45},
year = {2009}
}
@article{Weber2013,
abstract = {Network analysis is an emerging approach to functional connectivity in which the brain is construed as a graph and its connectivity and information processing estimated by mathematical characterizations of graphs. There has been little to no work examining the reproducibility of network metrics derived from different types of functional magnetic resonance imaging (fMRI) data (e.g.; resting vs. task related, or pulse sequences other than standard blood oxygen level dependent [BOLD] data) or of measures of network structure at levels other than summary statistics. Here, we take up these questions, comparing the reproducibility of graphs derived from resting arterial spin-labeling perfusion fMRI with those derived from BOLD scans collected while the participant was performing a task. We also examine the reproducibility of the anatomical connectivity implied by the graph by investigating test-retest consistency of the graphs' edges. We compare two measures of graph-edge consistency both within versus between subjects and across data types. We find a dissociation in the reproducibility of network metrics, with metrics from resting data most reproducible at lower frequencies and metrics from task-related data most reproducible at higher frequencies; that same dissociation is not recapitulated, however, in network structure, for which the task-related data are most consistent at all frequencies. Implications for the practice of network analysis are discussed. {\textcopyright} 2013 Psychonomic Society, Inc.},
author = {Weber, M.J. and Detre, J.A. and Thompson-Schill, S.L. and Avants, B.B.},
doi = {10.3758/s13415-013-0181-7},
issn = {15307026},
journal = {Cognitive, Affective and Behavioral Neuroscience},
keywords = {Functional connectivity,Network analysis,Perfusion,Reproducibility,fMRI},
number = {3},
title = {{Reproducibility of functional network metrics and network structure: A comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow}},
volume = {13},
year = {2013}
}
@article{Ng2007,
abstract = {Large-scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks, and, ultimately, the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed In brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed In situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts In tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and Its use to produce the first genomic scale 3D mapping of gene expression In a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of more than 20,000 genes Indicates that at least 78.8 percent are expressed at some level In the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org). {\textcopyright} 2007 IEEE.},
author = {Ng, L. and Pathak, S.D. and Kuan, C. and Lau, C. and Dong, H. and Sodt, A. and Dang, C. and Avants, B. and Yushkevich, P. and Gee, J.C. and Haynor, D. and Lein, E. and Jones, A. and Hawrylycz, M.},
doi = {10.1109/TCBB.2007.1035},
issn = {15455963},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
keywords = {Bioinformatics (genome or protein) databases,Data mining,Information visualization,Registration,Segmentation},
number = {3},
title = {{Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain}},
volume = {4},
year = {2007}
}
@article{Calhoun:2001aa,
abstract = {Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis.},
author = {Calhoun, V D and Adali, T and Pearlson, G D and Pekar, J J},
journal = {Hum Brain Mapp},
month = {nov},
number = {3},
pages = {140--151},
pmid = {11559959},
title = {{A method for making group inferences from functional {\{}MRI{\}} data using independent component analysis}},
volume = {14},
year = {2001}
}
@book{Song2013,
abstract = {In this paper we propose a novel way to construct a diffeomorphic polyaffine model. Each affine transform is defined on a local region and the resulting diffeomorphism encapsulates all the local transforms by a smooth and invertible displacement field. Compared with traditional weighting schemes used in combining local transforms, our new scheme guarantees that the resulting transform precisely preserves the value of each local affine transform. By introducing the trajectory of local regions instead of using regions themselves, the new approach encodes precisely each local affine transform using a diffeomorphism with one or more stationary velocity fields. Experiments show that our new polyaffine model is both accurate and efficient. {\textcopyright} 2013 Springer-Verlag.},
author = {Song, G. and Liu, Y. and Wu, B. and Avants, B. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-38868-2_56},
isbn = {9783642388675},
issn = {03029743},
keywords = {Diffeomorphism,Polyaffine,Transform},
title = {{Using region trajectories to construct an accurate and efficient polyaffine transform model}},
volume = {7917 LNCS},
year = {2013}
}
@article{Sundaram2005,
abstract = {We approach the problem of temporal reparameterization of dynamic sequences of lung MR images. In earlier work, we employed capacity-based reparameterization to co-register temporal sequences of 2-D coronal images of the human lungs. Here, we extend that work to the evaluation of a ventilator-acquired 3-D dataset from a normal mouse. Reparameterization according to both deformation and lung volume is evaluated. Both measures provide results that closely approximate normal physiological behavior, as judged from the original data. Our ultimate goal is to be able to characterize normal parenchymal biomechanics over a population of healthy individuals, and to use this statistical model to evaluate lung deformation under various pathological states.},
author = {Sundaram, T.A. and Avants, B.B. and Gee, J.C.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 2},
title = {{Towards a dynamic model of pulmonary parenchymal deformation: evaluation of methods for temporal reparameterization of lung data.}},
volume = {8},
year = {2005}
}
@article{Hopkins2013a,
abstract = {Recent advances in structural magnetic resonance imaging technology and analysis now allows for accurate in vivo measurement of cortical thickness, an important aspect of cortical organization that has historically only been conducted on postmortem brains. In this study, for the first time, we examined regional and lateralized cortical thickness in a sample of 71 chimpanzees for comparison with previously reported findings in humans. We also measured gray and white matter volumes for each subject. The results indicated that chimpanzees showed significant regional variation in cortical thickness with lower values in primary motor and sensory cortex compared with association cortex. Furthermore, chimpanzees showed significant rightward asymmetries in cortical thickness for a number of regions of interest throughout the cortex and leftward asymmetries in white but not gray matter volume. We also found that total and region-specific cortical thickness was significantly negatively correlated with white matter volume. Thus, chimpanzees with greater white matter volumes had thinner cortical thickness. The collective findings are discussed within the context of previous findings in humans and theories on the evolution of cortical organization and lateralization in primates. {\textcopyright} 2013 the authors.},
author = {Hopkins, W.D. and Avants, B.B.},
doi = {10.1523/JNEUROSCI.2996-12.2013},
issn = {02706474},
journal = {Journal of Neuroscience},
number = {12},
title = {{Regional and hemispheric variation in cortical thickness in chimpanzees (Pan troglodytes)}},
volume = {33},
year = {2013}
}
@article{Tustison:2011ad,
abstract = {We introduce a labeled point set registration algorithm based on a family of novel information-theoretic measures derived as a generalization of the well-known Shannon entropy. This generalization, known as the Havrda-Charvat-Tsallis entropy, permits a fine-tuning between solution types of varying degrees of robustness of the divergence measure between multiple point sets. A variant of the traditional free-form deformation approach, known as directly manipulated free-form deformation, is used to model the transformation of the registration solution. We provide an overview of its open source implementation based on the Insight Toolkit of the National Institutes of Health. Characterization of the proposed framework includes comparison with other state of the art kernel-based methods and demonstration of its utility for lung registration via labeled point set representation of lung anatomy.},
author = {Tustison, Nicholas J and Awate, Suyash P and Song, Gang and Cook, Tessa S and Gee, James C},
doi = {10.1109/TMI.2010.2086065},
journal = {IEEE Trans Med Imaging},
month = {feb},
number = {2},
pages = {451--460},
pmid = {20937578},
title = {{Point set registration using {\{}H{\}}avrda-{\{}C{\}}harvat-{\{}T{\}}sallis entropy measures}},
volume = {30},
year = {2011}
}
@book{Wang2010,
abstract = {We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them. {\textcopyright} 2010 Springer-Verlag.},
author = {Wang, H. and Das, S. and Pluta, J. and Craige, C. and Altinay, M. and Avants, B. and Weiner, M. and Mueller, S. and Yushkevich, P.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-15711-0_14},
isbn = {3642157106},
issn = {03029743},
number = {PART 3},
title = {{Standing on the shoulders of giants: Improving medical image segmentation via bias correction}},
volume = {6363 LNCS},
year = {2010}
}
@article{Avants2014,
author = {Avants, Brian and Tustison, Nick},
doi = {doi:10.6084/m9.figshare.915436},
month = {jan},
title = {{ANTs/ANTsR Brain Templates}},
url = {http://figshare.com/articles/ANTs{\_}ANTsR{\_}Brain{\_}Templates/915436},
year = {2014}
}
@book{Sun2008,
abstract = {The medial model is a powerful shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. It has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3D. This paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. The results show that the medial model can capture the heart shape accurately. To demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. We calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases. {\textcopyright} 2008 Springer Berlin Heidelberg.},
author = {Sun, H. and Avants, B.B. and Frangi, A.F. and Sukno, F. and Gee, J.C. and Yushkevich, P.A.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-540-85990-1-92},
isbn = {3540859896},
issn = {03029743},
number = {PART 2},
title = {{Cardiac medial modeling and time-course heart wall thickness analysis}},
volume = {5242 LNCS},
year = {2008}
}
@article{Nichols:2003aa,
abstract = {Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg's methods, are now applicable to dependent data. Continuous random field methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We find that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom. We also show the limitations of trying to find an equivalent number of independent tests for an image of correlated test statistics.},
author = {Nichols, Thomas and Hayasaka, Satoru},
doi = {10.1191/0962280203sm341ra},
journal = {Stat Methods Med Res},
month = {oct},
number = {5},
pages = {419--446},
pmid = {14599004},
title = {{Controlling the familywise error rate in functional neuroimaging: a comparative review}},
volume = {12},
year = {2003}
}
@book{Avants2003,
abstract = {This work provides a new technique for surface oriented volumetric image analysis. The method makes no assumptions about topology, instead constructing a local neighborhood from image information, such as a segmentation or edge map, to define a surface patch. Neighborhood constructions using extrinsic and intrinsic distances are given. This representation allows one to estimate differential properties directly from the image's Gauss map. We develop a novel technique for this purpose which estimates the shape operator and yields both principal directions and curvatures. Only first derivatives need be estimated, making the method numerically stable. We show the use of these measures for multi-scale classification of image structure by the mean and Gaussian curvatures. Finally, we propose to register image volumes by surface curvature. This is particularly useful when geometry is the only variable. To illustrate this, we register binary segmented data by surface curvature, both rigidly and non-rigidly. A novel variant of Demons registration, extensible for use with differentiable similarity metrics, is also applied for deformable curvature-driven registration of medical images. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
author = {Avants, B. and Gee, J.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
issn = {03029743},
title = {{The shape operator for differential analysis of images}},
volume = {2732},
year = {2003}
}
@article{Tustison2015,
abstract = {{\textcopyright} 2014, Springer Science+Business Media New York. Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package—a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. A verage Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for “complete”, “core”, and “enhanced” tumor components, respectively.},
author = {Tustison, N.J. and Shrinidhi, K.L. and Wintermark, M. and Durst, C.R. and Kandel, B.M. and Gee, J.C. and Grossman, M.C. and Avants, B.B.},
doi = {10.1007/s12021-014-9245-2},
issn = {15392791},
journal = {Neuroinformatics},
keywords = {Advanced normalization tools,BRATS,Brain tumor segmentation,Glioma,R project for statistical computing},
number = {2},
title = {{Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR}},
volume = {13},
year = {2015}
}
@article{Yushkevich2010a,
abstract = {Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation. {\textcopyright} 2009 Elsevier Inc. All rights reserved.},
author = {Yushkevich, P.A. and Avants, B.B. and Das, S.R. and Pluta, J. and Altinay, M. and Craige, C.},
doi = {10.1016/j.neuroimage.2009.12.007},
issn = {10538119},
journal = {NeuroImage},
keywords = {Alzheimer's disease neuroimaging initiative,Deformation-based morphometry,Longitudinal image registration,Neurodegenerative disorders,Neuroimaging biomarkers,Unbiased estimation},
number = {2},
title = {{Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: An illustration in ADNI 3 T MRI data}},
volume = {50},
year = {2010}
}
@article{Jain2012,
abstract = {Purpose: To evaluate the longitudinal repeatability and accuracy of cerebral blood flow (CBF) measurements by using pseudo-continuous arterial spin-labeled (pCASL) perfusion magnetic resonance (MR) imaging in typically developing children. Materials and Methods: Institutional review board approval with HIPAA compliance and informed consent were obtained. Twenty-two children aged 7-17 years underwent repeated pCASL examinations 2-4 weeks apart with a 3-T MR imager, along with in vivo blood T1 and arterial transit time measurements. Phase-contrast (PC) MR imaging was performed as the reference standard for global blood flow volume. Intraclass correlation coefficient (ICC) and within-subject coefficient of variation (wsCV) were used to evaluate accuracy and repeatability. Results: The accuracy of pCASL against the reference standard of PC MR imaging increased on incorporating subjectwise in vivo blood T1 measurement (ICC: 0.32 vs 0.58). The ICC further increased to 0.65 by using a population-based model of blood T1. Additionally, CBF measurements with use of pCASL demonstrated a moderate to good level of longitudinal repeatability in whole brain (ICC = 0.61, wsCV = 15{\%}), in gray matter (ICC = 0.65, wsCV = 14{\%}), and across 16 brain regions (mean ICC = 0.55, wsCV = 17{\%}). The mean arterial transit time was 1538 msec ± 123 (standard deviation) in the pediatric cohort studied, which showed an increasing trend with age (P = .043). Conclusion: Incorporating developmental changes in blood T1 is important for improving the accuracy of pCASL CBF measurements in children and adolescents; the noninvasive nature, accuracy, and longitudinal repeatability should facilitate the use of pCASL perfusion MR imaging in neurodevelopmental studie s. {\textcopyright} RSNA, 2012.},
author = {Jain, V. and Duda, J. and Avants, B. and Giannetta, M. and Xie, S.X. and Roberts, T. and Detre, J.A. and Hurt, H. and Wehrli, F.W. and Wang, D.J.J.},
doi = {10.1148/radiol.12111509},
issn = {00338419},
journal = {Radiology},
number = {2},
title = {{Longitudinal reproducibility and accuracy of pseudo-continuous arterial spin-labeled perfusion MR imaging in typically developing children}},
volume = {263},
year = {2012}
}
@article{Pustina2017,
abstract = {{\textcopyright} 2017 Wiley Periodicals, Inc. The severity of post-stroke aphasia and the potential for recovery are highly variable and difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the integration of multiple neuroimaging modalities and the adoption of new methods that can detect multivariate brain-behavior relationships. We created and tested a multimodal framework that relies on three information sources (lesion maps, structural connectivity, and functional connectivity) to create an array of unimodal predictions which are then fed into a final model that creates “stacked multimodal predictions” (STAMP). Crossvalidated predictions of four aphasia scores (picture naming, sentence repetition, sentence comprehension, and overall aphasia severity) were obtained from 53 left hemispheric chronic stroke patients (age: 57.1 ± 12.3 yrs, post-stroke interval: 20 months, 25 female). Results showed accurate predictions for all four aphasia scores (correlation true vs. predicted: r = 0.79–0.88). The accuracy was slightly smaller but yet significant (r = 0.66) in a full split crossvalidation with each patient considered as new. Critically, multimodal predictions produced more accurate results that any single modality alone. Topological maps of the brain regions involved in the prediction were recovered and compared with traditional voxel-based lesion-to-symptom maps, revealing high spatial congruency. These results suggest that neuroimaging modalities carry complementary information potentially useful for the prediction of aphasia scores. More broadly, this study shows that the translation of neuroimaging findings into clinically useful tools calls for a shift in perspective from unimodal to multimodal neuroimaging, from univariate to multivariate methods, from linear to nonlinear models, and, conceptually, from inferential to predictive brain mapping. Hum Brain Mapp 38:5603–5615, 2017. {\textcopyright} 2017 Wiley Periodicals, Inc.},
author = {Pustina, D. and Coslett, H.B. and Ungar, L. and Faseyitan, O.K. and Medaglia, J.D. and Avants, B. and Schwartz, M.F.},
doi = {10.1002/hbm.23752},
issn = {10970193},
journal = {Human Brain Mapping},
keywords = {DTI,bold,cognitive,disease,language,machine learning,neurology,resting state},
number = {11},
title = {{Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions}},
volume = {38},
year = {2017}
}
@article{Shamy:2011aa,
abstract = {Spatiotemporal and recognition memory are affected by aging in humans and macaque monkeys. To investigate whether these deficits are coupled with atrophy of memory-related brain regions, T(1)-weighted magnetic resonance images were acquired and volumes of the cerebrum, ventricles, prefrontal cortex (PFC), calcarine cortex, hippocampus, and striatum were quantified in young and aged rhesus monkeys. Subjects were tested on a spatiotemporal memory procedure (delayed response [DR]) that requires the integrity of the PFC and a medial temporal lobe-dependent recognition memory task (delayed nonmatching to sample [DNMS]). Region of interest analyses revealed that age inversely correlated with striatal, dorsolateral prefrontal cortex (dlPFC), and anterior cingulate cortex volumes. Hippocampal volume predicted acquisition of the DR task. Striatal volume correlated with DNMS acquisition, whereas total prefrontal gray matter, prefrontal white matter, and dlPFC volumes each predicted DNMS accuracy. A regional covariance analysis revealed that age-related volumetric changes could be captured in a distributed network that was coupled with declining performance across delays on the DNMS task. This volumetric analysis adds to growing evidence that cognitive aging in primates arises from region-specific morphometric alterations distributed across multiple memory-related brain systems, including subdivisions of the PFC.},
author = {Shamy, Jul Lea and Habeck, Christian and Hof, Patrick R and Amaral, David G and Fong, Sania G and Buonocore, Michael H and Stern

repository: ntustison/PaperANTsX

repo_name: ntustison/PaperANTsX