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@misc{Baltrusaitis2018,
abstract = {Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.},
archivePrefix = {arXiv},
arxivId = {1705.09406},
author = {Baltrusaitis, Tadas and Ahuja, Chaitanya and Morency, Louis Philippe},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/TPAMI.2018.2798607},
eprint = {1705.09406},
issn = {01628828},
keywords = {Hidden Markov models,Media,Multimedia communication,Multimodal,Speech,Speech recognition,Streaming media,Visualization,introductory,machine learning,survey},
title = {{Multimodal Machine Learning: A Survey and Taxonomy}},
year = {2018}
}
@article{Tustison2010,
abstract = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as N4ITK, available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized 3 He lung image data, and 9.4T postmortem hippocampus data. {\textcopyright} 2006 IEEE.},
author = {Tustison, N.J. and Avants, B.B. and Cook, P.A. and Zheng, Y. and Egan, A. and Yushkevich, P.A. and Gee, J.C.},
doi = {10.1109/TMI.2010.2046908},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {B-spline approximation,Bias field,Inhomogeneity,N3},
number = {6},
title = {{N4ITK: Improved N3 bias correction}},
volume = {29},
year = {2010}
}
@article{Aguirre2007,
abstract = {Background: RPE65 is an essential molecule in the retinoid-visual cycle, and RPE65 gene mutations cause the congenital human blindness known as Leber congenital amaurosis (LCA). Somatic gene therapy delivered to the retina of blind dogs with an RPE65 mutation dramatically restores retinal physiology and has sparked international interest in human treatment trials for this incurable disease. An unanswered question is how the visual cortex responds after prolonged sensory deprivation from retinal dysfunction. We therefore studied the cortex of RPE65-mutant dogs before and after retinal gene therapy. Then, we inquired whether there is visual pathway integrity and responsivity in adult humans with LCA due to RPE65 mutations (RPE65-LCA). Methods and Findings: RPE65-mutant dogs were studied with fMRI. Prior to therapy, retinal and subcortical responses to light were markedly diminished, and there were minimal cortical responses within the primary visual areas of the lateral gyrus (activation amplitude mean ± standard deviation [SD] = 0.07{\%} ± 0.06{\%} and volume = 1.3 ± 0.6 cm 3 ). Following therapy, retinal and subcortical response restoration was accompanied by increased amplitude (0.18{\%} ± 0.06{\%}) and volume (8.2 ± 0.8 cm 3 ) of activation within the lateral gyrus (p  {\textless}  0.005 for both). Cortical recovery occurred rapidly (within a month of treatment) and was persistent (as long as 2.5 y after treatment). Recovery was present even when treatment was provided as late as 1-4 y of age. Human RPE65-LCA patients (ages 18-23 y) were studied with structural magnetic resonance imaging. Optic nerve diameter (3.2 ± 0.5 mm) was within the normal range (3.2 ± 0.3 mm), and occipital cortical white matter density as judged by voxel-based morphometry was slightly but significantly altered (1.3 SD below control average, p = 0.005). Functional magnetic resonance imaging in human RPE65-LCA patients revealed cortical responses with a markedly diminished activation volume (8.8 ± 1.2 cm  3 ) compared to controls (29.7 ± 8.3 cm 3 , p  {\textless}  0.001) when stimulated with lower intensity light. Unexpectedly, cortical response volume (41.2 ± 11.1 cm 3 ) was comparable to normal (48.8 ± 3.1 cm 3 , p = 0.2) with higher intensity light stimulation. Conclusions: Visual cortical responses dramatically improve after retinal gene therapy in the canine model of RPE65-LCA. Human RPE65-LCA patients have preserved visual pathway anatomy and detectable cortical activation despite limited visual experience. Taken together, the results support the potential for human visual benefit from retinal therapies currently being aimed at restoring vision to the congenitally blind with genetic retinal disease. {\textcopyright} 2007 Aguirre et al.},
author = {Aguirre, G.K. and Kom{\'{a}}romy, A.M. and Cideciyan, A.V. and Brainard, D.H. and Aleman, T.S. and Roman, A.J. and Avants, B.B. and Gee, J.C. and Korczykowski, M. and Hauswirth, W.W. and Acland, G.M. and Aguirre, G.D. and Jacobson, S.G.},
doi = {10.1371/journal.pmed.0040230},
issn = {15491277},
journal = {PLoS Medicine},
number = {6},
title = {{Canine and human visual cortex intact and responsive despite early retinal blindness from RPE65 mutation}},
volume = {4},
year = {2007}
}
@article{Song:2012aa,
abstract = {RATIONALE AND OBJECTIVES: The aim of this study was to compare the performance of various image-based metrics computed from thoracic high-resolution computed tomography (HRCT) with data from pulmonary function testing (PFT) in characterizing interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD).
MATERIALS AND METHODS: Fourteen patients with ILD and 11 with COPD had undergone both PFT and HRCT within 3 days. For each patient, 93 image-based metrics were computed, and their relationships with the 21 clinically used PFT parameters were analyzed using a minimal-redundancy-maximal-relevance statistical framework. The first 20 features were selected among the total of 114 mixed image metrics and PFT values in the characterization of ILD and COPD.
RESULTS: Among the best-performing 20 features, 14 were image metrics, derived from attenuation histograms and texture descriptions. The highest relevance value computed from PFT parameters was 0.47, and the highest from image metrics was 0.52, given the theoretical bound as [0, 0.69]. The ILD or COPD classifier using the first four features achieved a 1.92{\%} error rate.
CONCLUSIONS: Some image metrics are not only as good discriminators as PFT for the characterization of ILD and COPD but are also not redundant when PFT values are provided. Image metrics of attenuation histogram statistics and texture descriptions may be valuable for further investigation in computer-assisted diagnosis.},
author = {Song, Gang and {Mortani Barbosa Jr}, Eduardo and Tustison, Nicholas and Gefter, Warren B and Kreider, Maryl and Gee, James C and Torigian, Drew A},
doi = {10.1016/j.acra.2012.03.007},
journal = {Acad Radiol},
month = {jul},
number = {7},
pages = {857--864},
pmid = {22516670},
title = {{A comparative study of {\{}HRCT{\}} image metrics and {\{}PFT{\}} values for characterization of {\{}ILD{\}} and {\{}COPD{\}}}},
volume = {19},
year = {2012}
}
@book{Cullen2018,
abstract = {{\textcopyright} 2018, Springer Science+Business Media, LLC. Convolutional neural networks are poised to become a standard technology in neuroimage analysis. This general purpose framework learns both low-level and high-level features directly from images, making them ideal for image segmentation. To highlight the potential of these tools, we present a novel convolutional-deconvolutional network architecture designed for efficient three-dimensional, supervised brain segmentation. We detail the problem definition, network design, evaluation and interpretation underlying this effort. We also provide evidence that such networks can achieve accuracy in a matter of seconds that rivals what traditional methods may take over an hour to compute.},
author = {Cullen, N.C. and Avants, B.B.},
booktitle = {Neuromethods},
doi = {10.1007/978-1-4939-7647-8_2},
issn = {19406045},
keywords = {Brain,Convolutional,Deconvolutional,Deep learning,Neuroimaging,Segmentation},
title = {{Convolutional neural networks for rapid and simultaneous brain extraction and tissue segmentation}},
volume = {136},
year = {2018}
}
@article{Fan2007,
abstract = {A multivariate classification approach has been presented to examine the brain abnormalities, i.e., due to prenatal cocaine exposure, using both structural and functional brain images. First, a regional statistical feature extraction scheme was adopted to capture discriminative features from voxel-wise morphometric and functional representations of brain images, in order to reduce the dimensionality of the features used for classification, as well as to achieve the robustness to registration error and inter-subject variations. Then, this feature extraction method was used in conjunction with a hybrid feature selection method and a nonlinear support vector machine for the classification o f brain abnormalities. This brain classification approach has been applied to detecting the brain abnormality associated with prenatal cocaine exposure in adolescents. A promising classification performance was achieved on a data set of 49 subjects (24 normal and 25 prenatally cocaine-exposed teenagers), with a leave-one-out cross-validation. Experimental results demonstrated the efficacy of our method, as well as the importance of incorporating both structural and functional images for brain classification. Moreover, spatial patterns of group difference derived from the constructed classifier were mostly consistent with the results of the conventional statistical analysis method. Therefore, the proposed approach provided not only a multivariate classification method for detecting brain abnormalities, but also an alternative way for group analysis of multimodality images. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
author = {Fan, Y. and Rao, H. and Hurt, H. and Giannetta, J. and Korczykowski, M. and Shera, D. and Avants, B.B. and Gee, J.C. and Wang, J. and Shen, D.},
doi = {10.1016/j.neuroimage.2007.04.009},
issn = {10538119},
journal = {NeuroImage},
number = {4},
title = {{Multivariate examination of brain abnormality using both structural and functional MRI}},
volume = {36},
year = {2007}
}
@article{Simon2008,
abstract = {Background: Chromosome 22q11.2 deletion syndrome is one of the most common genetic causes of cognitive impairment and developmental disability yet little is known about the neural bases of those challenges. Here we expand upon our previous neurocognitive studies by specifically investigating the hypothesis that changes in neural connectivity relate to cognitive impairment in children with the disorder. Methods: Whole brain analyses of multiple measures computed from diffusion tensor image data acquired from the brains of children with the disorder and typically developing controls. We also correlated diffusion tensor data with performance on a visuospatial cognitive task that taps spatial attention. Results: Analyses revealed four common clusters, in the parietal and frontal lobes, that showed complementary patterns of connectivity in children with the deletion and typical controls. We interpreted these results as indicating differences in connective complexity to adjoining cortical regions that are critical to the cognitive functions in which affected children show impairments. Strong, and similarly opposing patterns of correlations between diffusion values in those clusters and spatial attention performance measures considerably strengthened that interpretation. Conclusion: Our results suggest that atypical development of connective patterns in the brains of children with chromosome 22q11.2 deletion syndrome indicate a neuropathology that is related to the visuospatial cognitive impairments that are commonly found in affected individuals. {\textcopyright} 2008 Simon et al; licensee BioMed Central Ltd.},
author = {Simon, T.J. and Wu, Z. and Avants, B. and Zhang, H. and Gee, J.C. and Stebbins, G.T.},
doi = {10.1186/1744-9081-4-25},
issn = {17449081},
journal = {Behavioral and Brain Functions},
title = {{Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome}},
volume = {4},
year = {2008}
}
@inproceedings{Pan2008,
abstract = {Transfer learning addresses the problem of how to uti-lize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source do-main data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can ap-ply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new di-mensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different domains in a latent space. The ef-fectiveness of our approach to transfer learning is veri-fied by experiments in two real world applications: in-door WiFi localization and binary text classification.},
author = {Pan, SJ and Kwok, JT and Yang, Qiang},
booktitle = {AAAI Conference on Artificial Intelligence},
doi = {10.1109/TKDE.2009.191},
isbn = {978-1-57735-368-3},
issn = {1041-4347},
keywords = {Machine Learning},
title = {{Transfer Learning via Dimensionality Reduction.}},
year = {2008}
}
@article{Du2016,
abstract = {Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ shenli@iu.edu Supplementary data are available at Bioinformatics online.},
author = {Du, Lei and Huang, Heng and Yan, Jingwen and Kim, Sungeun and Risacher, Shannon L and Inlow, Mark and Moore, Jason H and Saykin, Andrew J and Shen, Li and Initiative, Alzheimer's Disease Neuroimaging},
doi = {10.1093/bioinformatics/btw033},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = { methods,Algorithms; Brain; Humans; Neuroimaging},
month = {may},
number = {10},
pages = {1544--1551},
pmid = {26801960},
title = {{Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.}},
volume = {32},
year = {2016}
}
@article{Eddelbuettel2018,
abstract = {R has always provided an application programming interface (API) for extensions. Based on the C language, it uses a number of macros and other low-level constructs to exchange data structures between the R process and any dynamically-loaded component modules authors added to it. With the introduction of the Rcpp package, and its later refinements, this process has become considerably easier yet also more robust. By now, Rcpp has become the most popular extension mechanism for R. This article introduces Rcpp, and illustrates with several examples how the Rcpp Attributes mechanism in particular eases the transition of objects between R and C++ code.},
author = {Eddelbuettel, Dirk and Balamuta, James Joseph},
doi = {10.1080/00031305.2017.1375990},
issn = {15372731},
journal = {American Statistician},
keywords = {Applications and case studies,Computationally intensive methods,Simulation,Statistical computing},
title = {{Extending R with C++: A Brief Introduction to Rcpp}},
year = {2018}
}
@article{Tustison2011,
abstract = {Purpose: To develop an automated segmentation method to differentiate the ventilated lung volume on 3 He magnetic resonance imaging (MRI). Materials and Methods: Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers. Results: There was excellent correlation between the number of ventilation defects tabulated by CP and reader {\#}1 (intraclass correlation coefficient [ICC] = 0.86), CP and reader {\#}2 (ICC = 0.85), and between the two readers (ICC = 0.97). The STAPLE results from the second analysis yielded the following sensitivity/specificity numbers: CP (0.898/0.905), radiologist {\#}1 (0.743/0.897), radiologist {\#}2 (0.501/0.985), radiologist {\#}3 (0.898/0.848), and the first author (0.600/0.984). Conclusion: We developed and evaluated an automated method for quantifying the ventilated lung volume on 3 He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects. Copyright {\textcopyright} 2011 Wiley-Liss, Inc.},
author = {Tustison, N.J. and Avants, B.B. and Flors, L. and Altes, T.A. and {De Lange}, E.E. and {Mugler III}, J.P. and Gee, J.C.},
doi = {10.1002/jmri.22738},
issn = {10531807},
journal = {Journal of Magnetic Resonance Imaging},
keywords = {hyperpolarized He 3,segmentation,ventilation defects},
number = {4},
title = {{Ventilation-based segmentation of the lungs using hyperpolarized{\textless}sup{\textgreater}3{\textless}/sup{\textgreater}He MRI}},
volume = {34},
year = {2011}
}
@book{Avants2007b,
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. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
author = {Avants, B. and Anderson, C. 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 = {9783540757580},
issn = {03029743},
number = {PART 2},
title = {{Spatiotemporal normalization for longitudinal analysis of gray matter atrophy in frontotemporal dementia}},
volume = {4792 LNCS},
year = {2007}
}
@article{Ashtari2011,
abstract = {Converging lines of evidence suggest an adverse effect of heavy cannabis use on adolescent brain development, particularly on the hippocampus. In this preliminary study, we compared hippocampal morphology in 14 "treatment-seeking" adolescents (aged 18-20) with a history of prior heavy cannabis use (5.8 joints/day) after an average of 6.7 months of drug abstinence, and 14 demographically matched normal controls. Participants underwent a high-resolution 3D MRI as well as cognitive testing including the California Verbal Learning Test (CVLT). Heavy-cannabis users showed significantly smaller volumes of the right (p  {\textless}  0.04) and left (p  {\textless}  0.02) hippocampus, but no significant differences in the amygdala region compared to controls. In controls, larger hippocampus volumes were observed to be significantly correlated with higher CVLT verbal learning and memory scores, but these relationships were not observed in cannabis users. In cannabis users, a smaller right hippocampus volume was correlated with a higher amount of cannabis use (r = -0.57, p  {\textless}  0.03). These data support a hypothesis that heavy cannabis use may have an adverse effect on hippocampus development. These findings, after an average 6.7 month of supervised abstinence, lend support to a theory that cannabis use may impart long-term structural and functional damage. Alternatively, the observed hippocampal volumetric abnormalities may represent a risk factor for cannabis dependence. These data have potential significance for understanding the observed relationship between early cannabis exposure during adolescence and subsequent development of adult psychopathology reported in the literature for schizophrenia and related psychotic disorders. {\textcopyright} 2011 Elsevier Ltd.},
author = {Ashtari, M. and Avants, B. and Cyckowski, L. and Cervellione, K.L. and Roofeh, D. and Cook, P. and Gee, J. and Sevy, S. and Kumra, S.},
doi = {10.1016/j.jpsychires.2011.01.004},
issn = {00223956},
journal = {Journal of Psychiatric Research},
keywords = {Adolescence,CVLT,Cannabis,Hippocampus,Learning and memory,Magnetic resonance imaging},
number = {8},
title = {{Medial temporal structures and memory functions in adolescents with heavy cannabis use}},
volume = {45},
year = {2011}
}
@article{Kandel2015,
abstract = {{\textcopyright} 2014 Elsevier Inc. We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve the interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test-retest data show that both the anatomically predicted and residual perfusion signals are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in selected regions including the hippocampi (corr=0.38, p-value  {\textless} 10 -6 ), precuneus (corr=-0.44, p {\textless} 10 -5 ), and combined default mode network regions (corr=-0.45, p {\textless} 10 -8 ) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development.},
author = {Kandel, B.M. and Wang, D.J.J. and Detre, J.A. and Gee, J.C. and Avants, B.B.},
doi = {10.1016/j.neuroimage.2014.10.052},
issn = {10959572},
journal = {NeuroImage},
title = {{Decomposing cerebral blood flow MRI into functional and structural components: A non-local approach based on prediction}},
volume = {105},
year = {2015}
}
@article{Mcmillan2014,
abstract = {Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N=93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was  performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89{\%} sensitive and 89{\%} specific) and required a lower minimum sample size (N=26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. {\textcopyright} 2014 Wiley Periodicals, Inc.},
author = {Mcmillan, C.T. and Avants, B.B. and Cook, P. and Ungar, L. and Trojanowski, J.Q. and Grossman, M.},
doi = {10.1002/hbm.22515},
issn = {10970193},
journal = {Human Brain Mapping},
keywords = {Alzheimer's disease,Biomarkers,Classification,DTI,Frontotemporal degeneration,MRI,Statistical power},
number = {9},
title = {{The power of neuroimaging biomarkers for screening frontotemporal dementia}},
volume = {35},
year = {2014}
}
@inproceedings{Song2006b,
abstract = {We describe a novel framework for automatic brain MRI tissue segmentation. To overcome inherent difficulties associated with this particular segmentation problem, we use a graph cut/atlas-based registration methodology optimized within an iterative mode. The basic graph cut algorithm guarantees a global or near-global minimum of an energy function associated with a Markov Random Field (MRF). For atlas-based graph cuts, we tailor this energy function to incorporate both a priori information derived from registered brain atlases as well as region and boundary information derived directly from the images. The iterative algorithm adaptively alternates segmentation and inhomogeneity correction. The proposed method can be extended to multispectral image segmentation. We validate our method in both simulated adult and real neonatal brain MR images corrupted by significant noise and intensity inhomogeneities. {\textcopyright} 2006 IEEE.},
author = {Song, Z. and Tustison, N. and Avants, B. and Gee, J.},
booktitle = {2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings},
isbn = {0780395778},
title = {{Adaptive graph cuts with tissue priors for brain MRI segmentation}},
volume = {2006},
year = {2006}
}
@article{Duda2013,
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.},
author = {Duda, J.T. and Detre, J.A. and Kim, J. and Gee, J.C. and Avants, B.B.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 3},
title = {{Fusing functional signals by sparse canonical correlation analysis improves network reproducibility.}},
volume = {16},
year = {2013}
}
@article{Tustison:2003aa,
abstract = {Current research investigating the modeling of left ventricular dynamics for accurate clinical assessment of cardiac function is extensive. Magnetic resonance (MR) tagging is a functional imaging method which allows for encoding of a grid of signal voids on cardiac MR images, providing a mechanism for noninvasive measurement of intramural tissue deformations, in vivo. We present a novel technique of employing a four-dimensional (4-D) B-spline model which permits concurrent determination of myocardial beads and myocardial strains. The method entails fitting the knot planes of the 4-D B-spline model for fixed times to a sequence of triplets of orthogonal sets of tag surfaces for all imaged volumetric frames within the constraints of the model's spatio-temporal internal energy. From a three-dimensional (3-D) displacement field, the corresponding long and short-axis Lagrangian normal, shear, and principal strain maps are produced. As an important byproduct, the points defined by the 3-D intersections of the triplets of orthogonal tag planes, which we refer to as myocardial beads, can easily be determined by our model. Displaying the beads as a movie loop allows for the visualization of the nonrigid movement of the left ventricle in 3-D.},
author = {Tustison, Nicholas J and D{\'{a}}vila-Rom{\'{a}}n, Victor G and Amini, Amir A},
doi = {10.1109/TBME.2003.814530},
journal = {IEEE Trans Biomed Eng},
month = {aug},
number = {8},
pages = {1038--1040},
pmid = {12892332},
title = {{Myocardial kinematics from tagged {\{}MRI{\}} based on a {\{}4-D{\}} {\{}B{\}}-spline model}},
volume = {50},
year = {2003}
}
@article{Cook2005a,
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.},
author = {Cook, P.A. and Zhang, H. and Avants, B.B. and Yushkevich, P. and Alexander, D.C. and Gee, J.C. and Ciccarelli, O. and Thompson, A.J.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 1},
title = {{An automated approach to connectivity-based partitioning of brain structures.}},
volume = {8},
year = {2005}
}
@article{Avants2007d,
abstract = {Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome.},
author = {Avants, B.B. and Duda, J.T. and Zhang, H. 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 1},
title = {{Multivariate normalization with symmetric diffeomorphisms for multivariate studies.}},
volume = {10},
year = {2007}
}
@article{Tustison2014,
abstract = {Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos ( n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan-Killiany-Tourville (DKT) cortical labeling protocol. We found good scan-rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline. {\textcopyright} 2014 Elsevier Inc.},
author = {Tustison, N.J. and Cook, P.A. and Klein, A. and Song, G. and Das, S.R. and Duda, J.T. and Kandel, B.M. and van Strien, N. and Stone, J.R. and Gee, J.C. and Avants, B.B.},
doi = {10.1016/j.neuroimage.2014.05.044},
issn = {10959572},
journal = {NeuroImage},
keywords = {Advanced Normalization Tools,Age prediction,Gender prediction,MRI,Open science,Scientific reproducibility},
title = {{Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements}},
volume = {99},
year = {2014}
}
@article{Rao2010,
abstract = {The effects of early life experience on later brain structure and function have been studied extensively in animals, yet the relationship between childhood experience and normal brain development in humans remains largely unknown. Using a unique longitudinal data set including ecologically valid in-home measures of early experience during childhood (at age 4 and 8 years) and high-resolution structural brain imaging during adolescence (mean age 14 years), we examined the effects on later brain morphology of two dimensions of early experience: parental nurturance and environmental stimulation. Parental nurturance at age 4 predicts the volume of the left hippocampus in adolescence, with better nurturance associated with smaller hippocampal volume. In contrast, environmental stimulation did not correlate with hippocampal volume. Moreover, the association between hippocampal volume and parental nurturance disappears at age 8, supporting the existence of a sensitive developmental period for brain maturation. These findings indicate that variation in normal childhood experience is associated with differences in brain morphology, and hippocampal volume is specifically associated with early parental nurturance. Our results provide neuroimaging evidence supporting the important role of warm parental care during early childhood for brain maturation. {\textcopyright} 2009 Elsevier Inc. All rights reserved.},
author = {Rao, H. and Betancourt, L. and Giannetta, J.M. and Brodsky, N.L. and Korczykowski, M. and Avants, B.B. and Gee, J.C. and Wang, J. and Hurt, H. and Detre, J.A. and Farah, M.J.},
doi = {10.1016/j.neuroimage.2009.07.003},
issn = {10538119},
journal = {NeuroImage},
keywords = {Childhood experience,Environmental stimulation,Hippocampus,Morphology,Parental nurturance},
number = {1},
title = {{Early parental care is important for hippocampal maturation: Evidence from brain morphology in humans}},
volume = {49},
year = {2010}
}
@inproceedings{Huang2017,
author = {Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q and van der Maaten, Laurens},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
title = {{Densely connected convolutional networks}},
year = {2017}
}
@book{Avants2001,
author = {Avants, B. and Siqueira, M. and Gee, J.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/3-540-45468-3_152},
isbn = {3540426973},
issn = {16113349},
title = {{Computing match functions for curves in R{\textless}sup{\textgreater}2{\textless}/sup{\textgreater}and R{\textless}sup{\textgreater}3{\textless}/sup{\textgreater}by refining polyline approximations}},
volume = {2208},
year = {2001}
}
@article{Habeck:2010aa,
abstract = {As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.},
author = {Habeck, Christian and Stern, Yaakov and {Alzheimer's Disease Neuroimaging Initiative}},
doi = {10.1007/s12013-010-9093-0},
journal = {Cell Biochem Biophys},
month = {nov},
number = {2},
pages = {53--67},
pmid = {20658269},
title = {{Multivariate data analysis for neuroimaging data: overview and application to Alzheimer's disease}},
volume = {58},
year = {2010}
}
@article{Daubechies2008,
abstract = {Regularization of ill-posed linear inverse problems via {\$}\backslashell{\_}1{\$} penalization has been proposed for cases where the solution is known to be (almost) sparse. One way to obtain the minimizer of such an {\$}\backslashell{\_}1{\$} penalized functional is via an iterative soft-thresholding algorithm. We propose an alternative implementation to {\$}\backslashell{\_}1{\$}-constraints, using a gradient method, with projection on {\$}\backslashell{\_}1{\$}-balls. The corresponding algorithm uses again iterative soft-thresholding, now with a variable thresholding parameter. We also propose accelerated versions of this iterative method, using ingredients of the (linear) steepest descent method. We prove convergence in norm for one of these projected gradient methods, without and with acceleration.},
archivePrefix = {arXiv},
arxivId = {0706.4297},
author = {Daubechies, Ingrid and Fornasier, Massimo and Loris, Ignace},
doi = {10.1007/s00041-008-9039-8},
eprint = {0706.4297},
isbn = {1069-5869},
issn = {10695869},
journal = {Journal of Fourier Analysis and Applications},
keywords = {Linear inverse problems,Projected gradient method,Sparse recovery},
title = {{Accelerated projected gradient method for linear inverse problems with sparsity constraints}},
year = {2008}
}
@article{neuRosim,
author = {Welvaert, Marijke and Durnez, Joke and Moerkerke, Beatrijs and Berdoolaege, Geert and Rosseel, Yves},
issn = {1548-7660},
journal = {Journal of Statistical Software},
number = {10},
pages = {1--18},
title = {{neuRosim: An R Package for Generating fMRI Data}},
url = {http://www.jstatsoft.org/v44/i10},
volume = {44},
year = {2011}
}
@inproceedings{Avants2002a,
abstract = {{\textcopyright} 2002 IEEE. We give a method for estimating the optimal rotation between anatomical curves corrupted by segmentation noise. We formulate the energy function probabilistically and use an annealing algorithm with explicit outlier rejection to stabilize our solution. We also provide methods for a nearly symmetric estimation of the rigid transformation between three-dimensional curve pairs. We evaluate the performance of the algorithm numerically with respect to the energy function and computation time.},
author = {Avants, B. and Gee, J.},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2002.1029262},
isbn = {078037584X},
issn = {19458452},
title = {{Robust rotations between anatomical curves}},
volume = {2002-Janua},
year = {2002}
}
@article{Tustison:2010aa,
abstract = {PURPOSE: To propose and test the feasibility of a novel method for quantifying 3D regional pulmonary kinematics from hyperpolarized helium-3 tagged MRI in human subjects using a tailored image processing pipeline and a recently developed nonrigid registration framework.
MATERIALS AND METHODS: Following image acquisition, inspiratory and expiratory tagged (3)He magnetic resonance (MR) images were preprocessed using various image filtering techniques to enhance the tag surfaces. Segmentation of the three orthogonal sets of tag planes in each lung produced distinct point-set representations of the tag surfaces. Using these labeled point-sets, deformation fields and corresponding strain maps were obtained via nonrigid point-set registration. Kinematic analysis was performed on three volunteers.
RESULTS: Tag lines in inspiratory and expiratory images were coregistered producing a continuous 3D correspondence mapping. Average displacement and directional strains were calculated in three subjects in the inferior, mid, and superior portions of the right and left lungs. As expected, the predominant direction of displacements with expiration is from inferior to superior.
CONCLUSION: Kinematic quantitation of pulmonary motion using tagged (3)He MRI is feasible using the applied image preprocessing filtering techniques and nonrigid point-set registration. Potential benefits from regional pulmonary kinematic quantitation include the facilitation of diagnosis and local assessment of disease progression.},
author = {Tustison, Nicholas J and Awate, Suyash P and Cai, Jing and Altes, Talissa A and Miller, G Wilson and de Lange, Eduard E and {Mugler 3rd}, John P and Gee, James C},
doi = {10.1002/jmri.22137},
journal = {J Magn Reson Imaging},
month = {may},
number = {5},
pages = {1236--1241},
pmid = {20432362},
title = {{Pulmonary kinematics from tagged hyperpolarized helium-3 {\{}MRI{\}}}},
volume = {31},
year = {2010}
}
@article{Avants2000,
abstract = {We present an efficient method for the segmentation and axis extraction of vessels and other curvilinear structures in volumetric medical images. The image is treated as a graph from which the user selects seed points to be connected via 1-dimensional paths. A variant of Dijkstra's algorithm both grows the segmenting surface from initial seeds and connects them with a minimal path computation. The technique is local and does not require examination or pre-processing of the entire volume. The surface propagation is controlled by iterative computation of border probabilities. As expanding regions meet, the statistics collected during propagation are passed to an active minimal-path generation module which links the associating points through the vessel tree. We provide a probabilistic basis for the volume search and path- finding speed functions and then apply the algorithm to phantom and real data sets. This work focuses on the contrast-enhanced magnetic resonance angiography (CE-MRA) and computed tomography angiography (CTA) domains, although the framework is adaptable for other purposes. {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.},
author = {Avants, B.B. and Williams, J.P.},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
title = {{An adaptive minimal path generation technique for vessel tracking in CTA/CE-MRA volume images}},
volume = {1935},
year = {2000}
}
@inproceedings{Tustison2013a,
abstract = {The utility of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper- polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of 1H and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule. {\textcopyright} 2013 SPIE.},
author = {Tustison, N.J. and Contrella, B. and Altes, T.A. and Avants, B.B. and {De Lange}, E.E. and {Mugler III}, J.P.},
booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},
doi = {10.1117/12.2006951},
isbn = {9780819494467},
issn = {16057422},
keywords = {Hyperpolarized 3he,Pulmonary segmentation,Regression analysis,Ventilation defects},
title = {{Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates}},
volume = {8672},
year = {2013}
}
@inproceedings{Duda2008,
abstract = {Diffusion tensor (DT) images quantify connectivity patterns in the brain while the Tl modality provides highresolution images of tissue interfaces. Our objective is to use both modalities to build subject-specific, quantitative models of fiber connections in order to discover effects specific to a neural system. The health of this thalamo-cortical network is compromised by traumatic brain injury, and we hypothesize that these effects are due to a primary injury to the thalamus which results in subsequent compromise of radiating fibers. We first use a population-specific average Tl and DT template to label the thalamus and Brodmann areas (BA) 9,10 and 11 in each subject. We also build an expected connection model within this template space that is transferred to subject space in order to provide a prior restriction on probabilistic tracking performed in subject space. We evaluate the effect of traumatic brain injury on this prefrontal-thalamus network by quantifying, in 10 subjects and 8 controls, the mean diffusion and fractional anisotropy along fiber tracts, along with the mean diffusion within the thalamus and cortical regions. We contrast results gained by a template-based tract definition with those gained by performing analysis in the subject space. Both approaches reveal connectivity effects of TBI, specifically a region of reduced FA in the white matter connecting the thalamus to BA 10. {\textcopyright} 2008 IEEE.},
author = {Duda, J. and Avants, B. and Kim, J. and Zhang, H. and Patel, S. and Whyte, J. and Gee, J.},
booktitle = {2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops},
doi = {10.1109/CVPRW.2008.4562992},
isbn = {9781424423408},
title = {{Multivariate analysis of thalamo-cortical connectivity loss in TBI}},
year = {2008}
}
@article{Morgan2011,
abstract = {Quantifiers are very common in everyday speech, but we know little about their cognitive basis or neural representation. The present study examined comprehension of three classes of quantifiers that depend on different cognitive components in patients with focal neurodegenerative diseases. Patients evaluated the truth-value of a sentence containing a quantifier relative to a picture illustrating a small number of familiar objects, and performance was related to MRI grey matter atrophy using voxel-based morphometry. We found that patients with corticobasal syndrome (CBS) and posterior cortical atrophy (PCA) are significantly impaired in their comprehension of cardinal quantifiers (e.g. " At least three birds are on the branch" ), due in part to their deficit in quantity knowledge. MRI analyses related this deficit to temporal-parietal atrophy found in CBS/PCA. We also found that patients with behavioral variant frontotemporal dementia (bvFTD) are significantly impaired in their comprehension of logical quantifiers (e.g. " Some of the birds are on the branch" ), associated with a simple form of perceptual logic, and this correlated with their deficit on executive measures. This deficit was related to disease in rostral prefrontal cortex in bvFTD. These patients were also impaired in their comprehension of majority quantifiers (e.g. " At least half of the birds are on the branch" ), and this too was correlated with their deficit on executive measures. This was related to disease in the basal ganglia interrupting a frontal-striatal loop critical for executive functioning. These findings suggest that a large-scale frontal-parietal neural network plays a crucial role in quantifier comprehension, and that comprehension of specific classes of quantifiers may be selectively impaired in patients with focal neurodegenerative conditions in these areas. {\textcopyright} 2011 Elsevier Ltd.},
author = {Morgan, B. and Gross, R.G. and Clark, R. and Dreyfuss, M. and Boller, A. and Camp, E. and Liang, T.-W. and Avants, B. and McMillan, C.T. and Grossman, M.},
doi = {10.1016/j.neuropsychologia.2011.09.005},
issn = {00283932},
journal = {Neuropsychologia},
keywords = {Comprehension,Corticobasal,Frontal,Frontotemporal dementia,Parietal,Quantifier},
number = {13},
title = {{Some is not enough: Quantifier comprehension in corticobasal syndrome and behavioral variant frontotemporal dementia}},
volume = {49},
year = {2011}
}
@article{Kandel2015b,
abstract = {{\textcopyright} 2014 Elsevier Inc. Rigorous statistical analysis of multimodal imaging datasets is challenging. Mass-univariate methods for extracting correlations between image voxels and outcome measurements are not ideal for multimodal datasets, as they do not account for interactions between the different modalities. The extremely high dimensionality of medical images necessitates dimensionality reduction, such as principal component analysis (PCA) or independent component analysis (ICA). These dimensionality reduction techniques, however, consist of contributions from every region in the brain and are therefore difficult to interpret. Recent advances in sparse dimensio nality reduction have enabled construction of a set of image regions that explain the variance of the images while still maintaining anatomical interpretability. The projections of the original data on the sparse eigenvectors, however, are highly collinear and therefore difficult to incorporate into multi-modal image analysis pipelines. We propose here a method for clustering sparse eigenvectors and selecting a subset of the eigenvectors to make interpretable predictions from a multi-modal dataset. Evaluation on a publicly available dataset shows that the proposed method outperforms PCA and ICA-based regressions while still maintaining anatomical meaning. To facilitate reproducibility, the complete dataset used and all source code is publicly available.},
author = {Kandel, B.M. and Wang, D.J.J. and Gee, J.C. and Avants, B.B.},
doi = {10.1016/j.ymeth.2014.10.016},
issn = {10959130},
journal = {Methods},
keywords = {Magnetic resonance imaging,Multi-modal,Pediatric,Sparse},
title = {{Eigenanatomy: Sparse dimensionality reduction for multi-modal medical image analysis}},
volume = {73},
year = {2015}
}
@article{Ash2009,
abstract = {We investigated the cognitive and neural bases of impaired speech fluency, a central feature of primary progressive aphasia. Speech fluency was assessed in 35 patients with frontotemporal lobar degeneration (FTLD) who presented with progressive non-fluent aphasia (PNFA, n = 11), semantic dementia (SemD, n = 12), or a social and executive disorder without aphasia (SOC/EXEC, n = 12). Fluency was quantified as the number of words per minute in an extended, semi-structured speech sample. This was related to language characteristics of the speech sample and to neuropsychological measures. PNFA patients were significantly less fluent than controls and other FTLD patients. Fluency correlated with grammatical expression but not with speech errors or executive difficulty. SemD and SOC/EXEC patients were also less fluent than controls. In SemD, fluency was associated with semantically limited content. In SOC/EXEC, fluency was associated with executive limitations. Voxel-based morphometry analyses of high-resolution MRI related fluency to gray matter volume in left inferior frontal, insula, and superior temporal regions for the entire cohort of FTLD patients. This region overlapped partially distinct atrophic areas in each FTLD subgroup. It thus appears to play a crucial role in speech fluency, which can be interrupted in different ways in different FTLD subgroups. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.},
author = {Ash, S. and Moore, P. and Vesely, L. and Gunawardena, D. and McMillan, C. and Anderson, C. and Avants, B. and Grossman, M.},
doi = {10.1016/j.jneuroling.2008.12.001},
issn = {09116044},
journal = {Journal of Neurolinguistics},
keywords = {Frontotemporal dementia,MRI,Progressive aphasia,Speech fluency},
number = {4},
title = {{Non-fluent speech in frontotemporal lobar degeneration}},
volume = {22},
year = {2009}
}
@book{Gee2006,
abstract = {{\textcopyright} Springer-Verlag Berlin Heidelberg 2006. In this chapter, we consider the task of anatomically labeling diffusion tensor images of cerebral white matter to facilitate visualization as well as quantitative comparison of these data. The analogous labeling problem for structural magnetic resonance images of the brain has been extensively studied and we propose that advances in atlas-based techniques may be leveraged to anatomically segment the fiber-tractographic maps derived from diffusion tensor data. The feasibility of the approach is demonstrated with data acquired of the corpus callosum, and implications of the results for callosal morphometry are discussed.},
author = {Gee, J.C. and Zhang, H. and Dubb, A. and Avants, B.B. and Yushkevich, P.A. and Duda, J.T.},
booktitle = {Mathematics and Visualization},
doi = {10.1007/3-540-31272-2_8},
issn = {2197666X},
number = {200709},
title = {{Anatomy-based visualizations of diffusion tensor images of brain white matter}},
year = {2006}
}
@article{Tustison2013,
author = {Tustison, N.J. and Johnson, H.J. and Rohlfing, T. and Klein, A. and Ghosh, S.S. and Ibanez, L. and Avants, B.B.},
doi = {10.3389/fnins.2013.00162},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Best practices,Comparative evaluations,Confirmation bias,Open science,Reproducibility},
number = {7 SEP},
title = {{Instrumentation bias in the use and evaluation of scientific software: Recommendations for reproducible practices in the computational sciences}},
year = {2013}
}
@article{Menze2015,
abstract = {{\textcopyright} 2014 IEEE. In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74{\%}-85{\%}), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.},
author = {Menze, B.H. and Jakab, A. and Bauer, S. and Kalpathy-Cramer, J. and Farahani, K. and Kirby, J. and Burren, Y. and Porz, N. and Slotboom, J. and Wiest, R. and Lanczi, L. and Gerstner, E. and Weber, M.-A. and Arbel, T. and Avants, B.B. and Ayache, N. and Buendia, P. and Collins, D.L. and Cordier, N. and Corso, J.J. and Criminisi, A. and Das, T. and Delingette, H. and Demiralp, C. and Durst, C.R. and Dojat, M. and Doyle, S. and Festa, J. and Forbes, F. and Geremia, E. and Glocker, B. and Golland, P. and Guo, X. and Hamamci, A. and Iftekharuddin, K.M. and Jena, R. and John, N.M. and Konukoglu, E. and Lashkari, D. and Mariz, J.A. and Meier, R. and Pereira, S. and Precup, D. and Price, S.J. and Raviv, T.R. and Reza, S.M.S. and Ryan, M. and Sarikaya, D. and Schwartz, L. and Shin, H.-C. and Shotton, J. and Silva, C.A. and Sousa, N. and Subbanna, N.K. and Szekely, G. and Taylor, T.J. and Thomas, O.M. and Tustison, N.J. and Unal, G. and Vasseur, F. and Wintermark, M. and Ye, D.H. and Zhao, L. and Zhao, B. and Zikic, D. and Prastawa, M. and Reyes, M. and {Van Leemput}, K.},
doi = {10.1109/TMI.2014.2377694},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Benchmark,Brain,Image segmentation,MRI,Oncology/tumor},
number = {10},
title = {{The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)}},
volume = {34},
year = {2015}
}
@article{Kim2013,
abstract = {Traumatic brain injury (TBI) has recently been reconceptualized as a chronic, evolving disease process. This new view necessitates quantitative assessment of post-injury changes in brain structure that may allow more accurate monitoring and prediction of recovery. In particular, TBI is known to trigger neurodegenerative processes and therefore quantifying progression of diffuse atrophy over time is currently of utmost interest. However, there are various methodological issues inherent to longitudinal morphometry in TBI that researchers need to be aware of. In this paper, we first overview several of these methodological challenges: lesion evolution, neurosurgical procedures, power, bias, and nonlinearity. We then introduce a sensitive, reliable, and unbiased longitudinal multivariate analysis protocol that combines dimensionality reduction and region of interest approaches. This analysis pipeline is demonstrated using a small dataset consisting of four chronic TBI survivors. {\textcopyright} 2013 Kim, Avants, Whyte and Gee.},
author = {Kim, J. and Avants, B. and Whyte, J. and Gee, J.},
doi = {10.3389/fnhum.2013.00052},
issn = {16625161},
journal = {Frontiers in Human Neuroscience},
keywords = {Bias,Longitudinal,Magnetic resonance imaging,Power,Sparse canonical correlation analysis,Traumatic brain injury},
number = {FEB},
title = {{Methodological considerations in longitudinal morphometry of traumatic brain injury}},
year = {2013}
}
@article{McMillan2013,
abstract = {Background: Frontotemporal lobar degeneration (FTLD) is most commonly associated with TAR-DNA binding protein (TDP-43) or tau pathology at autopsy, but there are no in vivo biomarkers reliably discriminating between sporadic cases. As disease-modifying treatments emerge, it is critical to accurately identify underlying pathology in living patients so that they can be entered into a ppropriate etiology-directed clinical trials. Patients with tau inclusions (FTLD-TAU) appear to have relatively greater white matter (WM) disease at autopsy than those patients with TDP-43 (FTLD-TDP). In this paper, we investigate the ability of white matter (WM) imaging to help discriminate between FTLD-TAU and FTLD-TDP during life using diffusion tensor imaging (DTI). Methods: Patients with autopsy-confirmed disease or a genetic mutation consistent with FTLD-TDP or FTLD-TAU underwent multimodal T1 volumetric MRI and diffusion weighted imaging scans. We quantified cortical thickness in GM and fractional anisotropy (FA) in WM. We performed Eigenanatomy, a statistically robust dimensionality reduction algorithm, and used leave-oneout cross-validation to predict underlying pathology. Neuropathological assessment of GM and WM disease burden was performed in the autopsy-cases to confirm our findings of an ante-mortem GM and WM dissociation in the neuroimaging cohort. Results: ROC curve analyses evaluated classification accuracy in individual patients and revealed 96{\%} sensitivity and 100{\%} specificity for WM analyses. FTLDTAU had significantly more WM degeneration and inclusion severity at autopsy relative to FTLD-TDP. Conclusions: These neuroimaging and neuropathological investigations provide converging evidence for greater WM burden associated with FTLDTAU, and emphasise the role of WM neuroimaging for in vivo discrimination between FTLD-TAU and FTLD-TDP.},
author = {McMillan, C.T. and Irwin, D.J. and Avants, B.B. and Powers, J. and Cook, P.A. and Toledo, J.B. and Wood, E.M. and {Van Deerlin}, V.M. and Lee, V.M.-Y. and Trojanowski, J.Q. and Grossman, M.},
doi = {10.1136/jnnp-2012-304418},
issn = {1468330X},
journal = {Journal of Neurology, Neurosurgery and Psychiatry},
number = {9},
title = {{White matter imaging helps dissociate tau from TDP-43 in frontotemporal lobar degeneration}},
volume = {84},
year = {2013}
}
@book{Tustison2006,
abstract = {Since the 1970's B-splines {\textless} sup {\textgreater} 1 {\textless} /sup {\textgreater}  have evolved to become the de facto standard for use in curve and surface representation. This resulted in a relatively recent proliferation of nonrigid image registration techniques based on B-splines. These techniques fall under the general Free-Form Deformation (FFD) approach in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object represents the transformation space of the registered object. In this paper, we describe the implementation of our finite element methodological (FEM) approach using B-splines. This registration framework subsumes essential components of currently popular FFD image registration algorithms, while providing a more principled and generalized control mechanism for nonrigid deformation. Our implementation constitutes an extension of the existing FEM library of the Insight Toolkit (ITK). We discuss the theoretical implications and provide experimental results of our proposed methodology. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
author = {Tustison, N.J. and Avants, B.B. and Sundaram, T.A. and Duda, J.T. 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_29},
isbn = {3540356487},
issn = {16113349},
title = {{A generalization of free-form deformation Image registration within the ITK finite element framework}},
volume = {4057 LNCS},
year = {2006}
}
@article{Yilmaz:2011aa,
abstract = {In adult canines following major lung resection, the remaining lobes expand asymmetrically, associated with alveolar tissue regrowth, remodeling, and progressive functional compensation over many months. To permit noninvasive longitudinal assessment of regional growth and function, we performed serial high-resolution computed tomography (HRCT) on six male dogs (∼9 mo old, 25.0 {\$}\backslashpm{\$} 4.5 kg, {\$}\backslashpm{\$}SD) at 15 and 30 cmH(2)O transpulmonary pressure (Ptp) before resection (PRE) and 3 and 15 mo postresection (POST3 and POST15, respectively) of 65-70{\%} of lung units. At POST3, lobar air volume increased 83-148{\%} and tissue (including microvascular blood) volume 120-234{\%} above PRE values without further changes at POST15. Lobar-specific compliance (Cs) increased 52-137{\%} from PRE to POST3 and 28-79{\%} from POST3 to POST15. Inflation-related parenchyma strain and shear were estimated by detailed registration of corresponding anatomical features at each Ptp. Within each lobe, regional displacement was most pronounced at the caudal region, whereas strain was pronounced in the periphery. Regional three-dimensional strain magnitudes increased heterogeneously from PRE to POST3, with further medial-lateral increases from POST3 to POST15. Lobar principal strains (PSs) were unchanged or modestly elevated postresection; changes in lobar maximum PS correlated inversely with changes in lobar air and tissue volumes. Lobar shear distortion increased in coronal and transverse planes at POST3 without further changes thereafter. These results establish a novel use of functional HRCT to map heterogeneous regional deformation during compensatory lung growth and illustrate a stimulus-response feedback loop whereby postresection mechanical stress initiates differential lobar regrowth and sustained remodeling, which in turn, relieves parenchyma stress and strain, resulting in progressive increases in lobar Cs and a delayed increase in whole lung Cs.},
author = {Yilmaz, Cuneyt and Tustison, Nicholas J and Dane, D Merrill and Ravikumar, Priya and Takahashi, Masaya and Gee, James C and Hsia, Connie C W},
doi = {10.1152/japplphysiol.00527.2011},
journal = {J Appl Physiol (1985)},
month = {oct},
number = {4},
pages = {1150--1158},
pmid = {21799134},
title = {{Progressive adaptation in regional parenchyma mechanics following extensive lung resection assessed by functional computed tomography}},
volume = {111},
year = {2011}
}
@article{Zhan2018,
abstract = {Recent development of technologies has made it easy to obtain a large amount of multi-view biomedical data from different sources or different preprocessing methods, such as the genome-wide data or electroencephalography (EEG) data. Multi-view intact space learning (MISL), which integrates the complementary information from multiple views to discover a latent intact representation of the data, has shown to be effective in computer vision and data mining tasks. However, it fails in biomedical data analysis since it loses geometric information when projecting data from the original space to the intact space. To overcome this problem, we propose a multi-view intact space learning with geodesic similarity preserving (MISL-GSP). The method first builds one KNN graph for each view and then constructs a geodesic distance matrix that provides a suitable and noise-free similarity for characterizing the intra-view proximity. Based on the geodesic distance matrix, the average similarity among all the views can be obtained, which is used in the manifold regularization term of the multi-view intact space learning for preserving the geodesic similarity. Extensive experiments have been conducted to evaluate the effectiveness of the proposed method, including the clustering experiments on 5 cancer datasets from the cancer genome atlas and the classification experiments on 1 EEG dataset.},
author = {Zhan, Zengrong and Ma, Zhengming and Peng, Wei},
doi = {10.1007/s11063-018-9874-9},
issn = {1573-773X},
journal = {Neural Processing Letters},
month = {jul},
pages = {1},
publisher = {Springer},
title = {{Biomedical Data Analysis Based on Multi-view Intact Space Learning with Geodesic Similarity Preserving}},
url = {http://dx.doi.org/10.1007/s11063-018-9874-9},
year = {2018}
}
@inproceedings{Tustison2007,
abstract = {Due to their computational efficiency and other salient properties, B-splines form the basis not only in comprising the de facto standard for curve and surface representation but also for various nonrigid registration techniques frequently employed in medical image analysis. These registration techniques fall under the rubric of Free-Form Deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object represents the transformation of the registration. Representative, and often cited within the relevant community, of this class of techniques is the formulation of Rueckert et. al [7]  who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained its essential characteristics since Rueckert's incarnation. We assert that such a straightforward gradient-learning is suboptimal in certain cases and to remedy this sub-optimality, we propose a fitting-based strategy for registration in the spirit of Thirion's Demons [14] and directly manipulated free-form deformations [2] , which takes advantage of our previously developed generalized B-spline fitting algorithm [17]. {\textcopyright}2007 IEEE.},
author = {Tustison, N.J. and Avants, B.A. and Gee, J.C.},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2007.4409161},
title = {{Improved FFD B-spline image registration}},
year = {2007}
}
@article{Yushkevich2008a,
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 mm3 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.},
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.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 1},
title = {{Shape-based alignment of hippocampal subfields: evaluation in postmortem MRI.}},
volume = {11},
year = {2008}
}
@article{Jiang2018,
abstract = {Many real-world applications propose the request for sharing knowledge among different tasks or datasets. Transfer learning has been proposed to solve this kind of problems and it has been successfully applied in supervised learning and semi-supervised learning settings. However, its adoption in clustering, one of the most classical research problems in machine learning and data mining, is still scarce. Spectral clustering, as a major clustering algorithm with wide applications and better performance than k-means typically, has not been well incorporated with knowledge transfer. In this paper, we first consider the problem of learning from only one auxiliary unlabeled dataset for spectral clustering and propose a novel algorithm called transfer spectral clustering (TSC). Then, it is extended to the settings with multiple auxiliary tasks. TSC assumes the feature embeddings being shared with the auxiliary tasks and utilizes co-clustering to extract useful information from the auxiliary datasets to improve the clustering performance. TSC involves not only the data manifold information of individual task but also the feature manifold information shared between related tasks. An in-depth explanation of our algorithm together with a convergence analysis are provided. As demonstrated by the extensive experiments, TSC can effectively improve the clustering performance by using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.},
author = {Jiang, Wenhao and Liu, Wei and Chung, Fu-lai},
doi = {10.1016/j.patcog.2018.04.018},
issn = {00313203},
journal = {Pattern Recognition},
title = {{Knowledge transfer for spectral clustering}},
year = {2018}
}
@article{Datta2012,
abstract = {There is a long history and a growing interest in the canine as a subject of study in neuroscience research and in translational neurology. In the last few years, anatomical and functional magnetic resonance imaging (MRI) studies of awake and anesthetized dogs have been reported. Such efforts can be enhanced by a population atlas of canine brain anatomy to implement group analyses. Here we present a canine brain atlas derived as the diffeomorphic average of a population of fifteen mesaticephalic dogs. The atlas includes: 1) A brain template derived from in-vivo, T1-weighted imaging at 1 mm isotropic resolution at 3 Tesla (with and without the soft tissues of the head); 2) A co-registered, high-resolution (0.33 mm isotropic) template created from imaging of ex-vivo brains at 7 Tesla; 3) A surface representation of the gray matter/white matter boundary of the high-resolution atlas (including labeling of gyral and sulcal features). The properties of the atlas are considered in relation to historical nomenclature and the evolutionary taxonomy of the Canini tribe. The atlas is available for download (https://cfn.upenn.edu/aguirre/wiki/public:data{\_}plosone{\_}2012{\_}datta). {\textcopyright} 2012 Datta et al.},
author = {Datta, R. and Lee, J. and Duda, J. and Avants, B.B. and Vite, C.H. and Tseng, B. and Gee, J.C. and Aguirre, G.D. and Aguirre, G.K.},
doi = {10.1371/journal.pone.0052140},
issn = {19326203},
journal = {PLoS ONE},
number = {12},
title = {{A Digital Atlas of the Dog Brain}},
volume = {7},
year = {2012}
}
@article{Hopkins2013,
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 = {00034819},
journal = {Annals of Internal Medicine},
number = {6},
title = {{Regional and hemispheric variation in cortical thickness in chimpanzees (Pan troglodytes)}},
volume = {158},
year = {2013}
}
@article{Dubb2003,
abstract = {Despite decades of research, there is still no agreement over the presence of gender-based morphologic differences in the human corpus callosum. We approached the problem using a highly precise computational technique for shape comparison. Starting with a prospectively acquired sample of cranial MRIs of healthy volunteers (age ranges 18-84), the variations of individual callosa are quantified with respect to a reference callosum shape in the form of Jacobian determinant maps derived from the geometric transformations that map the reference callosum into anatomic alignment with the subject callosa. Voxelwise t tests performed over the determinant values demonstrated that females had a larger splenium than males (P  {\textless}  0.001 uncorrected for multiple comparisons) while males possessed a larger genu (P  {\textless}  0.001). In addition, pointwise Pearson plots using age as a correlate showed a different pattern of age-related changes in male and female callosa, with female splenia tending to expand more with age, while the male genu tended to contract. Our results demonstrate significant morphologic differences in the corpus callosum between genders and a possible sex difference in the neuro-developmental cycle. {\textcopyright} 2003 Elsevier Inc. All rights reserved.},
author = {Dubb, A. and Gur, R. and Avants, B. and Gee, J.},
doi = {10.1016/S1053-8119(03)00313-6},
issn = {10538119},
journal = {NeuroImage},
number = {1},
title = {{Characterization of sexual dimorphism in the human corpus callosum}},
volume = {20},
year = {2003}
}
@book{Avants2010,
abstract = {We present a new algorithm for reliable, unbiased, multivariate longitudinal analysis of cortical and white matter atrophy rates with penalized statistical methods. The pipeline uses a step-wise approach to transform and personalize template information first to a single-subject template (SST) and then to the individual's time series data. The first stream of information flows from group template to the SST; the second flows from the SST to the individual time-points and provides unbiased, prior-based segmentation and measurement of cortical thickness. MRI-bias correction, consistent longitudinal segmentation, cortical parcellation and cortical thickness estimation are all based on strong use of the subject-specific priors built from initial diffeomorphic mapping between the SST and optimal group template. We evaluate our approach with both test-retest data and with application to a driving biological problem. We use test-retest data to show that this approach produces (a) zero change when the retest data contains the same image content as the test data and (b) produces normally distributed, low variance estimates of thickness change centered at zero when test-retest data is collected near in time to test data. We also show that our approach-when combined with sparse canonical correlation analysis-reveals plausible, significant, annualized decline in cortical thickness and white matter volume when contrasting frontotemporal dementia and normal aging. {\textcopyright} 2010 Springer-Verlag.},
author = {Avants, B. and Cook, P.A. and McMillan, C. and Grossman, M. and Tustison, N.J. and Zheng, Y. and Gee, J.C.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-15705-9_40},
isbn = {3642157041},
issn = {03029743},
number = {PART 1},
title = {{Sparse unbiased analysis of anatomical variance in longitudinal imaging}},
volume = {6361 LNCS},
year = {2010}
}
@article{Das2009,
abstract = {The hippocampus is a major structure of interest affected by temporal lobe epilepsy (TLE). Region of interest (ROI)-based analysis has traditionally been used to study hippocampal involvement in TLE, although spatial variation of structural and functional pathology have been known to exist within the ROI. In this article, structure-specific analysis (Yushkevich et al. (2007) Neuroimage 35:1516-1530) is applied to the study of both structure and function in TLE patients. This methodology takes into account information about the spatial correspondence of voxels within ROIs on left and right sides of the same subject as well as between subjects. Hippocampal thickness is studied as a measure of structural integrity, and functional activation in a functional magnetic resonance imaging (fMRI) experiment in which subjects performed a memory encoding task is studied as a measure of functional integrity. Pronounced disease-related decrease in thickness is found in posterior and anterior hippocampus. A region in the body also shows increased thickness in patients' healthy hippocampi compared with controls. Functional activation in diseased hippocampi is reduced in the body region compared to controls, whereas a region in the tail showing greater right-lateralized activation in controls also shows greater activation in healthy hippocampi compared with the diseased side in patients. Summary measurements generated by integrating quantities of interest over the entire hippocampus can also be used, as is done in conventional ROI analysis. {\textcopyright} 2009 Wiley-Liss, Inc.},
author = {Das, S.R. and Mechanic-Hamilton, D. and Korczykowski, M. and Pluta, J. and Glynn, S. and Avants, B.B. and Detre, J.A. and Yushkevich, P.A.},
doi = {10.1002/hipo.20620},
issn = {10509631},
journal = {Hippocampus},
keywords = {Hippocampus,Shape analysis,Structure-specific,Temporal lobe epilepsy,fMRI},
number = {6},
title = {{Structure specific analysis of the hippocampus in temporal lobe epilepsy}},
volume = {19},
year = {2009}
}
@inproceedings{Adler2012,
abstract = {In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology. {\textcopyright} 2012 IEEE.},
author = {Adler, D.H. and Liu, A.Y. and Pluta, J. and Kadivar, S. and Orozco, S. and Wang, H. and Gee, J.C. and Avants, B.B. and Yushkevich, P.A.},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2012.6235542},
isbn = {9781457718588},
issn = {19457928},
keywords = {Hippocampus,histology,reconstruction,registration,segmentation},
title = {{Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI}},
year = {2012}
}
@article{Avants2014a,
author = {Avants, Brian and Ntustison},
doi = {doi:10.6084/m9.figshare.923555},
month = {feb},
title = {{The Pediatric Template of Brain Perfusion (PTBP)}},
url = {http://figshare.com/articles/The{\_}Pediatric{\_}Template{\_}of{\_}Brain{\_}Perfusion{\_}PTBP{\_}/923555},
year = {2014}
}
@book{Xie2014,
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 (0.4×0.4×2.0mm 3 ) 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). {\textcopyright} 2014 Springer International Publishing.},
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.},
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_11},
isbn = {9783319104423},
issn = {16113349},
number = {PART 3},
title = {{Automatic clustering and thickness measurement of anatomical variants of the human perirhinal cortex}},
volume = {8675 LNCS},
year = {2014}
}
@misc{Wang2012,
abstract = {In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. ?? 2012 .},
author = {Wang, Shijun and Summers, Ronald M.},
booktitle = {Medical Image Analysis},
doi = {10.1016/j.media.2012.02.005},
isbn = {1361-8415},
issn = {13618415},
keywords = {Computer-aided detection and diagnosis,Image segmentation,Machine learning,Radiology,Survey},
number = {5},
pages = {933--951},
pmid = {22465077},
title = {{Machine learning and radiology}},
volume = {16},
year = {2012}
}
@book{Avants2007a,
abstract = {Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate dataseis provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
author = {Avants, B.B. and Duda, J.T. 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)},
isbn = {9783540757566},
issn = {03029743},
number = {PART 1},
title = {{Multivariate normalization with symmetric diffeomorphisms for multivariate studies}},
volume = {4791 LNCS},
year = {2007}
}
@article{Barbosa:2011aa,
abstract = {RATIONALE AND OBJECTIVES: Obstructive pulmonary disease phenotypes are related to variable combinations of emphysema and small-airway disease, the latter manifested as air trapping (AT) on imaging. The investigators propose a method to extract AT information quantitatively from thoracic multi-detector row high-resolution computed tomography (HRCT), validated by pulmonary function testing (PFT) correlation.
MATERIALS AND METHODS: Seventeen patients with obstructive pulmonary disease who underwent HRCT and PFT within a 3-day interval were retrospectively identified. Thin-section volumetric HRCT in inspiration and expiration was registered and analyzed using custom-made software. Nonaerated regions of lung were segmented through exclusion of voxels {\textgreater} -50 Hounsfield units (HU); emphysematous areas were segmented as voxels {\textless} -950 HU on inspiratory images. Small-airway AT volume (ATV) was segmented as regions of lung voxels whose attenuation values increased by less than a specified change threshold (set from 5 to 300 HU in 25-HU increments) between inspiration and expiration. Inspiratory and expiratory total segmented lung volumes, emphysema volume (EV), and ATV for each threshold were subsequently calculated and correlated with PFT parameters.
RESULTS: A strong positive correlation was obtained between total segmented lung volume in inspiration and total lung capacity (r = 0.83). A strong negative correlation (r = -0.80) was obtained between EV and the ratio between forced expiratory volume in 1 second and forced vital capacity. Stronger negative correlation with forced expiratory volume in 1 second/forced vital capacity (r = -0.85) was demonstrated when ATV (threshold, 50 HU) was added to EV, indicating improved quantification of total AT to predict obstructive disease severity. A moderately strong positive correlation between ATV and residual volume was observed, with a maximum r value of 0.72 (threshold, 25 HU), greater than that between EV and residual volume (r = 0.58). The benefit of ATV quantification was greater in a subgroup of patients with negligible emphysema compared to patients with moderate to severe emphysema.
CONCLUSIONS: Small-airway AT segmentation in conjunction with emphysema segmentation through computer-assisted methodologies may provide better correlations with key PFT parameters, suggesting that the quantification of emphysema-related and small airway-related components of AT from thoracic HRCT has great potential to elucidate phenotypic differences in patients with chronic obstructive pulmonary disease.},
author = {{Barbosa Jr}, Eduardo Mortani and Song, Gang and Tustison, Nicholas and Kreider, Maryl and Gee, James C and Gefter, Warren B and Torigian, Drew A},
doi = {10.1016/j.acra.2011.06.004},
journal = {Acad Radiol},
month = {oct},
number = {10},
pages = {1258--1269},
pmid = {21893294},
title = {{Computational analysis of thoracic multidetector row {\{}HRCT{\}} for segmentation and quantification of small airway air trapping and emphysema in obstructive pulmonary disease}},
volume = {18},
year = {2011}
}
@article{Murphy2011,
abstract = {EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed. {\textcopyright} 2011 IEEE.},
author = {Murphy, K. and {Van Ginneken}, B. and Reinhardt, J.M. and Kabus, S. and Ding, K. and Deng, X. and Cao, K. and Du, K. and Christensen, G.E. and Garcia, V. and Vercauteren, T. and Ayache, N. and Commowick, O. and Malandain, G. and Glocker, B. and Paragios, N. and Navab, N. and Gorbunova, V. and Sporring, J. and {De Bruijne}, M. and Han, X. and Heinrich, M.P. and Schnabel, J.A. and Jenkinson, M. and Lorenz, C. and Modat, M. and McClelland, J.R. and Ourselin, S. and Muenzing, S.E.A. and Viergever, M.A. and {De Nigris}, D. and Collins, D.L. and Arbel, T. and Peroni, M. and Li, R. and Sharp, G.C. and Schmidt-Richberg, A. and Ehrhardt, J. and Werner, R. and Smeets, D. and Loeckx, D. and Song, G. and Tustison, N. and Avants, B. and Gee, J.C. and Staring, M. and Klein, S. and Stoel, B.C. and Urschler, M. and Werlberger, M. and Vandemeulebroucke, J. and Rit, S. and Sarrut, D. and Pluim, J.P.W.},
doi = {10.1109/TMI.2011.2158349},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Chest,computed tomography,evaluation,registration},
number = {11},
title = {{Evaluation of registration methods on thoracic CT: The EMPIRE10 challenge}},
volume = {30},
year = {2011}
}
@article{Wang2010b,
abstract = {Arterial spin labeling (ASL) provides noninvasive measurement of tissue blood flow, but sensitivity to motion has limited its application to imaging of myocardial blood flow. Although different cardiac phases can be synchronized using electrocardiography triggering, breath holding is generally required to minimize effects of respiratory motion during ASL scanning, which may be challenging in clinical populations. Here a free-breathing myocardial ASL technique with the potential for reliable clinical application is presented, by combining ASL with a navigator-gated, electrocardiography-triggered TrueFISP readout sequence. Dynamic myocardial perfusion signals were measured at multiple delay times that allowed simultaneous fitting of myocardial blood flow and arterial transit time. With the assist of a nonrigid motion correction program, the estimated mean myocardial blood flow was 1.00 ± 0.55 mL/g/min with a mean transit time of ∼400 msec. The intraclass correlation coefficient of repeated scans was 0.89 with a mean within subject coefficient of variation of 22{\%}. Perfusion response during mild to moderate stress was further measured. The capability for noninvasive, free-breathing assessment of myocardial blood flow using ASL may offer an alternative approach to first-pass perfusion MRI for clinical evaluation of patients with coronary artery disease. {\textcopyright} 2010 Wiley-Liss, Inc.},
author = {Wang, D.J.J. and Bi, X. and Avants, B.B. and Meng, T. and Zuehlsdorff, S. and Detre, J.A.},
doi = {10.1002/mrm.22630},
issn = {07403194},
journal = {Magnetic Resonance in Medicine},
keywords = {Arterial spin labeling,Free-breathing,Myocardial blood flow,Navigator-echo,Nonrigid motion correction},
number = {5},
title = {{Estimation of perfusion and arterial transit time in myocardium using free-breathing myocardial arterial spin labeling with navigator-echo}},
volume = {64},
year = {2010}
}
@book{Avants2005,
abstract = {We develop a practical, symmetric, data-driven formulation, geodesic image interpolation (GII), for interpolating images with respect to geometric and photometric variables. GII captures, in implementation, the desirable properties of symmetry that comes from the theory of diffeomorphisms and Grenander's computational anatomy (CA). Geodesic diffeomorphisms are a desirable transformation model as they provide a symmetric deforming path connecting images or a series of images. Once estimated, this geodesic may be used to (re)parameterize and interpolate image sets in approximation of continuous, deforming dynamic processes. One may then closely recover the original continuous signal from a few samples. The method, based on our work in symmetric diffeomorphic image registration, generalizes the concept of point set reparameterization to the case where point sets are replaced by image sets. This problem differs from point reparameterization in that a variational image correspondence problem must be solved before resampling. Our image reparameterization method is applied to solve similar problems to point reparameterization: dense interpolation, matching and simulation of dynamic processes are illustrated. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
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)},
doi = {10.1007/11567646_21},
isbn = {3540293485},
issn = {03029743},
title = {{Geodesic image interpolation: Parameterizing and interpolating spatiotemporal images}},
volume = {3752 LNCS},
year = {2005}
}
@article{Badea2012,
abstract = {Sophisticated image analysis methods have been developed for the human brain, but such tools still need to be adapted and optimized for quantitative small animal imaging. We propose a framework for quantitative anatomical phenotyping in mouse models of neurological and psychiatric conditions. The framework encompasses an atlas space, image acquisition protocols, and software tools to register images into this space. We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols. We present a flexible approach for segmenting such hyperimages, optimizing registration, and identifying optimal combinations of image channels for particular structures. Brain imaging with T1, T2* and T2 contrasts yielded accuracy in the range of 83{\%} for hippocampus and caudate putamen (Hc and CPu), but only 54{\%} in white matter tracts, and 44{\%} for the ventricles. The addition of diffusion tensor parameter images improved accuracy for large gray matter structures (by  {\textgreater} 5{\%}), white matter (10{\%}), and ventricles (15{\%}). The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6{\%}; so Dice coefficients for Hc and CPu reached 93{\%}, for white matter 79{\%}, for ventricles 68{\%}, and for substantia nigra 80{\%}. We demonstrate the segmentation pipeline for the widely used C57BL/6 strain, and two test strains (BXD29, APP/TTA). This approach appears promising for characterizing temporal changes in mouse models of human neurological and psychiatric conditions, and may provide anatomical constraints for other preclinical imaging, e.g. fMRI and molecular imaging. This is the first demonstration that multiple MR imaging modalities combined with multivariate segmentation methods lead to significant improvements in anatomical segmentation in the mouse brain. {\textcopyright} 2012 Elsevier Inc.},
author = {Badea, A. and Gewalt, S. and Avants, B.B. and Cook, J.J. and Johnson, G.A.},
doi = {10.1016/j.neuroimage.2012.07.021},
issn = {10538119},
journal = {NeuroImage},
keywords = {Alzheimer's mouse model,Automated segmentation,BXD,Magnetic resonance microscopy,Mouse brain,Phenotyping},
number = {3},
title = {{Quantitative mouse brain phenotyping based on single and multispectral MR protocols}},
volume = {63},
year = {2012}
}
@book{Kandel2013,
abstract = {We present a new framework for predicting cognitive or other continuous-variable data from medical images. Current methods of probing the connection between medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly cognitive function is related to neuroanatomical structure. On the other hand, high-dimensional machine learning techniques do not typically provide a direct method for discovering which parts of the brain are used for making predictions. We present a framework, based on recent work in sparse linear regression, that addresses both drawbacks of mass univariate approaches, while preserving the direct spatial interpretability that they provide. In addition, we present a novel optimization algorithm that adapts the conjugate gradient method for sparse regression on medical imaging data. This algorithm produces coefficients that are more interpretable than existing sparse regression techniques. {\textcopyright} 2013 Springer-Verlag.},
author = {Kandel, B.M. and Wolk, D.A. and Gee, J.C. and Avants, B.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-38868-2_8},
isbn = {9783642388675},
issn = {03029743},
title = {{Predicting cognitive data from medical images using sparse linear regression}},
volume = {7917 LNCS},
year = {2013}
}
@article{Tao2015,
abstract = {In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object. },
author = {Tao, Jianwen and Wen, Shiting and Hu, Wenjun},
doi = {10.1016/j.neunet.2015.01.009},
issn = {1879-2782},
journal = {Neural networks : the official journal of the International Neural Network Society},
keywords = {Algorithms; Humans; Linear Models; Nonlinear Dynamics; Supervised Machine Learning; Graph regularization; Graph-based semi-supervised learning; L1-norm locally linear representation; Multi-source adaptation learning},
month = {sep},
pages = {80--98},
pmid = {26091754},
title = {{L1-norm locally linear representation regularization multi-source adaptation learning.}},
volume = {69},
year = {2015}
}
@article{Hanson2012a,
abstract = {A large corpus of research indicates that exposure to stress impairs cognitive abilities, specifically executive functioning dependent on the prefrontalcortex(PFC).WecollectedstructuralMRIscans(n$\beta$61),well-validatedassessmentsofexecutivefunctioning,anddetailedinterviews assessing stress exposure in humans to examine whether cumulative life stress affected brain morphometry and one type of executive functioning, spatial working memory, during adolescence-a critical time of brain development and reorganization. Analysis of variations in brain structure revealed that cumulative life stressandspatialworkingmemorywererelated to smallervolumesin thePFC,specifically prefrontal gray and white matter between the anterior cingulate and the frontal poles. Mediation analyses revealed that individual differences in prefrontal volumesaccounted for the association between cumulative life stressandspatial workingmemory.These results suggest that structural changes in the PFC may serve as a mediating mechanism through which greater cumulative life stress engenders decrements in cognitive functioning. {\textcopyright} 2012 the authors.},
author = {Hanson, J.L. and Chung, M.K. and Avants, B.B. and Rudolph, K.D. and Shirtcliff, E.A. and Gee, J.C. and Davidson, R.J. and Pollak, S.D.},
doi = {10.1523/JNEUROSCI.0307-12.2012},
issn = {02706474},
journal = {Journal of Neuroscience},
number = {23},
title = {{Structural variations in prefrontal cortex mediate the relationship between early childhood stress and spatial working memory}},
volume = {32},
year = {2012}
}
@article{Avants2005a,
abstract = {This study uses large deformation medical image registration to analyze, in a disease-specific normalized space, the annual rate of gray matter atrophy caused by frontotemporal dementia (FTD) and its correlation with cognitive decline. The analysis consists of three parts. First, a labeled structural MRI atlas is deformed into the shape of an average FTD brain. Second, annualized FTD-related atrophy of gray matter structures is estimated for each patient in the database. Third, the group-wise annualized atrophy rate caused by FTD is correlated, for each gray matter voxel, with declining performance on cognitive tests. This study gives insight into the relationship between FTD-related progressive cortical atrophy and loss in cognitive function. Copyright {\textcopyright} 2005 by Lippincott Williams  {\&}  Wilkins.},
author = {Avants, B. and Grossman, M. and Gee, J.C.},
doi = {10.1097/01.wad.0000183083.14939.82},
issn = {08930341},
journal = {Alzheimer Disease and Associated Disorders},
keywords = {Alzheimer disease,Diffeomorphic morphometry,Frontotemporal dementia},
number = {SUPPL. 1},
title = {{The correlation of cognitive decline with frontotemporal dementia induced annualized gray matter loss using diffeomorphic morphometry}},
volume = {19},
year = {2005}
}
@article{Pluta2009,
abstract = {We present a robust, high-throughput, semiautomated template-based protocol for segmenting the hippocampus in temporal lobe epilepsy. The semiautomated component of this approach, which minimizes user effort while maximizing the benefit of human input to the algorithm, relies on "incomplete labeling." Incomplete labeling requires the user to quickly and approximately segment a few key regions of the hippocampus through a user-interface. Subsequently, this partial labeling of the hippocampus is combined with image similarity terms to guide volumetric diffeomorphic normalization between an individual brain and an unbiased disease-specific template, with fully labeled hippocampi. We solve this many-to-few and few-to-many matching problem, and gain robustness to inter and intrarater variability and small errors in user labeling, by embedding the template-based normalization within a probabilistic framework that examines both label geometry and appearance data at each label. We evaluate the reliability of this framework with respect to manual labeling and show that it increases minimum performance levels relative to fully automated approaches and provides high inter-rater reliability. Thus, this approach does not require expert neuroanatomical training and is viable for high-throughput studies of both the normal and the highly atrophic hippocampus. {\textcopyright} 2009 Wiley-Liss, Inc.},
author = {Pluta, J. and Avants, B.B. and Glynn, S. and Awate, S. and Gee, J.C. and Detre, J.A.},
doi = {10.1002/hipo.20619},
issn = {10509631},
journal = {Hippocampus},
keywords = {Hippocampus,Normalization,Segmentation},
number = {6},
title = {{Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation}},
volume = {19},
year = {2009}
}
@article{Bonner2009,
abstract = {Patients with semantic dementia (SD) have a striking impairment in semantic memory, but the basis for this deficit is unclear. We examined semantic memory for concrete and abstract verbs with a two-alternative, forced-choice measure of lexical semantic associative knowledge. Patients with SD had significantly greater difficulty with concrete verbs (z = -3.33) than with abstract verbs (z = -2.05), a "reversal of the concreteness effect" that was present in a majority of individual patients. The subgroup of SD patients with imaging had significant cortical thinning in the anterior and inferolateral portions of the temporal lobes. These areas of visual association cortex may be important for storing and processing visual features for word meaning. Moreover, poor performance with concrete relative to abstract verbs correlated with cortical thinning of the right anterior temporal lobe in SD, suggesting that this region may contribute to storing and processing visual semantic features. These observations raise the possibility that degraded visual feature knowledge contributes in part to the impaired comprehension of concrete words in SD. {\textcopyright} 2009 Psychology Press.},
author = {Bonner, M.F. and Vesely, L. and Price, C. and Anderson, C. and Richmond, L. and Farag, C. and Avants, B. and Grossman, M.},
doi = {10.1080/02643290903512305},
issn = {02643294},
journal = {Cognitive Neuropsychology},
keywords = {Semantic,Semantic dementia,Verb},
number = {6},
title = {{Reversal of the concreteness effect in semantic dementia}},
volume = {26},
year = {2009}
}
@article{Tzourio-Mazoyer2002,
abstract = {An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.},
author = {Tzourio-Mazoyer, N and Landeau, B and Papathanassiou, D and Crivello, F and Etard, O and Delcroix, N and Mazoyer, B and Joliot, M},
doi = {10.1006/nimg.2001.0978},
institution = {Groupe d'Imagerie Neurofonctionnelle, UMR 6095 CNRS CEA, Universit{\'{e}} de Caen, Universit{\'{e}} de Paris 5, France.},
journal = {Neuroimage},
keywords = { Cerebral, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Reference Values, anatomy /{\&}/ histology; Computer Graphics; Dominance, physiology; Humans; Image Processing,Brain Mapping; Brain},
month = {jan},
number = {1},
pages = {273--289},
pmid = {11771995},
title = {{Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.}},
url = {http://dx.doi.org/10.1006/nimg.2001.0978},
volume = {15},
year = {2002}
}
@article{Gross2012,
abstract = {Prior work has related sentence processing to executive deficits in non-demented patients with Parkinson's disease (PD). We extended this investigation to patients with dementia with Lewy bodies (DLB) and PD dementia (PDD) by examining grammatical and working memory components of sentence processing in the full range of patients with Lewy body spectrum disorder (LBSD). Thirty-three patients with LBSD were given a two-alternative, forced-choice sentence-picture matching task. Sentence type, working memory, and grammatical structure were systematically manipulated in the sentences. We found that patients with PDD and DLB were significantly impaired relative to non-demented PD patients and healthy controls. The deficit in PDD/DLB was most pronounced for sentences lengthened by the strategic placement of an additional prepositional phrase and for sentences with an additional proposition due to a center-embedded clause. However, there was no effect for subject-relative versus object-relative grammatical structure. An MRI voxel-based morphometry analysis in a subset of patients showed significant gray matter thinning in the frontal lobe bilaterally, and this extended to temporal, parietal and occipital regions. A regression analysis related sentence processing difficulty in LBSD to frontal neocortex, including inferior prefrontal, premotor, and dorsolateral prefrontal regions, as well as right superior temporal cortex. These findings are consistent with the hypothesis that patients with PDD and DLB have difficulty processing sentences with increased working memory demands and that this deficit is related in part to their frontal disease. {\textcopyright} 2011 Elsevier Inc.},
author = {Gross, R.G. and McMillan, C.T. and Chandrasekaran, K. and Dreyfuss, M. and Ash, S. and Avants, B. and Cook, P. and Moore, P. and Libon, D.J. and Siderowf, A. and Grossman, M.},
doi = {10.1016/j.bandc.2011.12.004},
issn = {02782626},
journal = {Brain and Cognition},
keywords = {Lewy body,MRI,Parkinson's,Prefrontal,Sentence processing,Working memory},
number = {2},
title = {{Sentence processing in Lewy body spectrum disorder: The role of working memory}},
volume = {78},
year = {2012}
}
@book{Avants2003a,
abstract = {We extend a symmetric parametric curve matching algorithm designed for recognition and morphometry by incorporating Gaussian smoothing and curvature scale-space. A general statement of the matching theory and the properties of the associated algorithm is given. Gaussian smoothing is used to assist in approximating the continuous solution from the discrete solution given by dynamic programming. The method is then investigated in a multi-scale framework, which has the advantage of reducing the effects of noise and occlusion. A novel scale-space derived energy functional that incorporates geometric information from many scales at once is proposed. The related issue of selecting a smoothing kernel for a given matching problem is also explored, resulting in a topologically based method of scale-selection. This application requires estimating the matching between the fine and coarse scale versions of the same curve. We provide a tool for finding this inter-scale, intra-curve correspondence, based on tracking curvature extrema through scales. These novel algorithms are demonstrated on both 2D and 3D data. {\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 = {{Continuous curve matching with scale-space curvature and extrema-based scale selection}},
volume = {2695},
year = {2003}
}
@article{Gennatas2017,
abstract = {{\textcopyright} 2017 the authors. Developmental structural neuroimaging studies in humans have long described decreases in gray matter volume (GMV) and cortical thickness (CT) during adolescence. Gray matter density (GMD), a measure often assumed to be highly related to volume, has not been systematically investigated in development. We used T1 imaging data collected on the Philadelphia Neurodevelopmental Cohort to study age-related effects and sex differences in four regional gray matter measures in 1189 youths ranging in age from 8 to 23 years. Custom T1 segmentation and a novel high-resolution gray matter parcellation were used to extract GMD, GMV, gray matter mass (GMM; defined as GMD ☓ GMV), and CT from 1625 brain regions. Nonlinear models revealed that each modality exhibits unique age-related effects and sex differences. While GMV and CT generally decrease with age, GMD increases and shows the strongest age-related effects, while GMM shows a slight decline overall. Females have lower GMV but higher GMD than males throughout the brain. Our findings suggest that GMD is a prime phenotype for the assessment of brain development and likely cognition and that periadolescent gray matter loss may be less pronounced than previously thought. This work highlights the need for combined quantitative histological MRI studies.},
author = {Gennatas, E.D. and Avants, B.B. and Wolf, D.H. and Satterthwaite, T.D. and Ruparel, K. and Ciric, R. and Hakonarson, H. and Gur, R.E. and Gur, R.C.},
doi = {10.1523/JNEUROSCI.3550-16.2017},
issn = {15292401},
journal = {Journal of Neuroscience},
keywords = {Brain structure,Cortical thickness,Gray matter density,Gray matter volume,MRI,T1-weighted imaging},
number = {20},
title = {{Age-related effects and sex differences in gray matter density, volume, mass, and cortical thickness from childhood to young adulthood}},
volume = {37},
year = {2017}
}
@book{Tustison2012,
abstract = {Motivated by previous work [16] and recent diffeomorphic image registration developments in which the characteristic velocity field is represented by spatiotemporal B-splines [3] , we present a diffeomorphic B-spline-based image registration algorithm combining and extending these techniques. The advancements of the proposed framework over previous work include a preconditioned gradient descent algorithm and potential weighting of the metric gradient permitting, among other things, enforcement of stationary boundary conditions. In addition to theoretical and practical discussions of our contribution, we also describe its parallelized implementation as open source in the Insight Toolkit and perform an evaluation on publicly available brain data. {\textcopyright} 2012 Springer-Verlag.},
author = {Tustison, N.J. and Avants, B.B.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-31340-0_4},
isbn = {9783642313394},
issn = {03029743},
keywords = {B-splines,DMFFD,ITK,diffeomorphisms,image registration},
title = {{Diffeomorphic directly manipulated free-form deformation image registration via vector field flows}},
volume = {7359 LNCS},
year = {2012}
}
@article{Manjon:2010aa,
abstract = {The automated segmentation of vessel tree structures is a crucial preprocessing stage in computer aided diagnosis (CAD) schemes of interstitial lung disease (ILD) patterns in multidetector computed tomography (MDCT). The accuracy of such preprocessing stages is expected to influence the accuracy of lung CAD schemes. Although algorithms aimed at improving the accuracy of lung fields segmentation in presence of ILD have been reported, the corresponding vessel tree segmentation stage is under-researched. Furthermore, previously reported vessel tree segmentation methods have only dealt with normal lung parenchyma. In this paper, an automated vessel tree segmentation scheme is proposed, adapted to the presence of pathologies affecting lung parenchyma. The first stage of the method accounts for a recently proposed method utilizing a 3-D multiscale vessel enhancement filter based on eigenvalue analysis of the Hessian matrix and on unsupervised segmentation. The second stage of the method is a texture-based voxel classification refinement to correct possible over-segmentation. The performance of the proposed scheme, and of the previously reported technique, in vessel tree segmentation was evaluated by means of area overlap (previously reported: 0.715 {\$}\backslashpm{\$} 0.082, proposed: 0.931 {\$}\backslashpm{\$} 0.027), true positive fraction (previously reported: 0.968 {\$}\backslashpm{\$} 0.019, proposed: 0.935 {\$}\backslashpm{\$} 0.036) and false positive fraction (previously reported: 0.400 {\$}\backslashpm{\$} 0.181, proposed: 0.074 {\$}\backslashpm{\$} 0.031) on a dataset of 210 axial slices originating from seven ILD affected patient scans (used for performance evaluation out of 15). The proposed method demonstrated a statistically significantly ( p {\textless} 0.05) higher performance as compared to the previously reported vessel tree segmentation technique. The impact of suboptimal vessel tree segmentation in a reticular pattern quantification system is also demonstrated.},
author = {Stone, James R and Wilde, Elisabeth A and Taylor, Brian A and Tate, David F and Levin, Harvey and Bigler, Erin D and Scheibel, Randall S and Newsome, Mary R and Mayer, Andrew R and Abildskov, Tracy and Black, Garrett M and Lennon, Michael J and York, Gerald E and Agarwal, Rajan and DeVillasante, Jorge and Ritter, John L and Walker, Peter B and Ahlers, Stephen T and Tustison, Nick J Nicholas Nick Nicholas J and Hutchison, R Matthew and Chiao, P and Avants, Brian B A and Poggio, T and Girosi, F and Miller, Michael R I and Beg, M Faisal and Ceritoglu, Can and Stark, Craig and Jenkinson, Mark and Smith, Stephen M and Peyrat, Jean-Marc and Delingette, Herv{\'{e}} and Sermesant, Maxime and Xu, Chenyang and Ayache, Nicholas and Christensen, Gary E and Rabbitt, R D and Miller, Michael R I and Rueckert, D and Sonoda, L I and Hayes, C and Hill, Derek L G and Leach, M O and Hawkes, D J and Tustison, Nick J Nicholas Nick Nicholas J and Avants, Brian B A and Wang, Hongzhi and Xie, L and Coupe, P and Yushkevich, Paul A and Manjon, J and Cook, P A and Avants, Brian B A and Stone, James R and Yushkevich, Paul A and Tustison, Nick J Nicholas Nick Nicholas J and Manj{\'{o}}n, Jos{\'{e}} V and Fedorov, Andriy and Beichel, Reinhard and Kalpathy-Cramer, Jayashree and Finet, Julien and Fillion-Robin, Jean-Christophe and Pujol, Sonia and Bauer, Christian and Jennings, Dominique and Fennessy, Fiona and Sonka, Milan and Buatti, John and Aylward, Stephen and Miller, James V and Pieper, Steve and Kikinis, Ron and Vercauteren, Tom and Pennec, Xavier and Perchant, Aymeric and Ayache, Nicholas and Klein, Stefan and Staring, Marius and Murphy, Keelin and Viergever, Max A and Pluim, Josien P W and Zhu, Y and Tan, Yongqiang and Hua, Y and Zhang, G and Zhang, J and Lovinfosse, Pierre and Janvary, Zsolt Levente and Coucke, Philippe and Jodogne, S{\'{e}}bastien and Bernard, Claire and Hatt, Mathieu and Visvikis, Dimitris and Jansen, Nicolas and Duysinx, Bernard and Hustinx, Roland and Tustison, Nick J Nicholas Nick Nicholas J and Qing, Kun and Wang, Chengbo and Altes, Talissa A and {Mugler 3rd}, John P and Henderson, R and Keiding, N and Depeursinge, Adrien and Foncubierta-Rodriguez, Antonio and {Van De Ville}, Dimitri and M{\"{u}}ller, Henning and Chicklore, Sugama and Goh, Vicky and Siddique, Musib and Roy, Arunabha and Marsden, Paul K and Cook, Gary J R and Ryu, Jeong-Seon and Hyun, In Young and Berman, Abigail T and Ellenberg, Susan S and {Simone 2nd}, Charles B and Kim, Keunyoung and Kim, Seong-Jang and Kim, In-Joo and Kim, Young Tae Yun Seong and Pak, Kyoungjune and Kim, Heeyoung Hyun J and Horne, Zachary D and Clump, David A and Vargo, John A and Shah, Samir and Beriwal, Sushil and Burton, Steven A and Quinn, Annette E and Schuchert, Matthew J and Landreneau, Rodney J and Christie, Neil A and Luketich, James D and Heron, Dwight E and Lee, Ho Yun Hyun Ju and Lee, Ho Yun Hyun Ju and Kim, Young Tae Yun Seong and Kang, Chang Hyun and Jang, Bo Gun and Chung, Doo Hyun and Goo, Jin Mo and Park, Chang Min and Lee, Chang Hyun and Kang, Keon Wook and Lardinois, Didier and Weder, Walter and Hany, Thomas F and Kamel, Ehab M and Korom, Stephan and Seifert, Burkhardt and von Schulthess, Gustav K and Steinert, Hans C and Hansch, Andreas and Kohlmann, Peter and Hinneburg, Uta and Boettcher, Joachim and Malich, Ansgar and Wolf, Gunter and Laue, Hendrik and Pfeil, Alexander and Ohno, Yoshiharu and Hatabu, Hiroto and Murase, Kenya and Higashino, Takanori and Kawamitsu, Hideaki and Watanabe, Hirokazu and Takenaka, Dais

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