Philip A. Cook

A reproducible evaluation of ANTs similarity metric performance in brain image registration.

The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLAs LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.

N4ITK: Improved N3 Bias Correction

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 3He lung image data, and 9.4T postmortem hippocampus data.

Evidence for Segregated and Integrative Connectivity Patterns in the Human Basal Ganglia

Detailed knowledge of the anatomy and connectivity pattern of cortico-basal ganglia circuits is essential to an understanding of abnormal cortical function and pathophysiology associated with a wide range of neurological and neuropsychiatric diseases. We aim to study the spatial extent and topography of human basal ganglia connectivity in vivo. Additionally, we explore at an anatomical level the hypothesis of coexistent segregated and integrative cortico-basal ganglia loops. We use probabilistic tractography on magnetic resonance diffusion weighted imaging data to segment basal ganglia and thalamus in 30 healthy subjects based on their cortical and subcortical projections. We introduce a novel method to define voxel-based connectivity profiles that allow representation of projections from a source to more than one target region. Using this method, we localize specific relay nuclei within predefined functional circuits. We find strong correlation between tractography-based basal ganglia parcellation and anatomical data from previously reported invasive tracing studies in nonhuman primates. Additionally, we show in vivo the anatomical basis of segregated loops and the extent of their overlap in prefrontal, premotor, and motor networks. Our findings in healthy humans support the notion that probabilistic diffusion tractography can be used to parcellate subcortical gray matter structures on the basis of their connectivity patterns. The coexistence of clearly segregated and also overlapping connections from cortical sites to basal ganglia subregions is a neuroanatomical correlate of both parallel and integrative networks within them. We believe that this method can be used to examine pathophysiological concepts in a number of basal ganglia-related disorders.

An open source multivariate framework for n-tissue segmentation with evaluation on public data.

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs (http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.

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.

Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis

We use a new, unsupervised multivariate imaging and analysis strategy to identify related patterns of reduced white matter integrity, measured with the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI), and decreases in cortical thickness, measured by high resolution T1-weighted imaging, in Alzheimers disease (AD) and frontotemporal dementia (FTD). This process is based on a novel computational model derived from sparse canonical correlation analysis (SCCA) that allows us to automatically identify mutually predictive, distributed neuroanatomical regions from different imaging modalities. We apply the SCCA model to a dataset that includes 23 control subjects that are demographically matched to 49 subjects with autopsy or CSF-biomarker-diagnosed AD (n=24) and FTD (n=25) with both DTI and T1-weighted structural imaging. SCCA shows that the FTD-related frontal and temporal degeneration pattern is correlated across modalities with permutation corrected p<0.0005. In AD, we find significant association between cortical thinning and reduction in white matter integrity within a distributed parietal and temporal network (p<0.0005). Furthermore, we show that-within SCCA identified regions-significant differences exist between FTD and AD cortical-connective degeneration patterns. We validate these distinct, multimodal imaging patterns by showing unique relationships with cognitive measures in AD and FTD. We conclude that SCCA is a potentially valuable approach in image analysis that can be applied productively to distinguishing between neurodegenerative conditions.

Optimal acquisition orders of diffusion‐weighted MRI measurements

To propose a new method to optimize the ordering of gradient directions in diffusion‐weighted MRI so that partial scans have the best spherical coverage.

Cognitive decline and reduced survival in C9orf72 expansion frontotemporal degeneration and amyotrophic lateral sclerosis

Background Significant heterogeneity in clinical features of frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS) cases with the pathogenic C9orf72 expansion (C9P) have been described. To clarify this issue, we compared a large C9P cohort with carefully matched non-expansion (C9N) cases with a known or highly-suspected underlying TAR DNA-binding protein 43 (TDP-43) proteinopathy. Methods A retrospective case-control study was carried out using available cross-sectional and longitudinal clinical and neuropsychological data, MRI voxel-based morphometry (VBM) and neuropathological assessment from 64 C9P cases (ALS=31, FTLD=33) and 79 C9N cases (ALS=36, FTLD=43). Results C9P cases had an earlier age of onset (p=0.047) and, in the subset of patients who were deceased, an earlier age of death (p=0.014) than C9N. C9P had more rapid progression than C9N: C9P ALS cases had a shortened survival (2.6±0.3 years) compared to C9N ALS (3.8±0.4 years; log-rank λ2=4.183, p=0.041), and C9P FTLD showed a significantly greater annualised rate of decline in letter fluency (4.5±1.3 words/year) than C9N FTLD (1.4±0.8 words/year, p=0.023). VBM revealed greater atrophy in the right frontoinsular, thalamus, cerebellum and bilateral parietal regions for C9P FTLD relative to C9N FTLD, and regression analysis related verbal fluency scores to atrophy in frontal and parietal regions. Neuropathological analysis found greater neuronal loss in the mid-frontal cortex in C9P FTLD, and mid-frontal cortex TDP-43 inclusion severity correlated with poor letter fluency performance. Conclusions C9P cases may have a shorter survival in ALS and more rapid rate of cognitive decline related to frontal and parietal disease in FTLD. C9orf72 genotyping may provide useful prognostic and diagnostic clinical information for patients with ALS and FTLD.

Heteromodal conceptual processing in the angular gyrus.

Concepts bind together the features commonly associated with objects and events to form networks in long-term semantic memory. These conceptual networks are the basis of human knowledge and underlie perception, imagination, and the ability to communicate about experiences and the contents of the environment. Although it is often assumed that this distributed semantic information is integrated in higher-level heteromodal association cortices, open questions remain about the role and anatomic basis of heteromodal representations in semantic memory. Here we used combined neuroimaging evidence from functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to characterize the cortical networks underlying concept representation. Using a lexical decision task, we examined the processing of concepts in four semantic categories that varied on their sensory-motor feature associations (sight, sound, manipulation, and abstract). We found that the angular gyrus was activated across all categories regardless of their modality-specific feature associations, consistent with a heteromodal account for the angular gyrus. Exploratory analyses suggested that categories with weighted sensory-motor features additionally recruited modality-specific association cortices. Furthermore, DTI tractography identified white matter tracts connecting these regions of modality-specific functional activation with the angular gyrus. These findings are consistent with a distributed semantic network that includes a heteromodal, integrative component in the angular gyrus in combination with sensory-motor feature representations in modality-specific association cortices.

White matter imaging helps dissociate tau from TDP-43 in frontotemporal lobar degeneration

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 appropriate 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-one-out 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. FTLD-TAU 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 FTLD-TAU, and emphasise the role of WM neuroimaging for in vivo discrimination between FTLD-TAU and FTLD-TDP.