Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the ...use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license:
github.com/InsightSoftwareConsortium/SimpleITK-Notebooks
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Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making ...the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
Summary Background TRACK-HD is a multinational prospective observational study of Huntington's disease (HD) that examines clinical and biological findings of disease progression in individuals with ...premanifest HD (preHD) and early-stage HD. We aimed to describe phenotypic changes in these participants over 36 months and identify baseline predictors of progression. Methods Individuals without HD but carrying the mutant huntingtin gene (classed as preHD-A if ≥10·8 years and preHD-B if <10·8 years from predicted onset), participants with early HD (classed as HD1 if they had a total functional capacity score of 11–13 and HD2 if they had a score of 7–10), and healthy control individuals were assessed at four study sites in the Netherlands, the UK, France, and Canada. We measured 36-month change for 3T MRI, clinical, cognitive, quantitative motor, and neuropsychiatric assessments and examined their prognostic value. We also assessed the relation between disease progression and the combined effect of CAG repeat length and age. All participants were analysed according to their baseline subgroups. Longitudinal results were analysed using a combination of repeated-measure weighted least squares models and, when examining risk of new diagnosis, survival analysis. Findings At baseline, 366 participants were enrolled between Jan 17, and Aug 26, 2008, and of these 298 completed 36-month follow-up: 97 controls, 58 participants with preHD-A, 46 with preHD-B, 66 with HD1, and 31 with HD2. In the preHD-B group, several quantitative motor and cognitive tasks showed significantly increased rates of decline at 36 months, compared with controls, whereas few had at 24 months. Of the cognitive measures, the symbol digit modality test was especially sensitive (adjusted mean loss 4·11 points 95% CI 1·49–6·73 greater than controls; p=0·003). Among psychiatric indicators, apathy ratings specifically showed significant increases (0·34 points 95% CI 0·02–0·66 greater than controls; p=0·038). There was little evidence of reliable change in non-imaging measures in the preHD-A group, with the exception of the speeded tapping inter-tap interval (0·01 s 95% CI 0·01–0·02 longer than controls; p=0·0001). Several baseline imaging, quantitative motor, and cognitive measures had prognostic value, independent of age and CAG repeat length, for predicting subsequent clinical diagnosis in preHD. Of these, grey-matter volume and inter-tap interval were particularly sensitive (p=0·013 and 0·002, respectively). Longitudinal change in these two measures was also greater in participants with preHD who received a diagnosis of HD during the study compared with those who did not, after controlling for CAG repeat length and age-related risk (p=0·006 and 0·0003, respectively). In early HD, imaging, quantitative motor, and cognitive measures were predictive of decline in total functional capacity and tracked longitudinal change; also, neuropsychiatric changes consistent with frontostriatal pathological abnormalities were associated with this loss of functional capacity (problem behaviours assessment composite behaviour score p<0·0001). Age and CAG repeat length explained variance in longitudinal change of multimodal measures, with the effect more prominent in preHD. Interpretation We have shown changes in several outcome measures in individuals with preHD over 36 months. These findings further our understanding of HD progression and have implications for clinical trial design. Funding CHDI Foundation.
Summary Background Although the association between cytosine-adenine-guanine (CAG) repeat length and age at onset of Huntington's disease is well known, improved prediction of onset would be ...advantageous for clinical trial design and prognostic counselling. We compared various measures for tracking progression and predicting conversion to manifest Huntington's disease. Methods In this prospective observational study, we assessed the ability of 40 measures in five domains (motor, cognitive, psychiatric, functional, and imaging) to predict time to motor diagnosis of Huntington's disease, accounting for CAG repeat length, age, and the interaction of CAG repeat length and age. Eligible participants were individuals from the PREDICT-HD study (from 33 centres in six countries USA, Canada, Germany, Australia, Spain, UK) with the gene mutation for Huntington's disease but without a motor diagnosis (a rating below 4 on the diagnostic confidence level from the 15-item motor assessment of the Unified Huntington's Disease Rating Scale). Participants were followed up between September, 2002, and July, 2014. We used joint modelling of longitudinal and survival data to examine the extent to which baseline and change of measures analysed separately was predictive of CAG-adjusted age at motor diagnosis. Findings 1078 individuals with a CAG expansion were included in this analysis. Participants were followed up for a mean of 5·1 years (SD 3·3, range 0·0–12·0). 225 (21%) of these participants received a motor diagnosis of Huntington's disease during the study. 37 of 40 cross-sectional and longitudinal clinical and imaging measures were significant predictors of motor diagnosis beyond CAG repeat length and age. The strongest predictors were in the motor, imaging, and cognitive domains: an increase of one SD in total motor score (motor domain) increased the risk of a motor diagnosis by 3·07 times (95% CI 2·26–4·16), a reduction of one SD in putamen volume (imaging domain) increased risk by 3·32 times (2·37–4·65), and a reduction of one SD in Stroop word score (cognitive domain) increased risk by 2·32 times (1·88–2·87). Interpretation Prediction of diagnosis of Huntington's disease can be improved beyond that obtained by CAG repeat length and age alone. Such knowledge about potential predictors of manifest Huntington's disease should inform discussions about guidelines for diagnosis, prognosis, and counselling, and might be useful in guiding the selection of participants and outcome measures for clinical trials. Funding US National Institutes of Health, US National Institute of Neurological Disorders and Stroke, and CHDI Foundation.
The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research ...communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.
Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for ...neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org
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In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after ...study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.
A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with ...trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.
•A fully automated method to detect an arbitrary number of landmarks in human brain.•The method combines statistical shape models with brain morphometric measures.•Landmark detection is extended by applying a linear model estimation.•Once a model built, the method is reliably applicable to a variety of imaging data.•This method is reliable for general application in large-scale multi-site studies.
Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large ...resting‐state fMRI (rs‐fMRI) sample from the PREDICT‐HD study (N = 183 Huntington disease gene mutation carriers HDgmc and N = 78 healthy control HC participants) to examine whole‐brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs‐fMRI data to obtain whole‐brain resting state networks. FNC was defined as the correlation between RSN time‐courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k‐means clustering algorithm. HDgmc individuals spent significantly more time in State‐1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State‐4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State‐1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.
In the U.S., more than 80% of African-American smokers use mentholated cigarettes, compared to less than 30% of Caucasian smokers. The reasons for these differences are not well understood. To ...determine if genetic variation contributes to mentholated cigarette smoking, we performed an exome-wide association analysis in a multiethnic population-based sample from Dallas, TX (N = 561). Findings were replicated in an independent cohort of African Americans from Washington, DC (N = 741). We identified a haplotype of MRGPRX4 (composed of rs7102322G, encoding N245S, and rs61733596G, T43T), that was associated with a 5-to-8 fold increase in the odds of menthol cigarette smoking. The variants are present solely in persons of African ancestry. Functional studies indicated that the variant G protein-coupled receptor encoded by MRGPRX4 displays reduced agonism in both arrestin-based and G protein-based assays, and alteration of agonism by menthol. These data indicate that genetic variation in MRGPRX4 contributes to inter-individual and inter-ethnic differences in the preference for mentholated cigarettes, and that the existence of genetic factors predisposing vulnerable populations to mentholated cigarette smoking can inform tobacco control and public health policies.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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