Image analysis and statistical inference in neuroimaging with R

Reproducibility and Repeatability of Intravoxel Incoherent Motion MRI Acquisition Methods in Liver

BACKGROUND

Intravoxel incoherent motion (IVIM) diffusion weighted MRI (DWI) has potential for evaluating hepatic fibrosis but image acquisition technique influence on diffusion parameter estimation bears investigation.

PURPOSE

To minimize variability and maximize repeatably in abdominal DWI in terms of IVIM parameter estimates.

STUDY TYPE

Prospective test-retest and image quality comparison.

SUBJECTS

Healthy volunteers (3F/7M, 29.9 ± 12.9 years) and Family Study subjects (18F/12M, 51.7 ± 16.7 years), without and with liver steatosis.

FIELD STRENGTH/SEQUENCE

Abdominal single-shot echo-planar imaging (EPI) and simultaneous multi-slice (SMS) DWI sequences with respiratory triggering (RT), breath-holding (BH), and navigator echo (NE) at 3 Tesla.

ASSESSMENT

SMS-BH, EPI-NE, and SMS-RT data from twice-scanned healthy volunteers were analyzed using 6 × b-values (0-800 s⋅mm-2) and lower (LO) and higher (HI) b-value ranges. Family Study subjects were scanned using SMS and standard EPI sequences. The biexponential IVIM model was used to estimate fast-diffusion coefficient (Df), fraction of fast diffusion (f), and slow-diffusion coefficient (Ds). Scan time, estimated signal-to-noise ratio (eSNR), eSNR per acquisition, and distortion ratio were compared.

STATISTICAL TESTS

Coefficients of variation (CoV) and Bland Altman analyses were performed for test-retest repeatability. Interclass correlation coefficient (ICC) assessed interobserver agreement with P < 0.05 deemed significant.

RESULTS

Within-subject CoVs among volunteers (N = 10) for f and Ds were lowest in EPI-NE-LO (11.6%) and SMS-RT-HI (11.1%). Inter-observer ICCs for f and Ds were highest for EPI-NE-LO (0.63) and SMS-RT-LO (0.76). Df could not be estimated for most subjects. Estimated eSNR (EPI = 21.9, SMS = 4.7) and eSNR time (EPI = 6.7, SMS = 16.6) were greater for SMS, with less distortion in the liver region (DR-PE: EPI = 23.6, SMS = 13.1).

DATA CONCLUSION

Simultaneous multislice acquisitions had significantly less variability and higher ICCs of Ds, higher eSNR, less distortion, and reduced scan time compared to EPI.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma

Backgrounds

Uterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.

Methods

Consensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.

Results

All UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.

Conclusion

We successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Jalca Cantos L, Calderón Delgado EP, Yaguache Celi RM, Silva Saltos CA, Zamora A, Mastarreno M, Portalanza D. Injury patterns among road traffic accidents: a hospital-based study in Ecuador

Traffic accidents are serious public health problems, account for profound economic costs to individuals, families, and societies. The social impacts range from physiological to economic causes, which could be a serious negative effect, especially in undeveloped countries. To further elucidate this problem, the prevalence of injuries caused by traffic accidents in a Santa Ana Health Centre, Portoviejo, Ecuador, was studied. This registry-based retrospective study analyzed data on Santa Ana, from Enero 2016 to Diciembre 2019, and the medical records of patients who had been admitted were extracted and analyzed. Passengers cars, motorcycles, and bicycles involved in collisions were included, and the information collected was relating to sex, age, and type of injuries. In total, 75%±6.34 patients victims of road traffic injuries were males, and their mean age was 20 and 49 years. There was a cooperative agreement between total injury occurrence (%) and type of vehicle. Bus and car accidents had lower relation (R2 = 0.44, 078) (p = 0.063, 0.005) with total occurrence. The highest relation was found in motorbikes (R2 = 0.98 p = 2e-05), since it's the primary or most popular means of transportation in the city. The best of our knowledge is the first study to reporting data on road traffic injuries in the Province of Manabí, the third-largest province in extension in Ecuador. Additional studies with larger populations are thus necessary to construct a robust data system in undeveloped countries that can facilitate the flow of reliable information about road traffic injuries.

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Relationship between phenotype and genotype of 102 Chinese newborns with Prader-Willi syndrome

High rates of misdiagnosis and delayed intervention in neonatal PWS are leading to poor prognoses. To determine the clinical and image characteristics of newborns with Prader-Willi syndrome (PWS). A total of 102 cases of newborns definitively diagnosed with PWS at the Children's Hospital of Fudan University from 02/2014 to 12/2017 were retrospectively analyzed. We analyzed the modulated voxel-based morphology (VBM) of gray matter in PWS by T2 weighted imaging. Of 102 cases, 75 (73.5%) have paternal deletion of 15q11.2-q13, whereas 27 (26.5%) have maternal uniparental disomy (UPD). Of the 75 deletion cases, 75 (100%) week crying, 71 (94.7%) hypotonia, 70 (93.3%) poor feeding, 46 (61.3%) hypopigmentation, 43 (57.3%) male cryptorchidism, 10 (13.3%) female labia minora, 48 (64%) characteristic facial features. Of 27 UPD cases, 27 (100%) week crying and hypotonia, 25 (92.6%) hypophagia, 20 (74.1%) male cryptorchidism, 1 (3.7%) female labia minora, 19 (70.4%) characteristic facial features, 12 (44.4%) hypopigmentation. The modulated VBM analysis shows that the middle frontal gyrus, orbitofrontal cortex (middle), and inferior frontal gyrus are the most variable brain regions that determine the endo-phenotype difference between the two genotypes. Hypotonia, hypophagia, and maldevelopment of sexual organs are general characteristics of newborns with PWS in Chinese population. In UPD cases, the proportions of premature newborns, elderly parturient women and congenital malformations were higher than for paternal deletion cases. The differences in the gray matter volume of these three regions between the two genotypes may explain the differences in maladaptive behaviors and emotions.

Dietary Lactobacillus plantarum ST-III alleviates the toxic effects of triclosan on zebrafish (Danio rerio) via gut microbiota modulation

The probiotics, Lactobacillus plantarum ST-III, plays an important role in modulating microbiota and alleviating intestinal metabolic disorders. Herein, we reported that Lactobacillus increases biodiversity of zebrafish gut flora, and attenuates toxic effects from chronic triclosan (TCS) exposure. Lactobacillus-feeding recovered the species and amount of microorganisms in the intestines of zebrafish, and inhibited toxin production by saprophytic bacterial growth. Abnormal physiological indexes and malonaldeyhde content resulting from TCS exposure were effectively alleviated. Additionally, lipid-metabolism disorders, such as increased triglyceride and total cholesterol levels, were attenuated by a probiotics diet. The number of CD4⁺ T cell lymphocytes in the lamina propria of the duodenal mucosa was decreased in zebrafish receiving a Lactobacillus diet compared to the TCS-exposed group, showing a consistent expression trend for six immune genes (NF-κB, IL-1β, TNF-α, lysozyme, TLR4α, IL-10) in the intestinal mucosa. Histopathological observations of intestines, spleen and kidney showed that TCS exposure produced severe damage to the morphology and structure of immune and metabolism-related organs. Lactobacillus was capable of mitigating this damage, but bile salt hydrolase, an active extract of Lactobacillus, was not an effective mitigation strategy. The Lactobacillus-induced decrease in the number of inflammatory cells confirmed its role in preventing inflammatory injury. Three behavioral tests (T-maze, bottom dwelling and social interaction) indicated that a probiotics diet improved zebrafish movement and learning/memory capacity, effectively alleviating anxiety behavior due to TCS exposure. These findings inform development of beneficial strategies to alleviate intestinal metabolic syndromes and neurodegenerative diseases resulting from exposure to environmental contaminants through modifying gut flora with a probiotics diet.

Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study

BACKGROUND

Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques.

METHODS

Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness.

RESULTS

Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6.

CONCLUSION

The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.

Integrity of normal-appearing white matter: Influence of age, visible lesion burden and hypertension in patients with small-vessel disease

White matter hyperintensities accumulate with age and occur in patients with stroke, but their pathogenesis is poorly understood. We measured multiple magnetic resonance imaging biomarkers of tissue integrity in normal-appearing white matter and white matter hyperintensities in patients with mild stroke, to improve understanding of white matter hyperintensities origins. We classified white matter into white matter hyperintensities and normal-appearing white matter and measured fractional anisotropy, mean diffusivity, water content (T1-relaxation time) and blood-brain barrier leakage (signal enhancement slope from dynamic contrast-enhanced magnetic resonance imaging). We studied the effects of age, white matter hyperintensities burden (Fazekas score) and vascular risk factors on each biomarker, in normal-appearing white matter and white matter hyperintensities, and performed receiver-operator characteristic curve analysis. Amongst 204 patients (34.3-90.9 years), all biomarkers differed between normal-appearing white matter and white matter hyperintensities ( P < 0.001). In normal-appearing white matter and white matter hyperintensities, mean diffusivity and T1 increased with age ( P < 0.001), all biomarkers varied with white matter hyperintensities burden ( P < 0.001; P = 0.02 signal enhancement slope), but only signal enhancement slope increased with hypertension ( P = 0.028). Fractional anisotropy showed complex age-white matter hyperintensities-tissue interactions; enhancement slope showed white matter hyperintensities-tissue interactions. Mean diffusivity distinguished white matter hyperintensities from normal-appearing white matter best at all ages. Blood-brain barrier leakage increases with hypertension and white matter hyperintensities burden at all ages in normal-appearing white matter and white matter hyperintensities, whereas water mobility and content increase as tissue damage accrues, suggesting that blood-brain barrier leakage mediates small vessel disease-related brain damage.

White matter hyperintensities and normal-appearing white matter integrity in the aging brain

White matter hyperintensities (WMH) of presumed vascular origin are a common finding in brain magnetic resonance imaging of older individuals and contribute to cognitive and functional decline. It is unknown how WMH form, although white matter degeneration is characterized pathologically by demyelination, axonal loss, and rarefaction, often attributed to ischemia. Changes within normal-appearing white matter (NAWM) in subjects with WMH have also been reported but have not yet been fully characterized. Here, we describe the in vivo imaging signatures of both NAWM and WMH in a large group of community-dwelling older people of similar age using biomarkers derived from magnetic resonance imaging that collectively reflect white matter integrity, myelination, and brain water content. Fractional anisotropy (FA) and magnetization transfer ratio (MTR) were significantly lower, whereas mean diffusivity (MD) and longitudinal relaxation time (T1) were significantly higher, in WMH than NAWM (p < 0.0001), with MD providing the largest difference between NAWM and WMH. Receiver operating characteristic analysis on each biomarker showed that MD differentiated best between NAWM and WMH, identifying 94.6% of the lesions using a threshold of 0.747 × 10⁻⁹ m²s⁻¹ (area under curve, 0.982; 95% CI, 0.975–0.989). Furthermore, the level of deterioration of NAWM was strongly associated with the severity of WMH, with MD and T1 increasing and FA and MTR decreasing in NAWM with increasing WMH score, a relationship that was sustained regardless of distance from the WMH. These multimodal imaging data indicate that WMH have reduced structural integrity compared with surrounding NAWM, and MD provides the best discriminator between the 2 tissue classes even within the mild range of WMH severity, whereas FA, MTR, and T1 only start reflecting significant changes in tissue microstructure as WMH become more severe.

The myImageAnalysis project: a web-based application for high-content screening

A major challenge faced by screening centers developing image-based assays is the wide range of assays needed compared to the limited resources that are available to effectively analyze and manage them. To overcome this limitation, we have developed the web-based myImageAnalysis (mIA) application, integrated with an open database connectivity compliant database and powered by Pipeline Pilot (PLP) that incorporates dataset tracking, scheduling and archiving, image analysis, and data reporting. For system administrators, mIA provides automated methods for managing and archiving data. For the biologist, this application allows those without any programming or image analysis experience to quickly develop, validate, and share results of complex image-based assays. Further, the structure of the application within PLP allows those with experience in PLP programming to easily add additional analysis tools as required. The tools within mIA allow users to assess basic (cell count, protein per cell, protein subcellular localization) and more advanced (engineered cell lines analysis, cell toxicity) biological image-based assays that employ advanced statistics and provides key assay performance metrics.

Neural activation abnormalities during self-referential processing in schizophrenia: an fMRI study

Impairments in self-awareness contribute to disability in schizophrenia. Studies have revealed activation abnormalities in schizophrenia in cortical midline structures associated with self-reference. We used functional magnetic resonance imaging to compare activation throughout the brain in people with schizophrenia and healthy controls (Kelly et al., 2002) while they indicated whether trait adjectives described attributes of themselves, their mother or a former president of the United States. Blood oxygenation level dependent signal in each condition was compared to resting fixation. Patients were less likely and slower to endorse positive self-attributes, and more likely and quicker to endorse negative self-attributes than controls. Activation abnormalities reported previously in cortical midline structures were again noted. In addition, patients showed greater signal increases in frontal, temporal gyri and insula, and smaller signal decreases in posterior regions than healthy controls when thinking about themselves. Group differences were less evident when subjects were thinking about their mothers and tended to go in the opposite direction when thinking about a president. Many of the areas showing abnormality have been shown in other studies to differ between patients and controls in structure and with other activation paradigms. We suggest that general neuropathology in schizophrenia alters the neural system configurations associated with self-representation.

Potential of hybrid ¹⁸F-fluorocholine PET/MRI for prostate cancer imaging

PURPOSE

To report the first results of hybrid (18)F-fluorocholine PET/MRI imaging for the detection of prostate cancer.

METHODS

This analysis included 26 consecutive patients scheduled for prostate PET/MRI before radical prostatectomy. The examinations were performed on a hybrid whole-body PET/MRI scanner. The MR acquisitions which included T2-weighted, diffusion-weighted and dynamic contrast-enhanced sequences were followed during the same session by whole-body PET scans. Parametric maps were constructed to measure normalized T2-weighted intensity (nT2), apparent diffusion coefficient (ADC), volume transfer constant (K (trans)), extravascular extracellular volume fraction (v e) and standardized uptake values (SUV). With pathology as the gold standard, ROC curves were calculated using logistic regression for each parameter and for the best combination with and without PET to obtain a MR model versus a PETMR model.

RESULTS

Of the 26 patients initially selected, 3 were excluded due to absence of an endorectal coil (2 patients) or prosthesis artefacts (1 patient). In the whole prostate, the area under the curve (AUC) for SUVmax, ADC, nT2, K (trans) and v e were 0.762, 0.756, 0.685, 0.611 and 0.529 with a best threshold at 3.044 for SUVmax and 1.075 × 10(-3) mm(2)/s for ADC. The anatomical distinction between the transition zone and the peripheral zone showed the potential of the adjunctive use of PET. In the peripheral zone, the AUC of 0.893 for the PETMR model was significantly greater (p = 0.0402) than the AUC of 0.84 for the MR model only. In the whole prostate, no relevant correlation was observed between ADC and SUVmax. The SUVmax was not affected by the Gleason score.

CONCLUSION

The performance of a hybrid whole-body (18)F-fluorocholine PET/MRI scan in the same session combined with a prostatic MR examination did not interfere with the diagnostic accuracy of the MR sequences. The registration of the PET data and the T2 anatomical MR sequence data allowed precise localization of hypermetabolic foci in the prostate. While in the transition zone the adenomatous hyperplasia interfered with cancer detection by PET, the quantitative analysis tool performed well for cancer detection in the peripheral zone.

Analytic programming with FMRI data: a quick-start guide for statisticians using R

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial

Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p < 0.05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.

Principal networks

Graph representations of brain connectivity have attracted a lot of recent interest, but existing methods for dividing such graphs into connected subnetworks have a number of limitations in the context of neuroimaging. This is an important problem because most cognitive functions would be expected to involve some but not all brain regions. In this paper we outline a simple approach for decomposing graphs, which may be based on any measure of interregional association, into coherent “principal networks”. The technique is based on an eigendecomposition of the association matrix, and is closely related to principal components analysis. We demonstrate the technique using cortical thickness and diffusion tractography data, showing that the subnetworks which emerge are stable, meaningful and reproducible. Graph-theoretic measures of network cost and efficiency may be calculated separately for each principal network. Unlike some other approaches, all available connectivity information is taken into account, and vertices may appear in none or several of the subnetworks. Subject-by-subject “scores” for each principal network may also be obtained, under certain circumstances, and related to demographic or cognitive variables of interest.

Fully exploratory network independent component analysis of the 1000 functional connectomes database

The 1000 Functional Connectomes Project is a collection of resting-state fMRI datasets from more than 1000 subjects acquired in more than 30 independent studies from around the globe. This large, heterogeneous sample of resting-state data offers the unique opportunity to study the consistencies of resting-state networks at both subject and study level. In extension to the seminal paper by Biswal et al. (2010), where a repeated temporal concatenation group independent component analysis (ICA) approach on reduced subsets (using 20 as a pre-specified number of components) was used due to computational resource limitations, we herein apply Fully Exploratory Network ICA (FENICA) to 1000 single-subject independent component analyses. This, along with the possibility of using datasets of different lengths without truncation, enabled us to benefit from the full dataset available, thereby obtaining 16 networks consistent over the whole group of 1000 subjects. Furthermore, we demonstrated that the most consistent among these networks at both subject and study level matched networks most often reported in the literature, and found additional components emerging in prefrontal and parietal areas. Finally, we identified the influence of scan duration on the number of components as a source of heterogeneity between studies.

The Center for Computational Biology: resources, achievements, and challenges

The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains.

Along-tract statistics allow for enhanced tractography analysis

Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts, but most tractography studies use a "tract-averaged" approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.