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.

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.

A fundamental tenet in the field of developmental neuroscience is that brain maturation generally proceeds from posterior/inferior to anterior/superior. This pattern is thought to underlie the similar timing of cognitive development in related domains, with the dorsal frontal cortices – important for decision making and cognitive control – the last to fully mature. While this caudal to rostral wave of structural development was first qualitatively described for white matter in classical postmortem studies, and has been discussed frequently in the developmental neuroimaging literature and in the popular press, it has never been formally demonstrated continuously and quantitatively across the whole brain with magnetic resonance imaging (MRI). Here we use diffusion imaging to map developmental changes in the white matter in 32 typically-developing individuals age 5-28 years. We then employ a novel meta-statistic that is sensitive to the timing of this developmental trajectory, and use this integrated strategy to both confirm these long-postulated broad regional gradients in the timing of white matter maturation in vivo, and demonstrate a surprisingly smooth transition in the timing of white matter maturational peaks along a caudal-rostral arc in this cross-sectional sample. These results provide further support for the notion of continued plasticity in these regions well into adulthood, and may provide a new approach for the investigation of neurodevelopmental disorders that could alter the timing of this typical developmental sequence.

Highlights DTI was collected for 34 male adolescents, ages 15–17. Aerobic fitness related to white matter connectivity in frontal and motor tracts. HF had higher tractography streamline counts in CST and Fminor compared to LF. A negative relationship was seen between VO 2 peak and FA in the L CST. Exercise is an important environmental factor to consider during neurodevelopment.