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.

No abstract

The C allele at the rs11136000 locus in the clusterin (CLU) gene is the third strongest known genetic risk factor for late-onset Alzheimer's disease (LOAD). A recent genome-wide association study of LOAD found the strongest evidence of association with CLU at rs1532278, in high linkage disequilibrium with rs11136000. Brain structure and function are related to the CLU risk alleles, not just in LOAD patients but also in healthy young adults. We tracked the volume of the lateral ventricles across baseline, 1-year, and 2-year follow-up scans in a large sample of elderly human participants (N ϭ 736 at baseline), from the Alzheimer's Disease Neuroimaging Initiative, to determine whether these CLU risk variants predicted longitudinal ventricular expansion. The rs11136000 major C allele-previously linked with reduced CLU expression and with increased risk for dementia-predicted faster expansion, independently of dementia status or ApoE genotype. Further analyses revealed that the CLU and ApoE risk variants had combined effects on both volumetric expansion and lateral ventricle surface morphology. The rs1532278 locus strongly resembles a regulatory element. Its association with ventricular expansion was slightly stronger than that of rs11136000 in our analyses, suggesting that it may be closer to a functional variant. Clusterin affects inflammation, immune responses, and amyloid clearance, which in turn may result in neurodegeneration. Pharmaceutical agents such as valproate, which counteract the effects of genetically determined reduced clusterin expression, may help to achieve neuroprotection and contribute to the prevention of dementia, especially in carriers of these CLU risk variants.

Little is known about the effects of prenatal methamphetamine exposure on white matter microstructure, and the impact of concomitant alcohol exposure. Diffusion tensor imaging and neurocognitive testing were performed on 21 children with prenatal methamphetamine exposure (age 9.8±1.8 years; 17 also exposed to alcohol), 19 children with prenatal alcohol but not methamphetamine exposure (age 10.8±2.3 years), and 27 typically-developing children (age 10.3±3.3 years). Whole-brain maps of fractional anisotropy (FA) were evaluated using tract-based spatial statistics. Relative to unexposed controls, children with prenatal methamphetamine exposure demonstrated higher FA mainly in left-sided regions, including the left anterior corona radiata (LCR) and corticospinal tract (P<0.05, corrected). Post-hoc analyses of these FA differences showed they likely result more from lower radial diffusivity (RD) than higher axial diffusivity (AD). Relative to the methamphetamine-exposed group, children with prenatal alcohol exposure showed lower FA in frontotemporal regions – particularly the right external capsule (P<0.05, corrected). We failed to find any group-performance interaction (on tests of executive functioning and visuomotor integration) in predicting FA; however, FA in the right external capsule was significantly associated with performance on a test of visuomotor integration across groups (P<0.05). This report demonstrates unique diffusion abnormalities in children with prenatal methamphetamine/polydrug exposure that are distinct from those associated with alcohol exposure alone, and illustrates that these abnormalities in brain microstructure are persistent into childhood and adolescence – long after the polydrug exposure in utero.

a b s t r a c tBackground and purpose: Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid in the diagnosis and treatment of brain-based disorders such as temporal lobe epilepsy (TLE). Using a large cohort of epilepsy patients with and without mesial temporal sclerosis (MTS), we sought to automatically classify MTS using measures of cortical morphology, and to further relate classification probabilities to measures of disease burden. Materials and methods: Our sample consisted of high-resolution T1 structural scans of 169 adults with epilepsy collected across five different 1.5 T and four different 3 T scanners at UCLA. We applied a multiple support vector machine recursive feature elimination algorithm to morphological measures generated from FreeSurfer's automated segmentation and parcellation in order to classify Epilepsy patients with MTS (n = 85) from those without MTS (N = 84). Results: In addition to hippocampal volume, we found that alterations in cortical thickness, surface area, volume and curvature in inferior frontal and anterior and inferior temporal regions contributed to a classification accuracy of up to 81% (p = 1.3 × 10 −17 ) in identifying MTS. We also found that MTS classification probabilities were associated with a longer duration of disease for epilepsy patients both with and without MTS. Conclusions: In addition to implicating extra-hippocampal involvement of MTS, these findings shed further light on the pathogenesis of TLE and may ultimately assist in the development of automated tools that incorporate multiple neuroimaging measures to assist clinicians in detecting more subtle cases of TLE and MTS.