General additive models address statistical issues in diffusion MRI: An example with clinically anxious adolescents

Tractometry of Human Visual White Matter Pathways in Health and Disease

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Subtle microstructural alterations in white matter tracts involved in socio-emotional processing after very preterm birth

Children born very preterm (VPT, < 32 weeks of gestation) have an increased risk of developing socio-emotional difficulties. Possible neural substrates for these socio-emotional difficulties are alterations in the structural connectivity of the social brain due to premature birth. The objective of the current study was to study microstructural white matter integrity in VPT versus full-term (FT) born school-aged children along twelve white matter tracts involved in socio-emotional processing. Diffusion MRI scans were obtained from a sample of 35 VPT and 38 FT 8-to-12-year-old children. Tractography was performed using TractSeg, a state-of-the-art neural network-based approach, which offers investigation of detailed tract profiles of fractional anisotropy (FA). Group differences in FA along the tracts were investigated using both a traditional and complementary functional data analysis approach. Exploratory correlations were performed between the Social Responsiveness Scale (SRS-2), a parent-report questionnaire assessing difficulties in social functioning, and FA along the tract. Both analyses showed significant reductions in FA for the VPT group along the middle portion of the right SLF I and an anterior portion of the left SLF II. These group differences possibly indicate altered white matter maturation due to premature birth and may contribute to altered functional connectivity in the Theory of Mind network which has been documented in earlier work with VPT samples. Apart from reduced social motivation in the VPT group, there were no significant group differences in reported social functioning, as assessed by SRS-2. We found that in the VPT group higher FA values in segments of the left SLF I and right SLF II were associated with better social functioning. Surprisingly, the opposite was found for segments in the right IFO, where higher FA values were associated with worse reported social functioning. Since no significant correlations were found for the FT group, this relationship may be specific for VPT children. The current study overcomes methodological limitations of previous studies by more accurately segmenting white matter tracts using constrained spherical deconvolution based tractography, by applying complementary tractometry analysis approaches to estimate changes in FA more accurately, and by investigating the FA profile along the three components of the SLF.

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Diagnostic reference level quantities for adult chest and abdomen-pelvis CT examinations: correlation with organ doses

OBJECTIVES

To evaluate correlations between DRL quantities (DRLq) stratified into patient size groups for non-contrast chest and abdomen-pelvis CT examinations in adult patients and the corresponding organ doses.

METHODS

This study presents correlations between DRLq  (CTDI_vol, DLP and SSDE) stratified into patient size ranges and corresponding organ doses shared in four groups: inside, peripheral, distributed and outside. The demographic, technical and dosimetric parameters were used to identify the influence of these quantities in organ doses. A robust statistical method was implemented in order to establish these correlations and its statistical significance.

RESULTS

Median values of the grouped organ doses are presented according to the effective diameter ranges. Organ doses in the regions inside the imaged area are higher than the organ doses in peripheral, distributed and outside regions, excepted to the peripheral doses associated with chest examinations. Different levels of statistical significance between organ doses and the DRLq were presented.

CONCLUSIONS

Correlations between DRLq and target-organ doses associated with clinical practice can support guidance's to the establishment of optimization criteria. SSDE demonstrated to be significant in the evaluation of organ doses is also highlighted. The proposed model allows the design of optimization actions with specific risk-reduction results.

Sleep fails to depotentiate amygdala-reactivity to negative emotional stimuli in youth with elevated symptoms of anxiety

Sleep-related problems often precede escalating anxiety in early adolescence. Pushing beyond broad sleep-mental health associations and toward mechanistic theories of their interplay can inform etiological models of psychopathology. Recent studies suggest that sleep depotentiates neural (e.g., amygdala) reactivity during reexposure to negative emotional stimuli in adults. Persistent amygdala reactivity to negative experiences and poor sleep characterize anxiety, particularly at the transition to adolescence. We propose that sleep depotentiates amygdala reactivity in youth but fails to do so among youth with anxiety. Participants (n = 34; 18 males; age, mean [M] = 11.35, standard deviation [SD] = 2.00) recruited from the community and specialty anxiety clinics viewed valenced images (positive, negative, and neutral) across two fMRI sessions (Study, Test), separated by a 10-12-hour retention period of sleep or wake (randomized). Mixed linear models regressed basolateral amygdala (BLA) activation and BLA-medial prefrontal cortex (mPFC) functional connectivity to negative images on Time, Condition, and Anxiety Severity. There were greater reductions in BLA activations to negative target images from Study to Test in the Sleep Condition, which was blunted with higher anxiety (b = -0.065, z = -2.355, p = 0.019). No such sleep- or anxiety-related effects were observed for BLA-mPFC functional connectivity (ps > 0.05). Sleep supports depotentiation of amygdala reactivity to negative stimuli in youth, but this effect is blunted at higher levels of anxiety. Disruptions in sleep-related affective habituation may be a critical, modifiable driver of anxiety.

Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury

In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R² of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R² = 0.26 and R² = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R² = 0.49 and R² = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.