During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9–14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. Materials and Methods: In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients. Results: The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P , .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98). Conclusion: For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.

Despite the importance and versatility of linear mixed effects models (LME), they have seldom been used in whole brain imaging analyses due to the computational requirement. Here, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole brain voxelwise imaging LME analyses possible. We demonstrate the equivalency of statistical power and control of type I errors between FEMA and classical LME, whilst showing an order of magnitude improvement in the speed of FEMA compared to classical LME. By applying FEMA on diffusion images and resting state functional connectivity matrices from the ABCD StudySM release 4.0 data, we show voxelwise annualized changes in fractional anisotropy (FA) and functional connectomes in early adolescence, highlighting a critical time of establishing associations among cortical and subcortical regions.

Development in late childhood has been associated with microstructural changes in white matter (WM) that are hypothesized to underpin concurrent changes in cognitive and behavioral function. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that can probe microstructural changes within hindered and restricted compartments providing greater specificity than diffusion tensor imaging for characterizing intracellular diffusion. Using RSI, we modelled voxelwise restricted isotropic, N0, and anisotropic, ND, diffusion across the brain and measured cross-sectional and longitudinal age associations in a large sample (n=8,039) aged 9-13 years from the Adolescent Brain and Cognitive Development (ABCD) StudySM. Participants showed global increases in N0 and ND across WM with age. When controlling for global RSI measures (averaged across WM), we found smaller age-related associations in frontal regions, reflective of more protracted development of frontal WM. Moreover, variability in the development of restricted diffusion in subcortical regions and along particular gray-white matter boundaries was independent of the global developmental effect. Using the ABCD sample, we have unprecedented statistical power to estimate developmental effects with high precision. Our analyses reveal spatially-varying maturational changes for different regions, independent of global changes. This non-uniformity may reflect age-dependent development of distinct cognitive and behavioral processes.