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Additional file 1. Table S1. Quality control metrics for the enrolled subjects. Table S2. Lists number of ASD subjects with available clinical measures. Table S3. List of brain regions in the Neuromorphometrics atlas used as features in HYDRA. Table S4. Detailed statistical analysis of age between HC and ASD in each site and full cohort. Table S5. Clinical profiles between subgroups across the 7 sites. Fig. S1. The image quality measures describe the properties of the image. Fig. S2. Examples of different quality ratings for the original T1 images (left) and segmented images (right). Fig. S3. Schematic illustration of the HYDRA method. Controls (denoted by blue squares) are separated from the patients (denoted by red triangles) using a convex polytope decision boundary. Solid lines correspond to the classifier, dashed lines indicate the margin while highlighted linear segments define the separating convex polytope. Fig. S4. Cross-validated stability of ASD subtypes: Adjusted Rand Index (ARI) vs. number of subtypes (K) indicating highest reproducibility when K = 2. Fig. S5. Volume difference in gray matter (A) and white matter (B) volume between healthy control (HC) (n = 257) and ASD (n = 221) by standard case-control comparison. Effect size (Cohen���s d) maps were generated from regional volumetric maps masked by the set of regions that showed statistically significant differences (PFDR < 0.05) in the MIDAS analysis. Fig. S6. GM volumetric differences between each subtype and HC for K = 2 in Split 1 (left column) and Split 2 (right column). Fig. S7. WM volumetric differences between each subtype and HC for K = 2 in Split 1 (left column) and Split 2 (right column). Fig. S8. The number of overlaps assigned to the same ASD subtype in the leave-one-site-out strategy. Fig. S9. Temporal SD of dynamic R-fMRI measures differences between ASD 1 and ASD 2 using the two-sample t-tests for (a) DC, (b) GSCorr and (c) ReHo (GRF, voxel-level p < 0.001, cluster-level p < 0.01, two-tailed). Fig. S10. (a) Two sample t-test of the mean of concordance among R-fMRI indices between ASD 1 and ASD 2; (b) Two sample t-test of the SD of concordance among R-fMRI indices between ASD 1 and ASD 2. Of note, the demonstrated mean/SD were fitted values with the effect of head motion, age and site regressed out. Fig. S11. Temporal SD of dynamic stability measures differences between ASD 1 and ASD 2 using two-sample t-test (GRF correction at voxel-level p < 0.001, cluster-level p < 0.01, two-tailed). Fig. S12. Temporal SD of dynamic R-fMRI measures differences between healthy controls and ASD 1 using the two-sample t-tests for (a) DC, (b) GSCorr and (c) ReHo (GRF, voxel-level p < 0.001, cluster-level p < 0.01, two-tailed). Fig. S13. (a) Temporal SD of dynamic stability measures differences between healthy controls and ASD 1 using two-sample t-test (GRF correction at voxel-level p < 0.001, cluster-level p < 0.01, two-tailed). (b) Temporal SD of dynamic stability measures differences between healthy controls and ASD 2 using two-sample t-test (GRF correction at voxel-level p < 0.001, cluster-level p < 0.01, two-tailed). Fig. S14. Average weight ranking differences of ROI in the subtype-healthy control classification. Subtype1-healthy controls (Left) and subtype2-healthy controls (Right). The average weight of the top 40 ROIs is shown here.

BACKGROUND Generalized q-sampling (GQI) and connectometry analysis provide new indices, i.e., quantitative anisotropy (QA) and spin distribution function (SDF) in comparison with diffusion tensor imaging (DTI). They may provide more age-related changes in white matter (WM) in aging. PURPOSE To investigate the feasibility of using GQI and connectometry analysis to determine WM properties changes in aging. STUDY TYPE Cross-cohort research. POPULATION Fifty normal adults (27 females) aged 21-71 years. FIELD STRENGTH/SEQUENCE T1 -weighted images (T1 WI) and high angular resolution diffusion imaging (HARDI) images were acquired at 1.5T. ASSESSMENT HARDI data were analyzed using DTI and GQI to obtain fractional anisotropy (FA), QA, fiber numbers, and fiber lengths for tract analysis and using q-space diffeomorphic reconstruction (QSDR) for the connectometry analysis. We compared differences of DTI, GQI, and connectometry analysis to reflect WM changes in aging. STATISTICAL TESTS Associations between FA, QA, and fiber numbers and lengths and age were analyzed using Pearson's correlation coefficients. The connectometry analysis was conducted using a multiple linear regression analysis, including age and gender as factors. Uncorrected P-value/false discovery rate (FDR) (corrected for multiple comparisons) 6 regions, P 7 regions, P < 0.05). However, DTI and GQI analyses revealed consistent results in some regions, including the genu of the corpus callosum (GCC), body of the corpus callosum (BCC), fornix (Fx), and anterior coronal radiation (ACR) (P < 0.05). The connectometry analysis showed more tract changes associated with age at an FDR of 0.05, which partially overlapped with the FA and QA. DATA CONCLUSION GQI and connectometry provide more information about age-related tracts and complement the DTI findings. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:369-381.

BACKGROUND Altered gray matter (GM) volumes and functional connectivity (FC) happens in attention-deficit/hyperactivity disorder (ADHD) patients and the intelligence quotient (IQ) of ADHD patients is generally lower than healthy subjects, while few studies have focused on the association among the three. PURPOSE To investigate the abnormal changes of GM volumes and FC in ADHD patients compared with typically-developing control (TD-C), as well as the association between the changes and IQ. STUDY TYPE Case control. POPULATION 36 ADHD patients and 36 TD-C, aged 8-17. FIELD STRENGTH/SEQUENCE 3.0T/single shot echo planar imaging (EPI). ASSESSMENT Preprocessing of fMRI data and seed-based FC calculation using DPABI4.0, and VBM was performed by CAT12. STATISTICAL TESTS Comparison between groups of smoothed GM volumes was performed using two-sample t-test within SPM 12, age and total intracranial volume (TIV) were returned as covariates. Comparison between groups of FCs was performed using a two-sample t-test within RESTplus; age was returned as a covariate. False discovery rate (FDR)-corrected and P

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.