We report results from 21-cm intensity maps acquired from the Parkes radio telescope and cross-correlated with galaxy maps from the 2dF galaxy survey. The data span the redshift range 0.057 < z < 0.098 and cover approximately 1,300 square degrees over two long fields. Cross correlation is detected at a significance of 5.18 σ. The amplitude of the cross-power spectrum is low relative to the expected dark matter power spectrum, assuming a neutral hydrogen (HI) bias and mass density equal to measurements from the ALFALFA survey. The decrement is pronounced and statistically significant at small scales. At k ∼ 1.5 h Mpc −1 , the cross power spectrum is more than a factor of 6 lower than expected, with a significance of 14.8 σ. This decrement indicates either a lack of clustering of neutral hydrogen (HI) , a small correlation coefficient between optical galaxies and HI , or some combination of the two. Separating 2dF into red and blue galaxies, we find that red galaxies are much more weakly correlated with HI on k ∼ 1.5 h Mpc −1 scales, suggesting that HI is more associated with blue star-forming galaxies and tends to avoid red galaxies.

Purpose: Develop and evaluate a deep learning approach to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-labeling delay (PLD) ASL MRI.Methods: ASL MRI were acquired with 6 PLDs on a 1.5T or 3T GE system in adults with and without cognitive impairment (N = 99). Voxel-level CBF and ATT maps were quantified by training models with distinct convolutional neural network architectures: (1) convolutional neural network (CNN) and (2) U-Net. Models were trained and compared via 5-fold cross validation. Performance was evaluated using mean absolute error (MAE). Model outputs were trained on and compared against a reference ASL model fitting after data cleaning. Minimally processed ASL data served as another benchmark. Model output uncertainty was estimated using Monte Carlo dropout. The better-performing neural network was subsequently re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules.Results: Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4 ± 1.4 mL/100 g/min for CBF and 0.22 ± 0.09 s for ATT. A significant association was observed between MAE and Monte Carlo dropout-based uncertainty estimates. Neural network performance remained stable despite systematically reducing the number of input images (i.e., up to 3 missing PLD images). Mean processing time was 10.77 s for the U-Net neural network compared to 10 min 41 s for the reference pipeline. Conclusion:It is feasible to generate CBF and ATT maps from 1.5T and 3T multi-PLD ASL MRI with a fast deep learning image-generation approach.

Adolescence is a period of rapid development of the brain’s inherent functional and structural networks; however, little is known about the region-to-region organization of adolescent cerebral blood flow (CBF) or its relationship to neuroanatomy. Here, we investigate both the regional covariation of CBF MRI and the covariation of structural MRI, in adolescents with and without bipolar disorder. Bipolar disorder is a disease with increased onset during adolescence, putative vascular underpinnings, and evidence of anomalous CBF and brain structure. In both groups, through hierarchical clustering, we found CBF covariance was principally described by clusters of regions circumscribed to the left hemisphere, right hemisphere, and the inferior brain; these clusters were spatially reminiscent of cerebral vascular territories. CBF covariance was associated with structural covariance in both the healthy group (n = 56; r = 0.20, p < 0.0001) and in the bipolar disorder group (n = 68; r = 0.36, p < 0.0001), and this CBF-structure correspondence was higher in bipolar disorder ( p = 0.0028). There was lower CBF covariance in bipolar disorder compared to controls between the left angular gyrus and pre- and post-central gyri. Altogether, CBF covariance revealed distinct brain organization, had modest correspondence to structural covariance, and revealed evidence of differences in bipolar disorder.