A new method that merges tissue expansion, light-sheet microscopy and automated image segmentation can reconstruct neural circuits in about a week.

The method yields complex organoids that more closely mimic embryonic brain development than do those cultured in other ways.

The open-source software makes it possible to overlay disparate datasets and potentially accelerate hypothesis generation.

The technique could be used to identify and control cells involved in autism.

The developmental models have advantages over natural embryos and other synthetic models, such as organoids, but present technical and ethical challenges.

One drug blocks mTOR signaling, and the other stops the blocker from acting anywhere in the body but the brain, lowering the potential for side effects.

The machine-learning approach could help identify how brain structure differs between autistic and non-autistic infants, the researchers say.

The catalog of rare copy number variants tied to autism and other conditions could help researchers identify which genes account for the mutations’ effects.

The new tool may help researchers reconstruct the sequence of biological events that underlie development.

The method could boost reproducibility across brain imaging studies of autism.