To improve upon this technology, researchers created a souped-up MRI outfitted with a high-powered 9.4-tesla magnet. (For comparison, most MRIs are equipped with a 1.5-to 3-tesla magnet.) They also added gradient coils that are 100 times stronger than current models and are what create the images, as well as a high-speed computer that is as powerful as approximately 800 laptops, according to the statement.

After scanning the mouse brain, the researchers sent tissue samples to be imaged using a technique called light sheet microscopy, which allowed them to label specific groups of cells in the brain that were then mapped onto the original MRI. These additional steps provided a colorful view of cells and circuits throughout the brain, according to the statement.

The researchers took one set of MRI images that captured how the mouse’s brain-wide connectivity evolved with age. A second group of images showcased brilliantly colored brain connections that highlighted the deterioration of neural networks in a rodent model of Alzheimer’s disease, according to the statement.

Autism spectrum disorder (ASD) is a developmental disorder associated with difficulties in interacting with others, repetitive behaviors, restricted interests and other symptoms that can impact academic or professional performance. People diagnosed with ASD can present varying symptoms that differ in both their behavioral manifestations and intensity.

As a result, some autistic individuals often require far more support than others to complete their studies, learn new skills and lead a fulfilling life. Neuroscientists have been investigating the high variability of ASD for several decades, with the hope that this will aid the development of more effective therapeutic strategies tailored around the unique experiences of different patients.

Researchers at Weill Cornell Medicine have recently used machine learning to investigate the molecular and neural mechanisms that could underlie these differences among individuals diagnosed with ASD. Their paper, published in Nature Neuroscience, identifies different subgroups of ASD associated with distinct functional connections in the brain and symptomatology, which could be related to the expression of different ASD-related genes.