USC Mark and Mary Stevens Neuroimaging and Informatics Institute Newsletter / Summer 2021
Drs. Danny JJ Wang and Xingfeng Shao developed a new MRI method called diffusion-prepared arterial spin labeling (DP-ASL) that can detect subtle changes in blood-brain barrier (BBB) dysfunction by measuring water exchange across the BBB. They collaborated with researchers at the University of Kentucky to test whether it can provide a noninvasive imaging biomarker for early BBB problems associated with cerebral small vessel disease, with promising results. The paper was published in May in Alzheimer’s & Dementia.
A USC team led by the INI’s Dr. Dominique Duncan studied disparities in COVID-19 vaccination across California’s 58 counties using the Social Vulnerability Index (SVI). They found that vaccination coverage was lower in counties with high socio-demographic vulnerability, particularly along the lines of minority status and language. The study’s authors hope the findings can help inform future vaccine distribution policies to better promote equity.
A team that included the INI’s Dr. Michael Bienkowski, Dr. Ryan Cabeen, and others created a comprehensive connectivity map of the BLA, a region of the brain involved in behaviors such as fear acquisition and addiction. They used machine learning to analyze circuit-tracing data, identifying three new subdivisions of the anterior BLA. The results were published in Nature Communications in May.
The INI’s Drs. Neda Jahanshad and Paul Thompson coauthored an analysis of neuroimaging and clinical data from nearly 19,000 people across North America, Europe, Asia, and Australia. Of the individuals studied, 694 had attempted suicide, 6,448 had been diagnosed with depression but had not attempted suicide, and 12,477 were healthy controls. Those who had attempted suicide had slightly less volume in the thalamus, an area of the brain involved in processing sensory and motor signals, and the right palladum, which may be related to reward and motivation, compared to the other groups. The study was published in March in the journal Biological Psychiatry.
A team led by INI Director Dr. Arthur W. Toga and Dr. Kaida Ning used a new mathematical model to predict brain age in a sample of more than 15,000 UK Biobank subjects. The convolutional neural network (CNN) model improved the accuracy of brain age estimates and led to the discovery of new genetic markers linked to brain aging. This may in term help researchers identify other lifestyle factors associated with brain aging. The paper appears in the journal Neurobiology of Aging in September.