Method identifies brain areas linked to attention deficit

Researchers at the University of São Paulo are developing statistical and computational techniques to analyze large biological datasets, such as MRI brain scans.

By Elton Alisson

Agência FAPESP – A group of researchers at the University of São Paulo’s Mathematics & Statistics Institute (IME-USP) are developing statistical and computational techniques to analyze large volumes of data, such as magnetic resonance images of the brain, for use in identifying biological markers of neurological dysfunctions, including attention deficit hyperactivity disorder (ADHD).

The results of the research project, which is supported by FAPESP, were presented at a meeting entitled “Brazil-UK Frontiers of Engineering,” held in November in Jarinu, a city in the interior of São Paulo State.

Organized by the United Kingdom’s Royal Academy of Engineering in collaboration with FAPESP, the event brought together 63 young researchers from various areas of engineering – 33 from Brazil and 30 from the UK – affiliated with universities, research institutions and private enterprise.

“Eight years ago, we began developing techniques that combine statistics and computer science to analyze neuroscience and molecular biology datasets with the aim of identifying more objective biological markers based on a quantitative analysis of brain dysfunctions,” said André Fujita, a professor at IME-USP and coordinator of the project, in an interview with Agência FAPESP.

According to Fujita, the techniques under development can be used to compare the variability of neural networks and other biological data clusters in different populations.

Thanks to previous research by the same group at IME-USP, it was already known that the structure of neural networks in patients diagnosed with ADHD is different. In these individuals, the brain presents a type of functional disorganization of neural circuits known as network entropy.

Now the researchers have introduced a novel statistical technique called ANOCVA (analysis of cluster structure variability), which was developed in collaboration with colleagues at Universidade Federal do ABC (UFABC), Princeton University in the United States, Universidade Federal de São Paulo (Unifesp), and the University of Campinas (Unicamp), to identify specific brain regions that are involved in ADHD and present greater network entropy.

In the study, they compared resting-state brain functional magnetic resonance imaging (fMRI) scans for more than 600 children and young adults aged between 7 and 21, with and without ADHD.

The images, produced in eight countries, are held by the ADHD-200 Consortium, a collaborative science initiative involving universities and research institutions in the United States, Netherlands and China. The consortium maintains an open database with fMRI images of the brains of children and adolescents with and without ADHD. A comparison of the brain images showed the existence of several differences in brain cluster structures. The clustering structures of the neural subnetworks comprising the post-central gyrus and the superior and inferior temporal gyri in the brains of patients with ADHD exhibited statistically higher network entropy than those in children and adolescents with normal brain development, according to the ANOCVA analysis.

The authors report that the method also identified differences in brain regions that had not previously been linked to ADHD, such as the angular gyrus, which contributes to the integration of information and plays an important role in many cognitive processes.

In the future, Fujita said, it will be possible to diagnose the neurobiological disorder ADHD using brain fMRI and by comparing neural network structures. Furthermore, the new technique can be applied to datasets relating to other conditions.

“The method we propose can also be applied to other biological datasets of interest, not just fMRI data but also gene expression data from people diagnosed with breast cancer,” he explained.

The research group’s findings have been published in the journals Neuroimage and Statistics in Medicine, among others.

The Neuroimage paper by Sato et al., “Measuring network’s entropy in ADHD: A new approach to investigate neuropsychiatric disorders” (doi: 10.1016/j.neuroimage.2013.03.035), can be read at www.sciencedirect.com/science/article/pii/S1053811913002772.

The Statistics in Medicine paper by Fujita et al., “A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data” (doi: 10.1002/sim.6292), can be read at onlinelibrary.wiley.com/doi/10.1002/sim.6292/pdf.