Neuroscientists from Case Western Reserve University School
of Medicine and the University of Toronto have developed an efficient and
reliable method of analysing brain activity to detect autism in children.
findings appear in the online journal PLOS
How the research was
The researchers recorded and analysed dynamic patterns of
brain activity with magnetoencephalography (MEG) to determine the brain's
functional connectivity – that is, its communication from one region to
another. MEG measures magnetic fields generated by electrical currents in
neurons of the brain.
Roberto Fernández Galán, PhD, an assistant professor of
neurosciences at Case Western Reserve and an electrophysiologist seasoned in
theoretical physics led the research team that detected autism spectrum
disorder (ASD) with 94 percent accuracy. The new analytic method offers an
efficient, quantitative way of confirming a clinical diagnosis of autism.
"We asked the question, 'Can you distinguish an
autistic brain from a non-autistic brain simply by looking at the patterns of
neural activity?' and indeed, you can," Galán said. "This discovery
opens the door to quantitative tools that complement the existing diagnostic
tools for autism based on behavioural tests."
In a study of 19 children—nine with ASD—141 sensors tracked
the activity of each child's cortex. The sensors recorded how different regions
interacted with each other while at rest, and compared the brain's interactions
of the control group to those with ASD. Researchers found significantly
stronger connections between rear and frontal areas of the brain in the ASD
group; there was an asymmetrical flow of information to the frontal region, but
not vice versa.
The new insight into the directionality of the connections
may help identify anatomical abnormalities in ASD brains. Most current measures
of functional connectivity do not indicate the interactions' directionality.
"It is not just who is connected to whom, but rather
who is driving whom," Galán said.
High rate of accuracy
Their approach also allows them to measure background noise,
or the spontaneous input driving the brain's activity while at rest. A spatial
map of these inputs demonstrated there was more complexity and structure in the
control group than the ASD group, which had less variety and intricacy. This
feature offered better discrimination between the two groups, providing an even
stronger measure of criteria than functional connectivity alone, with 94% accuracy.
Case Western Reserve's Office of Technology Transfer has
filed a provisional patent application for the analysis' algorithm, which
investigates the brain's activity at rest. Galán and colleagues hope to
collaborate with others in the autism field with emphasis on translational and
Galán's collaborators and co-authors of this study are
University of Toronto's associate researcher, Luis García Domínguez, PhD, and
professor José Luis Pérez Velázquez, PhD.