×

Error message

  • Failed to authorize with Brightcove
  • Failed to authorize with Brightcove
  • Failed to authorize with Brightcove
  • Failed to authorize with Brightcove
News | March 09, 2015

Autism Detection Improved by Multimodal Neuroimaging

Tracking three different measures via structural MRI could allow autism diagnosis as early as six months

Rajesh Kana, Ph.D., and Lauren Libero, Ph.D., utilized structural MRI to analyze three factors that could lead to earlier diagnosis of autism spectrum disorder. Image courtesy of the University of Alabama at Birmingham.

March 9, 2015 — Rajesh Kana, Ph.D., of the University of Alabama at Birmingham, and colleagues are the first to combine three different measures of the brain to distinguish people with autism spectrum disorder (ASD) from matched, typically developing peers. This multimodal approach, published online in the journal Cortex, uses anatomy, the connectivity between different brain regions and levels of a neurochemical for measurement; this is distinct from many previous studies that have used a single neuroimaging measure. While those studies uncovered widespread functional and anatomical brain abnormalities in ASD, the results were not highly consistent, possibly reflecting the complex brain pathology in autism spectrum disorders.

At this time, autism diagnosis is based on behavior. Kana’s multimodal neuroimaging-based classification is a step toward a possible biomarker for autism and possibly diagnosing autism at an early age, perhaps as early as 6 months, when the brain is very plastic and intervention might be more effective. “But that’s a long, long way off,” said Kana, an associate professor in the Department of Psychology in the UAB College of Arts and Sciences and an associate scientist in UAB’s Civitan International Research Center.

This preliminary study needs to be validated with a larger sample, Kana said; but it “emphasizes that the brain abnormalities in autism may not be confined to a single area. Rather, they are distributed across different areas at multiple levels and layers.”

Kana, corresponding author of the study, examined 19 high-functioning adults with ASD and 18 typically developing peers, who were matched for age and intelligence. Using the 3-Tesla scanner in the UAB Civitan Functional Neuroimaging Laboratory, Kana’s group performed structural magnetic resonance imaging (MRI) to measure brain cortical thickness (volume data), diffusion tensor imaging to measure the connectivity of white-matter fibers of the brain and proton magnetic resonance spectroscopy to measure brain neurotransmitters like N-acetylaspartate. The brain’s white-matter areas are like electrical cables that link different regions of the brain.

The same MRI machine does all three measurements, using different settings for each. The volume data take 10 minutes, connectivity measurements take 12 minutes and the spectroscopy takes 30 minutes. While some participants were comfortable in the tight space of an MRI, others needed training in a nonfunctioning simulator MRI before any testing.

Kana’s group found significant differences in some specific measurements using each of the three neuroimaging approaches. They then combined certain of these key differences into a decision tree model — akin to the differential diagnosis flowchart used by clinicians. This decision-tree model gave a classification accuracy of 91.9 percent for distinguishing ASD subjects from the controls.

“Autism is such a heterogeneous disorder, and every patient presents with different symptoms and levels of severity,” said Lauren Libero, Ph.D., first author of the study. “That makes it really challenging to try to find one explanation within the brain for the very complex symptoms of autism.”

“This study is important,” said Libero who is now a post-doctoral scholar at the University of California-Davis MIND Institute (Medical Investigation of Neurodevelopmental Disorders), “because we found that different combinations of alterations in the brain could lead to the same or different levels of symptom severity. This could explain why previous studies have found varying results when it comes to which areas of the brain are affected in autism. There likely is not one uniform pattern affecting everyone with autism.”

“We also found that combining different MRI techniques led to better classification of our participants with autism,” Libero said. “Most previous studies have focused on using one technique at a time, even though we have evidence that there are alterations in the brain in autism in terms of structure, white-matter connectivity, and brain chemical concentrations. When we are looking at a disorder that is so complex, multiple modalities of investigation can be more efficient to separate autism from other disorders, or to identify subgroups within autism. Our study found a way to combine measures of brain structure, white matter diffusion, and neurochemical concentration to classify our participants by their diagnosis, as well as their level of autism severity.”

Specific significant findings in the Libero et al. Cortex paper include:Increased cortical thickness in ASD participants, compared to controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex and the right precuneus;

  • Reduced cortical thickness in the right cuneus and right precentral gyrus;
  • Reduced white matter connectivity (as measured by reduced fractional anisotropy and increased radial diffusivity) for two discrete clusters on the forceps minor of the corpus callosum; and
  • Reduction in N-acetylaspartate in the dorsal anterior cingulate cortex.
     

Just three of these significant differences — radial diffusivity in the right forceps minor, cortical thickness in the left pars opercularis and fractional anisotropy in the left forceps minor —yielded the best decision tree for distinguishing ASD participants from controls.

Kana and colleagues also built a decision tree with five of the significant findings that sorted ASD participants by disease severity.

For more information: www.uab.edu

Related Content

Technology | Focused Ultrasound Therapy | June 19, 2018
EDAP TMS SA has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its Focal One device for...
Elekta Unity High-Field MR-Linac Receives CE Mark
News | Image Guided Radiation Therapy (IGRT) | June 18, 2018
Elekta announced that its Elekta Unity magnetic resonance radiation therapy (MR/RT) system has received CE mark,...
Washington University in St. Louis Begins Clinical Treatments With ViewRay MRIdian Linac
News | Image Guided Radiation Therapy (IGRT) | June 14, 2018
June 14, 2018 — The Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine in S
Reduced hippocampal volume on MRI

This figure shows reduced hippocampal volume over the course of 6 years as seen on progressive volumetric analysis and also coronal MRI evaluations (arrows).Progressive volume loss in the mesial temporal lobe on MRI is a characteristic imaging feature of AD. This patient was a case of Alzheimer’s Dementia.

 

News | Neuro Imaging | June 12, 2018
According to a UCLA Medical Center study, a new technology shows the potential to help doctors better determine when...
High Prevalence of Atherosclerosis Found in Lower Risk Patients
News | Magnetic Resonance Imaging (MRI) | June 08, 2018
Whole-body magnetic resonance angiography (MRA) found a surprisingly high prevalence of atherosclerosis in people...
Philips Receives FDA 510(k) for Ingenia Elition MR System
Technology | Magnetic Resonance Imaging (MRI) | June 07, 2018
Philips announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its...
New Studies Highlight MRI Use for Prostate Cancer Screening and Management
News | Magnetic Resonance Imaging (MRI) | May 21, 2018
Three new studies presented at the 113th annual meeting of the American Urological Association (AUA) highlight the...
MRI "Glove" Provides New Look at Hand Anatomy

An experiment showed that a glove-shaped detector could yield images of bones, cartilage, and muscles interacting as a hand 'plays piano.' Traditionally, MRI had required patients to remain strictly motionless.Image courtesy of Nature Biomedical Engineering; Bei Zhang, Martijn Cloos, Daniel Sodickson

News | Magnetic Resonance Imaging (MRI) | May 17, 2018
A new kind of magnetic resonance imaging (MRI) component in the shape of a glove delivers the first clear images of...
FDA Clears Medic Vision's iQMR MRI Image Enhancement Technology

Image courtesy of Medic Vision Imaging Solutions

Technology | Magnetic Resonance Imaging (MRI) | May 15, 2018
May 15, 2018 — Medic Vision Imaging Solutions Ltd. announced that the U.S.
Impaired Brain Pathways May Cause Attention Problems After Stroke
News | Neuro Imaging | May 10, 2018
Damage to some of the pathways that carry information throughout the brain may be responsible for attention deficit in...
Overlay Init