New method use functional magnetic resonance imaging (fMRI) to diagnose autism


Scientists at Wake Forest School of Medicine have taken the first step in developing an objective, brain-based test to diagnose autism.

Using functional magnetic resonance imaging (fMRI), the team was able to measure the response of autistic children to different environmental cues by imaging a specific part of the brain involved in assigning value to social interactions.

Findings from the study are published in the current online edition of the journal Biological Psychology.

“Right now, a two- to four-hour session by a qualified clinician is required to diagnose autism, and ultimately it is a subjective assessment based on their experience,” said the study’s principal investigator, Kenneth Kishida, Ph.D., assistant professor of physiology and pharmacology at Wake Forest School of Medicine, part of Wake Forest Baptist Health.

“Our test would be a rapid, objective measurement of the brain to determine if the child responds normally to social stimulus versus non-social stimulus, in essence a biomarker for autism.”

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and interaction with other people.

The National Institutes of Health estimates that 1 in 60 children in the United States are autistic.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that starts in childhood and lasts a lifetime. ASD introduces three fundamental characteristics: qualitative disruption of relationships, alterations in the communication and speaking skills, and a lack of mental and behavioral flexibilities.

This suggests that these deficits observed in patients with ASD are the result of a deficient executive function that includes problems in working memory, inhibition, mental flexibility, and planning [1].

Worldwide, the prevalence of ASD is 1 in 100 children [2].

Unfortunately, no treatment can cure this disorder and, due to the heterogeneity in phenotype, personalized therapy is necessary.

Nowadays, the diagnosis of ASD consists of a combination of clinical and psychological tests that aim to detect the associated symptoms of this disorder using the Diagnostic and Statistical Manual of Mental Disorders (DSM) [3].

Consequently, the diagnosis is focused on observable symptoms and not on early signs.

For this reason, it is subjective and late, since the symptoms usually appear from the age of three years old.

Therefore, it is of utmost importance to provide biomarkers to characterize the brain’s functional alterations using functional magnetic resonance imaging (fMRI) images of the brain [4].

Commonly, MRI images are being used in this field because they are a powerful non-invasive tool for studying the developmental trajectory of the brain.

Quantitative MRI biomarkers promise to achieve early diagnosis of this disorder which would increase the effectiveness of therapies to improve the behavior and communication of autistic people [5].

The analysis of low-frequency oscillations in the blood-oxygen-level-dependent (BOLD) signal, extracted from fMRI images, indirectly allows calculating neuronal activity.

Functional connectivity (FC) is defined as the statistical dependence between BOLD signals. FC shows how the regions are organized and interrelated, and reveals which regions communicate with other regions to serve specific functions [6].

To diagnose ASD, it is important to acquire fMRI images in resting state (rs-fMRI) because one of the most important brain networks with anomalies in FC due to ASD—the default mode network (DMN)—it is activated in rest [7].

Several research studies show discrepancies in brain FC in the DMN when individuals with ASD are being compared with control subjects: hyperconnectivity (children [8,9] and children together with adolescents [10,11]) or hypoconnectivity (children and adolescents [12]; children, adolescents, and adults [13], adolescents [14]; adolescents and adults [15]; and adults [16,17]).

The theories of hypoconnectivity explain a reduction in the interregional neuronal connections, whereas the hyperconnectivity studies an increment of neuronal connections between some brain regions [1].

Among the possible causes of these contradictory results are (1) changes in brain development because of a wide age range [18], (2) a different methodology of analysis and different fMRI acquisition parameters [19], and (3) ASD heterogeneity [1].

Our purpose was the statistical analysis of the alterations in brain FC inside the DMN and other brain regions directly implicated between autists and healthy controls.

The subjects were divided into different age ranges (children, adolescents, and adults), to study autism from a developmental point of view, performing a correlation analysis between brain regions from rs-fMRI images.

With the separation in age ranges we aim to give a response to the controversy of hyper- and hypoconnectivities between the same brain networks in previous studies where subjects were not divided into age groups, despite ASD being a neurodevelopmental condition.

In contrast to seed-based correlation analyses (SCA) [20] and independent component analysis (ICA) [21] that are focused on functionally connected networks with connectivity weights, ROI-based analyses [22] facilitate the biological interpretation of the results because the ROIs are anatomical brain regions with a known function.

In the study, the team led by Kishida and P. Read Montague, Ph.D., of Virginia Tech, tested the responsiveness of the brain’s ventral medial prefrontal cortex (vmPFC) to visual cues that represented highly-valued social interaction in children diagnosed with ASD compared to typically developing (TD) children.

The study included 40 participants ranging in age from 6 to18; 12 had ASD and 28 were TD.

First, the study participants were scanned in an fMRI while viewing eight images of either people or objects, each one multiple times.

Included in each set of images were two self-selected pictures of a favorite person and object from each participant.

The other six were standardized images of three faces and three objects, each representing pleasant, neutral or unpleasant aspects from a data base widely used in psychological experiments.

After completing the 12- to 15-minute MRI scan, the children viewed the same set of images on a computer screen and ranked them in order from pleasant to unpleasant with a self-assessing sliding scale.

In addition, pairs of images were viewed and ranked as to which one they liked better.

According to the study, the average response of the vmPFC was significantly lower in the ASD group than in the TD group.

Using images as a single stimulus to capture 30 seconds of fMRI data was sufficient to differentiate the ASD and TD groups, Kishida said.

“How the brain responded to these pictures is consistent with our hypothesis that the brains of children with autism do not encode the value of social exchange in the same way as typically developing children,” he said.

“Based on our study, we envision a test for autism in which a child could simply get into a scanner, be shown a set of pictures and within 30 seconds have an objective measurement that indicates if their brain responds normally to social stimulus and non-social stimuli.”

He added that this approach could also help scientists better understand the brain mechanisms involved in autism disorder as a whole as well as the many variations on the disorder’s spectrum.

Kishida’s team plans to do follow-up studies to identify which additional areas of the brain are involved in the different facets of the disorder to help personalize treatments for patients.

Provided by Wake Forest University Baptist Medical Center


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