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Using MRI to detect ADHD

Information from brain MRIs can help identify people with attention deficit hyperactivity disorder (ADHD) and distinguish among sub-types of the condition, according to a Chinese study.

ADHD is a disorder of the brain characterised by periods of inattention, hyperactivity and impulsive behaviour. The disorder affects 5% to 7% of children and adolescents worldwide, according to the ADHD Institute. The three primary sub-types of ADHD are predominantly inattentive, predominantly hyperactive/impulsive and a combination of inattentive and hyperactive.

While clinical diagnosis and sub-typing of ADHD is currently based on reported symptoms, psycho-radiology, which applies imaging data analysis to mental health and neurological conditions, has emerged in recent years as a promising tool for helping to clarify diagnoses.

Study co-author Dr Qiyong Gong and colleagues at West China Hospital of Sichuan University in Chengdu, China, recently introduced an analytical framework for psycho-radiology that involves cerebral radiomics – the extraction of a large amount of quantitative information from digital imaging features that can be mined for disease characteristics. Radiomics, combined with other patient characteristics, could improve diagnostic power and help speed appropriate treatment to patients.

"The main aim of the current study was to establish classification models that can assist the psychiatrist or clinical psychologist in diagnosing and sub-typing of ADHD based on relevant radiomics signatures," Gong said.

With the help of his West China Hospital colleagues Dr Huaiqiang Sun and Dr Ying Chen, Gong studied 83 children, ranging in age from of 7 to 14, with newly diagnosed and never-treated ADHD. The group included children with the inattentive ADHD sub-type and the combined sub-type. Researchers compared brain MRI results with those of a control group of 87 healthy, similarly aged children. The researchers used a relatively new feature that allowed them to screen relevant radiomics signatures from more than 3,100 quantitative features extracted from the grey and white matter.

No overall difference was found between ADHD and controls in total brain volume or total grey and white matter volumes. However, differences emerged when the researchers looked at specific regions within the brain. Alterations in the shape of three brain regions (left temporal lobe, bilateral cuneus and areas around left central sulcus) contributed significantly to distinguishing ADHD from typically developing controls.

Within the ADHD population, features involved in the default mode network, which is a network of brain regions active when an individual is not engaged in a specific task, and the insular cortex, an area with diverse functions related to emotion, significantly contributed to discriminating the ADHD inattentive subtype from the combined sub-type.

Overall, the radiomics signatures allowed discrimination of ADHD patients and healthy control children with 74% accuracy and discrimination of ADHD inattentive and ADHD combined sub-types with 80% accuracy.

"This imaging-based classification model could be an objective adjunct to facilitate better clinical decision making," Gong said. "Additionally, the present study adds to the developing field of psycho-radiology, which seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders."

The researchers plan to recruit more newly diagnosed ADHD patients to validate the results and learn more about imaging-based classification. They also intend to apply the analytic approach to other mental or neurological disorders and test its feasibility in a clinical environment, where the fully automatic analytic framework can be readily deployed, Gong said.

Abstract
Purpose: To identify cerebral radiomic features related to diagnosis and subtyping of attention deficit hyperactivity disorder (ADHD) and to build and evaluate classification models for ADHD diagnosis and subtyping on the basis of the identified features.
Materials and Methods: A consecutive cohort of 83 age- and sex-matched children with newly diagnosed and never-treated ADHD (mean age 10.83 years ± 2.30; range, 7–14 years; 71 boys, 40 with ADHD-inattentive [ADHD-I] and 43 with ADHD-combined [ADHD-C, or inattentive and hyperactive]) and 87 healthy control subjects (mean age, 11.21 years ± 2.51; range, 7–15 years; 72 boys) underwent anatomic and diffusion-tensor magnetic resonance (MR) imaging. Features representing the shape properties of gray matter and diffusion properties of white matter were extracted for each participant. The initial feature set was input into an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for diagnosis and subtyping. Random forest classifiers were constructed and evaluated on the basis of identified features.
Results: No overall difference was found between children with ADHD and control subjects in total brain volume (1069830.00 mm3 ± 90743.36 vs 1079 213.00 mm3 ± 92742.25, respectively; P = .51) or total gray and white matter volume (611978.10 mm3 ± 51622.81 vs 616960.20 mm3 ± 51872.93, respectively; P = .53; 413532.00 mm3 ± 41 114.33 vs 418173.60 mm3 ± 42395.48, respectively; P = .47). The mean classification accuracy achieved with classifiers to discriminate patients with ADHD from control subjects was 73.7%. Alteration in cortical shape in the left temporal lobe, bilateral cuneus, and regions around the left central sulcus contributed significantly to group discrimination. The mean classification accuracy with classifiers to discriminate ADHD-I from ADHD-C was 80.1%, with significant discriminating features located in the default mode network and insular cortex.
Conclusion: The results of this study provide preliminary evidence that cerebral morphometric alterations can allow discrimination between patients with ADHD and control subjects and also between the most common ADHD subtypes. By identifying features relevant for diagnosis and subtyping, these findings may advance the understanding of neurodevelopmental alterations related to ADHD.

Authors
Huaiqiang Sun, Ying Chen, Qiang Huang, Su Lui, Xiaoqi Huang, Yan Shi, Xin Xu, John A Sweeney, Qiyong Gong

[link url="https://www.sciencedaily.com/releases/2017/11/171122093106.htm"]Radiological Society of North America material[/link]
[link url="http://pubs.rsna.org/doi/10.1148/radiol.2017170226"]Radiology abstract[/link]

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