Psychoradiologic Utility of MR Imaging for Diagnosis of Attention Deficit Hyperactivity Disorder: A Radiomics Analysis

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 mm ± 90743.36 vs 1079 213.00 mm ± 92742.25, respectively; P = .51) or total gray and white matter volume (611978.10 mm ± 51622.81 vs 616960.20 mm ± 51872.93, respectively; P = .53; 413532.00 mm ± 41 114.33 vs 418173.60 mm ± 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. RSNA, 2017 Online supplemental material is available for this article.

Reduced local segregation of single‐subject gray matter networks in adult PTSD

“…Present study particularly adds to psychoradiology, an evolving specialty as opposed to neuroradiology, mainly for psychiatric and psychological brain (Lui et al, 2016;Kressel et al, 2017;Sun et al, 2018;Port, 2018). …”

Disrupted brain functional networks in drug‐naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis

“…Prior ADHD neuroimaging studies have shown that the inhibitory behavioral control impairments in ADHD are related to regional abnormalities in the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), temporal/parietal areas, and basal ganglia (Chen et al, ; Chen et al, ; Lei et al, ; Lei, Du, et al, ; Lei, Li, et al, ; Li, He, et al, ; Li, Li, et al, ; Sun et al, ). Using a seed‐based functional connectivity analysis, we found that alterations in frontostriatal circuitry correlated with the degree of inhibitory executive dysfunction in ADHD (Li, He, et al, ; Li, Li, et al, ).…”

“…Prior ADHD neuroimaging studies have shown that the inhibitory behavioral control impairments in ADHD are related to regional abnormalities in the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), temporal/parietal areas, and basal ganglia Chen et al, 2016;Lei et al, 2014;Lei, Du, et al, 2015;Lei, Li, et al, 2015;Sun et al, 2018).…”

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults

“…With advances in quantitative neuroimaging techniques, radiomics may provide a promising modality for predicting WMH progression . Functional MRI has revealed that the destruction of microstructure integrity in cerebral white matter occurs prior to the conventional neuroimaging visibility of WMH, suggesting the existence of a white matter penumbra in which the parenchyma is visually normal but the microstructure has been damaged .…”

“…12 With advances in quantitative neuroimaging techniques, [13][14][15] radiomics may provide a promising modality for predicting WMH progression. [16][17][18] Functional MRI has revealed that the destruction of microstructure integrity in cerebral white matter occurs prior to the conventional neuroimaging visibility of WMH, suggesting the existence of a white matter penumbra in which the parenchyma is visually normal but the microstructure has been damaged. 12,19 In our previous cross-sectional study, 20 the WMH penumbra could be discriminated by a radiomics textural analysis, indicating that radiomics is a feasible technique to analyze microstructural changes in WMH.…”