Predicting disease severity in children with combined attention deficit hyperactivity disorder using quantitative
Shanghu Song1,2, Jianfeng Qiu1,2, Weizhao Lu1,2,3
1Medical Engineering and Technology Research Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, People's Republic of China.
Insights
Brain imaging of the amygdala and hippocampus can predict attention deficit hyperactivity disorder (ADHD) severity in children. This study used MRI data to identify volumetric features linked to ADHD-C, offering potential biomarkers for disease evaluation.
Area of Science:
- Neuroscience
- Radiology
- Psychiatry
Background:
- Children with combined attention deficit hyperactivity disorder (ADHD-C) exhibit volumetric alterations in amygdaloid and hippocampal subfields.
- Understanding these structural changes is crucial for predicting disease severity.
Purpose of the Study:
- To investigate the predictive capability of amygdaloid and hippocampal subfield volumetric changes for disease severity in children with ADHD-C.
- To identify specific subregions within the amygdala and hippocampus associated with ADHD severity.
Main Methods:
- Utilized T1 structural MRI data from 76 ADHD-C patients from the ADHD-200 datasets.
- Extracted 64 structural features from the amygdala and hippocampus.
- Employed Sequential Backward Elimination (SBE) for feature selection and a linear Support Vector Regression (SVR) model to predict ADHD severity using three ADHD rating scales.
Main Results:
- The SBE-SVR model accurately predicted ADHD severity, achieving a high correlation (0.7164, p<0.001) for the ADHD index.
- Identified specific amygdaloid and hippocampal subregions significantly related to ADHD severity based on SVR model weights.
- The model demonstrated a mean squared error of 43.6868 and mean absolute error of 3.2893.
Conclusions:
- The SBE-SVR approach effectively predicts ADHD-C severity using quantitative MRI-based features from the amygdala and hippocampus.
- These subcortical structures show potential as biomarkers for monitoring ADHD progression and evaluating treatment effectiveness.
Abstract:
Objective. Volumetric changes in the amygdaloid and hippocampal subfields have been observed in children with combined attention deficit hyperactivity disorder (ADHD-C). The purpose of this study was to investigate whether volumetric changes in the amygdaloid and hippocampal subfields could be used to predict disease severity in children with ADHD-C.Approach. The data used in this study was from ADHD-200 datasets, a total of 76 ADHD-C patients were included in this study. T1 structural MRI data were used and 64 structural features from the amygdala and hippocampus were extracted. Three ADHD rating scales were used as indicators of ADHD severity. Sequential backward elimination (SBE) algorithm was used for feature selection. A linear support vector regression (SVR) was configured to predict disease severity in children with ADHD-C.Main results. The three ADHD rating scales could be accurately predicted with the use of SBE-SVR. SBE-SVR achieved the highest accuracy in predicting ADHD index with a correlation of 0.7164 (p< 0.001, tested with 1000-time permutation test). Mean squared error of the SVR was 43.6868, normalized mean squared error was 0.0086, mean absolute error was 3.2893. Several amygdaloid and hippocampal subregions were significantly related to ADHD severity, as revealed by the absolute weight from the SVR model.Significance. The proposed SBE-SVR could accurately predict the severity of patients with ADHD-C based on quantitative features extracted from the amygdaloid and hippocampal structures. The results also demonstrated that the two subcortical nuclei could be used as potential biomarkers in the progression and evaluation of ADHD.
