<p>Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach</p>

Abstract

In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from wholebrain resting-state functional connectivity patterns using machine learning. Patients and Methods: We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests. Results: The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05). Conclusion: This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.

Caudate and cerebellar involvement in altered P2 and P3 components of GO/NoGO evoked potentials in children with attention‐deficit/hyperactivity disorder

“…Besides, some authors argue that the cerebellum may control the cognitive development of the child by guiding the maturation of diffuse brain circuitry (Wang et al., 2014). In this line, the recent (Sun et al., 2020) demonstrated with the use of a support vector machine applied on fMRI signals that the functional connections between the cerebellum and fronto‐parietal cortex provide the most discriminative power in ADHD versus. TDC classification.…”

The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

“…Furthermore, Sutcubasi and colleagues (Sutcubasi et al, 2020) found that ADHD was associated with a connectivity disruption within the DMN than HC. Sun and colleagues (Sun et al, 2020) et al, 2012) found decreased fALFF in the medial prefrontal and orbitofrontal cortices and DMNs in bilateral putamen with increased rsFC in the left insula and bilateral dorsal prefrontal cortex. In SCHZ, ALFF was reduced in the bilateral ventral frontal cortex (Lui et al, 2010).…”

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach

“…Previous studies have suggested that functional interactions in large-scale brain networks could be utilized for the classification of patients vs. healthy subjects. For example, interregional functional connections were regarded as discriminative features of support vector machine (SVM) to differentiate patients diagnosed with attention deficit hyperactivity disorder (ADHD) from healthy subjects (Sun et al, 2020 ). Whole-brain functional connections were also adopted as features of SVM to identify patients with schizophrenia (Li et al, 2019 ).…”

“…Machine learning has been regarded as an exploratory framework to characterize the brain functional organization from interregional functional connections that may be implicated in neuropathology underlying neuropsychiatric disorders. For example, the cerebellum showed high discriminative power for identifying patients with ADHD (Sun et al, 2020 ). The functional connections across the DMN and visual cortical areas showed high discriminative power when discriminating major depressive patients from healthy subjects (Zeng et al, 2012 ).…”