Classification of ADHD with fMRI data and multi-objective optimization

Shao, Lei; You, Yang; Du, Haizhou; Fu, Dongmei

doi:10.1016/j.cmpb.2020.105676

Cited by 20 publications

(20 citation statements)

References 29 publications

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“…In another study Shao et al used functional connectivity measures to classify ADHD from TD controls using RF classifier. The accuracy of RF classifier for PU center was obtained about 67.2%, which was lower than the accuracy obtained by RF classifier in our study (69.5%) (Shao et al, 2020).…”

Section: Resting-state Networkcontrasting

confidence: 90%

“…Table 2 shows that the GB classifier provided a superior accuracy compared to previous studies in ADHD classification, indicating that graph measures derived from rs-fMRI could be as promising biomarkers in the diagnosis of children with ADHD (Colby et al, 2012;Shao et al, 2020). Dos Santos Siqueira et al used graph theory and rs-fMRI data to classify ADHD from TD controls.…”

Section: Resting-state Networkmentioning

confidence: 99%

“…In the brain, nodes may correspond to predefined brain regions, whereas edges represent functional pairwise correlations among those nodes. While previous studies demonstrated the ability of rs-fMRI in identifying patients with ADHD from healthy controls, the utility of graph measures obtained from rs-fMRI data in the classification of ADHD from TD controls, has not been completely explored ( dos Santos Siqueira et al, 2014 ; Miao et al, 2019 ; Shao et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach

Rezaei

Zare²,

Hakimdavoodi³

et al. 2022

Front. Hum. Neurosci.

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Background and objectivesThe study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder (ADHD) has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development (TD) children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI (rs-fMRI).Materials and methodsWe investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty six drug-naive ADHD children (average age 11.86 ± 2.21 years; 49 male) and 56 age matched TD controls (average age 11.51 ± 1.77 years, 44 male) were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.ResultsAn accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.ConclusionPattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls.

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Section: Resting-state Networkcontrasting

confidence: 90%

Section: Resting-state Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach

Rezaei

Zare²,

Hakimdavoodi³

et al. 2022

Front. Hum. Neurosci.

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“…Our study achieved an AUC of 0.7408, considering only resting-state fMRI, superior to the studies above. In addition, our results are better than most studies with similar sample sizes, only inferior to these two [ 61 , 62 ] (detailed in Table 5 ).…”

Section: Discussioncontrasting

confidence: 59%

Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study

et al. 2023

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Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients’ interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson’s correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.

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“…References [28]- [30] classified ADHD based on different graph-based measures. Also, ADHD has been classified by measuring the brain's functional connectivity using different correlation-based methods, including Pearson's correlation, and partial correlation [31]- [33]. Other studies applied different deep neural network on whole-brain connectivity for ADHD classification [27], [35].…”

Section: Attention-deficit/hyperactive Disordermentioning

confidence: 99%

Stability Analysis of fMRI BOLD Signals for Disease Diagnosis

Ingabire

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Previous studies have demonstrated that the stability changes in physiological signals can reflect individuals' pathological conditions. Apart from this, according to system science theory, a large-scale system can generally be divided into many subsystems whose stability level govern its overall performance. Therefore, this study attempts to investigate the possibility of analyzing the stability of decomposed subsystems of restingstate fMRI (rs-fMRI) BOLD signals in order to assess the overall characteristic of the human brain and individuals' health conditions. We used attention deficit/hyperactive disorder (ADHD) as an example to illustrate our method. Rs-fMRI BOLD signals were first decomposed into dynamic modes (DMs) which can illuminate the patterns of brain subsystems. Each DM is associated with one eigenvalue that determines its stability as well as oscillation frequency. Accordingly, we divided the DMs within common BOLD frequency bands into stable and unstable DMs. Then, the features related to the stability of those DMs were extracted, and nine common classifiers were used to differentiate healthy controls from ADHD patients taken from ADHD-200, a well-known dataset. The results showed that almost Manuscript

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Classification of ADHD with fMRI data and multi-objective optimization

Cited by 20 publications

References 29 publications

Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach

Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach

Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study

Stability Analysis of fMRI BOLD Signals for Disease Diagnosis

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