A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity

Yang, Xin; Zhang, Ning; Schrader, Paul

doi:10.1016/j.mlwa.2022.100290

Cited by 33 publications

(24 citation statements)

References 64 publications

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“…Among the numerous predefined atlases, Bootstrap Analysis of Stable Clusters (BASC ) was chosen, since it was the map with best performance for distinguishing ASD patients by deep learning model, according to [22]. It was proposed in [47] and generated from group brain parcellation by BASC method, which is a k-means clustering-based algorithm that identifies brain networks with coherent activity in resting-state fMRI [48]. BASC map with a cluster number of 122 ROIs is used here (see figure 1).…”

Section: A Data and Data Preprocessingmentioning

confidence: 99%

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Alves

Toutain

Aguiar

et al. 2022

Preprint

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Autism spectrum disorder is a multifactorial neurodevelopmental disorder with high genetic heterogeneity. Studies of brain networks in autism can provide new insights into the dynamics of information processing in individuals who suffer from such a condition. This paper proposes a method for automatic diagnosis of autism based on fMRI time series and machine learning algorithms. We verify that the left ventral posterior cingulate cortex region reduces the functional connectivity of the brain area in patients with autism spectrum disorder. Also, the brain networks of patients with autism spectrum disorder show more segregation, lower distribution of information, and less connectivity. Our methodology accurately differentiates control and autistic subjects providing an area under the curve close to higher than 95%.

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Section: A Data and Data Preprocessingmentioning

confidence: 99%

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Alves

Toutain

Aguiar

et al. 2022

Preprint

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“…Heinsfeld et al (2018) proposed a deep learning framework combining the whole brain resting‐state FC patterns to identify ASD participants achieving 70% accuracy on the multi‐sties data. Yang et al (2022) used kernel SVM to classify ASD from HC with 69.43% accuracy based on the correlation of the functional atlas BASC444. These previous classification results are not higher than 80%.…”

Section: Discussionmentioning

confidence: 99%

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

et al. 2023

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Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large‐scale FNC as a feature to identify individuals with ASD. This study used a time‐sliding window method to study the dynamic FNC (dFNC) on the resting‐state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10–75 TRs (TR = 2 s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10‐fold cross‐validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD.

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“…Bootstrap Analysis of Stable Clusters (BASC) was chosen from among the several preconfigured atlases since it was the map with the most outstanding performance according to [40, 41]. It was proposed in [42] and obtained via group brain parcellation using the BASC technique, a k-means clustering-based approach that finds brain networks with coherent activity in resting-state fMRI [43]. BASC map with a cluster number of 122 ROIs is used here (see Figure 1-(A)).…”

Section: Methodsmentioning

confidence: 99%

Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

Alves

Toutain

Porto

et al. 2022

Preprint

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Schizophrenia is a severe mental disorder associated with persistent or recurrent psychosis, hallu- cinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization (WHO). Several studies encompass machine learning and deep learning algorithms to automate the diagnosis of this mental disorder. Others study schizophrenia brain networks to get new insights into the dynamics of information processing in patients suffering from the condition. In this paper, we offer a rigorous approach with machine learning and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in schizophrenia patients. For this purpose, we employed an fMRI and EEG dataset in a multimodal fashion. In addition, we combined EEG measures, i.e., Hjorth mobility and complexity, to complex network measurements to be analyzed in our model for the first time in the literature. When comparing the schizophrenia group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. In terms of complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most important measure in a multimodal fashion. Furthermore, the schizophrenia brain networks exhibit less segregation and lower distribution of information. As a final result, EEG measures outperformed complex networks in capturing the brain alterations associated with schizophrenia. As a result, our model achieved an AUC of 100%, an accuracy of 98% for the fMRI, an AUC of 95 %, and an accuracy of 95% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures for these results, which helped us better describe changes in the diseased brain.

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A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity

Cited by 33 publications

References 64 publications

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

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