DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder

Abbas, S. Qasim; Chi, Lianhua; Chen, Yi‐Ping Phoebe

doi:10.1016/j.artmed.2022.102475

Cited by 11 publications

(4 citation statements)

References 41 publications

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“…In their study, Abbas et al [ 1 ] implemented a 3D model, which is completely different from ours and strongly more demanding from the computational point of view. The authors stated that they had implemented a subject partitioning criterion in the training, validation and test sets aimed at balancing the contribution of the different sites and diagnostic groups to each set.…”

Section: Discussionmentioning

confidence: 87%

“…However, leveraging DL fusion techniques has consistently shown improvements in diagnostic performance [ 65 , 75 , 78 ]. Some studies reported good results in applying DL models using functional and structural MRI images demonstrating that a DL framework for multi-modality data fusion outperforms single-modality DL [ 1 , 5 , 67 ]. In this work, we developed a multi-modal joint fusion DL model, which combines structural and functional MRI to distinguish between ASD and TD subjects.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Saponaro,

Lizzi,

Serra

et al. 2024

Brain Inf.

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Background: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD). Material and methods We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework. Results The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain. Conclusions Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Saponaro,

Lizzi,

Serra

et al. 2024

Brain Inf.

View full text Add to dashboard Cite

show abstract

“…For the sake of comparison with the state-of-the-art, we used ABIDE I, since its pre-processed version was vastly used in many recent works all concerned with early ASD detection [ 9 , 30 ]. Moreover, we have also used this same database with other strategies in previous works to detect autism [ 9 , 10 ].…”

Section: Discussionmentioning

confidence: 99%

A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection

2023

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Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%.

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“…However, leveraging deep learning fusion techniques has consistently shown improvements in diagnostic performance [61,68,71]. Some studies have reported good result in applying deep learning models using functional and structural MRI images demonstrating that a DL framework for multi-modality data fusion outperforms single-modality DL [1,4,63]. In this work, we developed a multi-modal joint fusion DL model, which combines structural and functional MRI to distinguish between ASD and TD subjects.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Joint Fusion approach to exploit anatomical and functional brain information in Autism Spectrum Disorders

Saponaro,

Lizzi,

Serra

et al. 2023

Preprint

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Background: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD). Material and Methods: We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted mor-phometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-DNN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework. Results: The AUC values of 0.66 ± 0.05 and of 0.76 ± 0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain. Conclusions: Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

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DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder

Cited by 11 publications

References 41 publications

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection

Deep Learning based Joint Fusion approach to exploit anatomical and functional brain information in Autism Spectrum Disorders

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