Analysis of under-connectivity in Autism using the minimum spanning tree: application on large multi-site dataset

Benabdallah, Fatima Zahra; Maliani, Ahmed Driss El; Lotfi, Dounia; Jennane, Rachid

doi:10.1109/isivc.2018.8709213

Cited by 7 publications

(12 citation statements)

References 15 publications

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“…However, an important result shows up, which is the decrease of sensitivity in all datasets no matter the value of accuracy. This same result was also found in [27] with different criterions sub-datasets. Which means that the eliminated connections contain information about the autistic brain connectivity deficit.…”

Section: Resultssupporting

confidence: 86%

“…[21]: This method uses a leave-one-site-out cross-validation on matrices of the Multi-subject Dictionary Learning (MSDL) atlas ROIs [47]. -IM (Initial Matrices) [27]: This method is comparable to the one used in [21], however, the ROIs used to compute the Initial Matrices are from the AAL atlas. -M-MST (Minus Minimum Spanning Tree) [27]: In this method the MST is removed from the initial matrices (IM method).…”

Section: Resultsmentioning

confidence: 99%

“…-IM (Initial Matrices) [27]: This method is comparable to the one used in [21], however, the ROIs used to compute the Initial Matrices are from the AAL atlas. -M-MST (Minus Minimum Spanning Tree) [27]: In this method the MST is removed from the initial matrices (IM method). The resultant matrices are used for classification.…”

Section: Resultsmentioning

confidence: 99%

“…It is worth mentioning that we already investigated the under-connectivity deficit in [27]. Here, we extend the investigation to long-range (inter-cluster) under-connectivity, which is a more specific property of the autistic brain.…”

mentioning

confidence: 80%

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An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity

et al. 2022

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Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

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Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 80%

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An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity

et al. 2022

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“…Leming et al [ 66 ] trained a convolutional neural network and applied it to ASD recognition, and their experiments showed that deep learning models that distinguish ASD from NC controls focus broadly on temporal and cerebellar connections. However, the problem of small size fMRI data prevented the generalization of the above research works [ 67 ].…”

Section: Related Workmentioning

confidence: 99%

Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

2021

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Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

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Analysis of the Over-Connectivity in Autistic Brains Using the Maximum Spanning Tree: Application on the Multi-Site and Heterogeneous ABIDE Dataset

Benabdallah

Maliani

Lotfi

2020

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)

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Analysis of under-connectivity in Autism using the minimum spanning tree: application on large multi-site dataset

Cited by 7 publications

References 15 publications

An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity

An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity

Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

Analysis of the Over-Connectivity in Autistic Brains Using the Maximum Spanning Tree: Application on the Multi-Site and Heterogeneous ABIDE Dataset

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