Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs)

Jiang, Xi; Yan, Jiadong; Zhao, Yu; Jiang, Mingxin; Chen, Yuzhong; Zhou, Jingchao; Xiao, Zhenxiang; Wang, Zifan; Zhang, Rong; Becker, Benjamin; Zhu, Dajiang; Kendrick, Keith M.; Liu, Tianming

doi:10.1016/j.neunet.2022.11.004

Cited by 19 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of brain monitoring, CNNs can be used to analyze neuroimaging data such as MRI and CT scan results as well as physiological data such as EEG and MEG. CNNs can automatically identify brain structures and activity patterns to help doctors diagnose and treat them [42][43][44][45]. In terms of brain regulation, CNNs can be used in brain-computer interface technology, which is a technology that converts electrical brain signals into machine commands [46].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Deep Learning Aided Neuroimaging and Brain Regulation

Ouyang²,

Yuan³

2023

Sensors

View full text Add to dashboard Cite

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.

show abstract

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Deep Learning Aided Neuroimaging and Brain Regulation

Ouyang²,

Yuan³

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The core principle underlying Convolutional Neural Networks (CNN) lies in their ability to extract salient features from input data through convolutional operations [66][67][68][69]. These extracted features are then further refined through pooling operations, resulting in a reduction in the feature map size.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging

Zhao,

Guo,

Zhang

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep learning (DL) is a subfield of machine learning (ML) and artificial intelligence (AI) that focuses on developing algorithms and models inspired by the structure and function of the human brain’s neural networks. DL has been applied to various aspects of PET/MR imaging in AD, such as image segmentation, image reconstruction, diagnosis and prediction, and visualization of pathological features. In this review, we introduce the basic concepts and types of DL algorithms, such as feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. We then summarize the current applications and challenges of DL in PET/MR imaging in AD, and discuss the future directions and opportunities for automated diagnosis, predictions of models, and personalized medicine. We conclude that DL has great potential to improve the quality and efficiency of PET/MR imaging in AD, and to provide new insights into the pathophysiology and treatment of this devastating disease.

show abstract

“…Among these, fMRI technology has gained widespread usage in research due to its high resolution and non-invasive nature, both in task-based and resting-state scenarios [ 14 , 15 ]. Based on fMRI technology, there are models that directly utilize MRI data for deep learning construction [ 16 ] and others that employ various preprocessing tools to extract features for subsequent analysis. Functional connectivity (FC) is a commonly used analytical tool.…”

Section: Introductionmentioning

confidence: 99%

A hybrid CNN-SVM model for enhanced autism diagnosis

Qiu,

Zhai

2024

PLoS ONE

View full text Add to dashboard Cite

Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual’s behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to be invaluable in both therapeutic interventions and the timely provision of medical support. Currently, most scholars’ research primarily relies on neuroimaging techniques for auxiliary diagnosis and does not take into account the distinctive features of autism’s social impediments. In order to address this deficiency, this paper introduces a novel convolutional neural network-support vector machine model that integrates resting state functional magnetic resonance imaging data with the social responsiveness scale metrics for the diagnostic assessment of autism. We selected 821 subjects containing the social responsiveness scale measure from the publicly available Autism Brain Imaging Data Exchange dataset, including 379 subjects with autism spectrum disorder and 442 typical controls. After preprocessing of fMRI data, we compute the static and dynamic functional connectivity for each subject. Subsequently, convolutional neural networks and attention mechanisms are utilized to extracts their respective features. The extracted features, combined with the social responsiveness scale features, are then employed as novel inputs for the support vector machine to categorize autistic patients and typical controls. The proposed model identifies salient features within the static and dynamic functional connectivity, offering a possible biological foundation for clinical diagnosis. By incorporating the behavioral assessments, the model achieves a remarkable classification accuracy of 94.30%, providing a more reliable support for auxiliary diagnosis.

show abstract

Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs)

Cited by 19 publications

References 30 publications

Deep Learning Aided Neuroimaging and Brain Regulation

Deep Learning Aided Neuroimaging and Brain Regulation

Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging

A hybrid CNN-SVM model for enhanced autism diagnosis

Contact Info

Product

Resources

About