Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders

Zhao, Jing; Huang, Jinjie; Zhi, Dongmei; Yan, Weizheng; Ma, Xiaoyan; Yang, Xiao; Li, Xianbin; Ke, Qing; Jiang, Tianzi; Calhoun, Vince D.; Sui, Jing

doi:10.1016/j.jneumeth.2020.108756

Cited by 42 publications

(32 citation statements)

References 54 publications

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“…For instance, the discriminative and generative components were incorporated in LSTM to form a multitask learning approach for fMRI-based classification and achieved an improved diagnostic accuracy of ASD compared with the standard LSTM model [150]. By integrating GAN with group ICA, a functional network connectivity-based deep learning model was developed for the diagnosis of major depressive disorder and schizophrenia [151]. The generator with fake connectivity was trained to match the discriminator with real connectivity in the intermediate layers while simultaneously a new objective loss was determined for the generator to improve the diagnosis accuracy.…”

Section: Deep Learningmentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

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In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

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Section: Deep Learningmentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

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“…The level of clustering agreement and the individual level of classification agreement both reached 92.5%. Zhao et al [110] proposed a GAN based on functional network connectivity (FNC). The discriminator and generator of the proposed GAN model both have four fully-connected layers.…”

Section: Major Depressive Disordermentioning

confidence: 99%

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Zhang

et al. 2021

Diagnostics

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Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.

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“…Many efforts have been made to build functional connectivity-based predictive models for identifying network-based biomarkers of depression Craddock et al (2009), Zeng et al (2012), Bhaumik et al (2017), Rosa et al (2015), Zhao et al (2020a). Majority of studies are based on multivariate pattern analysis of functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study

Sahoo¹,

Antoniades²,

Fu³

et al. 2022

Preprint

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Many supervised machine learning frameworks have been proposed for disease classification using functional magnetic resonance imaging (fMRI) data, producing important biomarkers. More recently, data pooling has flourished, making the result generalizable across a large population. But, this success depends on the population diversity and variability introduced due to the pooling of the data that is not a primary research interest. Here, we look at hierarchical Sparse Connectivity Patterns (hSCPs) as biomarkers for major depressive disorder (MDD). We propose a novel model based on hSCPs to predict MDD patients from functional connectivity matrices extracted from resting-state fMRI data. Our model consists of three coupled terms. The first term decomposes connectivity matrices into hierarchical low-rank sparse components corresponding to synchronous patterns across the human brain. These components are then combined via patient-specific weights capturing heterogeneity in the data. The second term is a classification loss that uses the patient-specific weights to classify MDD patients from healthy ones. Both of these terms are combined with the third term, a robustness loss function to improve the reproducibility of hSCPs. This reduces the variability introduced due to site and population diversity (age and sex) on the predictive accuracy and pattern stability in a large dataset pooled from five different sites. Our results show the impact of diversity on prediction performance. Our model can reduce diversity and improve the predictive and generalizing capability of the components. Finally, our results show that our proposed model can robustly identify clinically relevant patterns characteristic of MDD with high reproducibility.

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Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders

Cited by 42 publications

References 54 publications

Modern Views of Machine Learning for Precision Psychiatry

Modern Views of Machine Learning for Precision Psychiatry

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study

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