Modeling regional dynamics in low-frequency fluctuation and its application to Autism spectrum disorder diagnosis

Jun, Eunji; Kang, Eunsong; Choi, Jaehun; Suk, Heung-Il

doi:10.1016/j.neuroimage.2018.09.043

Cited by 19 publications

(8 citation statements)

References 79 publications

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“…All implemented models were trained and tested on the augmented datasets. In addition, we compared published results for the same ABIDE datasets and AAL atlas, including another time-series modeling approach using hidden markov models (HMM) [11] and another neural network approach based on stacked autoencoders and deep transfer learning (DTL) [13].…”

Section: Experimental Methodsmentioning

confidence: 99%

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

Dvornek

Zhuang

et al. 2019

Lecture Notes in Computer Science

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Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imaging (fMRI) data. However, gathering large fMRI datasets for learning is a difficult task. Furthermore, network interpretability is unclear. To address these issues, we utilize multitask learning and design a novel RNN-based model that learns to discriminate between classes while simultaneously learning to generate the fMRI time-series data. Employing the long short-term memory (LSTM) structure, we develop a discriminative model based on the hidden state and a generative model based on the cell state. The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task. We apply our approach to the classification of subjects with autism vs. healthy controls using several datasets from the Autism Brain Imaging Data Exchange. Experiments show that our jointly discriminative and generative model improves classification learning while also producing robust and meaningful functional communities for better model understanding.

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Section: Experimental Methodsmentioning

confidence: 99%

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

Dvornek

Zhuang

et al. 2019

Lecture Notes in Computer Science

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“…This coarse-to-fine flow matching scheme computes the flow at a coarse level of an image volume, while gradually propagating and refining the flow from coarse to fine. More formally, assume that the fixed image I is to be downsampled h times, resulting in h + 1 images, I (0) , I (1) , . .…”

Section: Plos Onementioning

confidence: 99%

“…Accurate parcellation of neural regions in human brains plays an important role in brain disorder diagnosis [1], progression assessment [2], surgical planning [3] and large-scale neuroimaging studies [4,5]. For instance, the hippocampus, as a distinctive neural structure situated in the medial temporal lobe under the cerebral cortex, is crucial to memory, navigation and learning [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Integrated 3d flow-based multi-atlas brain structure segmentation

Qiu

Fan

et al. 2022

PLoS ONE

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MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method’s potential for general applicability in various brain structures and settings.

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“…Over the years, the development of functional magnetic resonance imaging (fMRI), which measures intrinsic neural activity based on blood oxygen level-dependent (BOLD) signals, has provided a versatile tool for investigating functional mechanisms underlying cognitive dysfunction. Compared to other imaging methods, fMRI has advantages in that is non-invasive and has relatively good temporal and spatial resolution [8]. The brain does not need to perform cognitive tasks during collection of resting state fMRI (rs-fMRI) data, so this type of imaging can reflect the functional organization of the brain [9] and the hemodynamic changes caused by disease [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…One of the effective methods is the Hidden Markov Model (HMM), which can describe the dynamic state switching process of the brain as a Markov chain with different transition probabilities between states [30], [31]. Due to the many excellent characteristics of HMM, it has been applied to the study of a variety of clinical diseases, among which the representative diseases include cancer [32] Alzheimer disease (AD), Huntington disease, Parkinson disease [29] and ASD [8]. Therefore, we consider the HMM to be a good model for measuring the dynamic changes in the brain time scale with good generalization ability.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Brain Regions in Two-Group Cross-Location Dynamics Model of Autism

et al. 2020

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Resting-state fMRI studies have suggested that autism spectrum disorder (ASD) is associated with aberrant dynamic changes. However, existing research either has difficulty showing the brain's dynamic characteristics or cannot obtain stable results. We examined the 'two-group cross-location hidden Markov model' of each region of interest (ROI) to identify possible pathogenic features of ASD. Specifically, we selected resting-state fMRI data with complete scales and good quality from Autism Brain Imaging Data Exchange (ABIDEI). Eligible data included 145 ASD and 157 control (CON). Two groups of subjects were separated to train Hidden Markov models representing respective populations. Then, we used each model to estimate the likelihood values of all participants. Using the likelihood value as features, we tested the significant differences of 200 ROIs and finally identified ROIs with common significant differences in the two types of models. Additionally, we investigated the relationship between likelihood values of significantly different ROIs and clinical scales. some ROIs were negatively correlated with the Autism Diagnostic Observation Schedule and positively correlated with full IQ. Finally, we constructed a support vector machine to classify ASD and CON. Overall, our findings suggested that the abnormal areas in the frontopolar area, orbitofrontal area, inferior temporal gyrus, middle temporal gyrus and fusiform gyrus are prominent features of ASD and are closely related to clinical functional decline. The average accuracy rate reached 74.9% after ten cross-validations. This 'two-group cross-localized Hidden Markov Model' provides a robust and powerful framework for understanding the dysfunctional brain architecture of ASD. INDEX TERMS Autism spectrum disorder, resting-state fMRI, hidden Markov model, computer-assisted diagnosis.

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Modeling regional dynamics in low-frequency fluctuation and its application to Autism spectrum disorder diagnosis

Cited by 19 publications

References 79 publications

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

Integrated 3d flow-based multi-atlas brain structure segmentation

Abnormal Brain Regions in Two-Group Cross-Location Dynamics Model of Autism

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