Unified and Contrasting Graphical Lasso for Brain Network Discovery

Kong, Xiangnan; Ragin, Ann B.

doi:10.1137/1.9781611974973.21

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Brain extraction (a.k.a. skull stripping), registration and segmentation serve as preliminary yet indispensable steps in many neuroimaging studies, such as anatomical and functional analysis [4,33,50,52], brain networks discovering [11,24,27,28,53,54], multi-modality fusion [6,26], diagnostic assistance [18,46], and brain region studies [8,25]. The brain extraction targets the removal of non-cerebral tissues (e.g., skull, dura, and scalp) from a patient's head scan; the registration step aims to align the extracted brain with a standard brain template; the segmentation step intends to label the anatomical brain regions in the raw imaging scan.…”

Section: Introductionmentioning

confidence: 99%

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

Qian

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies. The aim is to extract the brain from raw imaging scans (i.e., extraction step), align it with a target brain image (i.e., registration step) and label the anatomical brain regions (i.e., segmentation step). Conventional studies typically focus on developing separate methods for the extraction, registration and segmentation tasks in a supervised setting. The performance of these methods is largely contingent on the quantity of training samples and the extent of visual inspections carried out by experts for error correction. Nevertheless, collecting voxel-level labels and performing manual quality control on high-dimensional neuroimages (e.g., 3D MRI) are expensive and time-consuming in many medical studies. In this paper, we study the problem of one-shot joint extraction, registration and segmentation in neuroimaging data, which exploits only one labeled template image (a.k.a. atlas) and a few unlabeled raw images for training. We propose a unified end-to-end framework, called JERS, to jointly optimize the extraction, registration and segmentation tasks, allowing feedback among them. Specifically, we use a group of extraction, registration and segmentation modules to learn the extraction mask, transformation and segmentation mask, where modules are interconnected and mutually reinforced by selfsupervision. Empirical results on real-world datasets demonstrate that our proposed method performs exceptionally in the extraction, registration and segmentation tasks.

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Section: Introductionmentioning

confidence: 99%

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

Qian

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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“…Recent advances in network analysis have allowed new insights for our understanding of complex networks and interactions (i.e., connectivity patterns) within each network. A human brain can be modeled as a complex network, containing a number of structurally/functionally interconnected brain regions (Power et al 2010;Liu, Kong, and Ragin 2017). Generally, a brain network (a.k.a., connectome) can be characterized by a set of nodes and edges, where nodes represent regions-of-interest (ROIs) in the brain and edges denote the connectivity strength or correlation between brain regions.…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification

Wang

Huang

Liu

et al. 2019

AAAI

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Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain.

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“…Electroencephalographic (EEG) recording of neural activity has been used widely for sleep monitoring and diagnosis of sleep disorders [1] [2] [3] [4]. EEG activity in the α band (8)(9)(10)(11)(12) Hz) has long been associated with wakefulness with power attenuating and becoming sporadic during the Sleep Onset Process (SOP) [2] [5], then disappearing at the onset of sleep. Bursts of α activity have also been observed during and around [1] Rapid Eye Movement (REM) sleep.…”

Section: Introductionmentioning

confidence: 99%

“…Varoquaux et al proposed a population prior to address the variability among subjects when estimating the brain covariance matrix during spontaneous brain activity [9]. To identify the brain network alterations among HIV patients and healthy controls, Liu et al constructed a contrasting framework and observed major differences in the occipital lobe and parietal lobe [10]. To discover the default mode alterations in schizophrenia, Lefort-Besnard et al employed sparse inverse covariance estimation method and identified a statistically significant difference between healthy controls and patients in the default mode network (DMN), saliency network (SN), and dorsal attention network (DAN).…”

Section: Introductionmentioning

confidence: 99%

Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space

Liu

Stephen

Prerau

et al. 2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-oscillations, we show that the underlying networks are distinct.

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Unified and Contrasting Graphical Lasso for Brain Network Discovery

Cited by 6 publications

References 12 publications

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification

Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space

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