Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study

Qiao, Chen; Lan, Yang; Calhoun, Vince D.; Xu, Zong Ben; Wang, Yu Ping

doi:10.1016/j.neunet.2020.12.007

Cited by 10 publications

(10 citation statements)

References 62 publications

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“…For reconstruction of FCs, some peer deep linear models, Deep Sparse Dictionary Learning (Deep SDL) [33], Deep Non-negative Matrix Factorization (Deep NMF) [31,32], and a compositional approach to develop stacked multilayer Independent Component Analysis [34], has been proposed recently. These related deep linear models will be included in methodological validations.…”

Section: Related Work and Methodological Validationmentioning

confidence: 99%

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

Zhang

Palacios

Mukherjee

2020

Preprint

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The human brain exhibits hierarchical modular organization, which is not depicted by conventional fMRI functional connectivity reconstruction methods such as independent component analysis (ICA). To map hierarchical brain connectivity networks (BCNs), we propose a novel class of deep (multilayer) linear models that are constructed such that each successive layer decomposes the features of the preceding layer. Three of these are multilayer variants of Sparse Dictionary Learning (SDL), Non-Negative Matrix Factorization (NMF) and Fast ICA (FICA). We present a fourth deep linear model, Deep Matrix Fitting (MF), which incorporates both rank reduction for data-driven hyperparameter determination as well as a distributed optimization function. We also introduce a novel framework for theoretical comparison of these deep linear models based on their combination of mathematical operators, the predictions of which are tested using simulated resting state fMRI data with known ground truth BCNs. Consistent with the theoretical predictions, Deep MF and Deep SDL performed best for connectivity estimation of 1st layer networks, whereas Deep FICA and Deep NMF were modestly better for spatial mapping. Deep MF provided the best overall performance, including computational speed. These deep linear models can efficiently map hierarchical BCNs without requiring the manual hyperparameter tuning, extensive fMRI training data or high-performance computing infrastructure needed by deep nonlinear models, such as convolutional neural networks (CNNs) or deep belief networks (DBNs), and their results are also more explainable from their mathematical structure. These benefits gain in importance as continual improvements in the spatial and temporal resolution of fMRI reveal more of the hierarchy of spatiotemporal brain architecture. These new models of hierarchical BCNs may also advance the development of fMRI diagnostic and prognostic biomarkers, given the recent recognition of disparities between low-level vs high-level network connectivity across a wide range of neurological and psychiatric disorders.

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Section: Related Work and Methodological Validationmentioning

confidence: 99%

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

Zhang

Palacios

Mukherjee

2020

Preprint

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“…To learn the structured network of each RBM, which helps to improve the interpretability of the network, sparse regularization can be utilized [7][8][9][11][12][13]. Thus, some sparse regularization terms are introduced here obtain the sparsity of connections as well as the response of the neurons.…”

Section: Unsupervised Sparse Learning Of Dbnmentioning

confidence: 99%

“…They use the -norm or -norm of the connection weights as penalty functions, which are embedded in the original training process. These methods aim to achieve the sparse network topology by reducing the value of connective weights [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Feature Back-Tracking with Sparse Deep Belief Networks

Qiao

Zhang

et al. 2021

Frontiers in Artificial Intelligence and Applications

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To find a way of the interpretability of deep learning, in this paper, a features back-tracking (FBT) approach based on a sparse deep learning architecture is proposed. Firstly, for a deep belief network (DBN), both the Kullback-Leibler divergence of the hidden neurons and the L1 norm penalty on the connection weights are introduced. Thus, the sparse response mechanism as well as the sparse connection of the brain neurons can be simulated directly. That means the DBN can learn a sparse framework and an effective sparse data representation. On this basis, the feature back-tracking technique is put forward. For both the single nucleotide polymorphisms (SNPs) data and MNIST data, FBT has quite well performance on searching for the risk loci on the genes as well as the important sites of the digit data. It reveals that the proposed FBT method can pick out the essential features by deep learning architecture with quite high classification accuracy and data storage ability. Utilizing the sparse layer-wise feature learning to achieve key features from the original data, is an effective attempt to explore the profound mechanism of human brain and interpretability of deep learning.

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“…N ORMAL brain development is a complex process, from the establishment of basic cognitive functions in childhood to the gradual maturity of more complex self-regulatory functions throughout adolescence [1]- [3]. Functional magnetic resonance imaging (fMRI) can capture hemodynamic responses to neuronal activities by measuring the blood oxygenation level-dependent (BOLD) signal, based on which the changes in neural interaction and integration between functionally interconnected regions with development can be revealed [4]. Compared with BOLD signals, dynamic functional connectivity (dFC) measured by a sliding window approach can reflect time-varying dependencies between spatially separated brain regions.…”

mentioning

confidence: 99%

“…These studies aim to divide the whole-brain dFC profiles into distinct states observed reliably across subjects throughout the fMRI scans [5]- [8]. It enables us to investigate the differences of states related to brain development, capture the transition mechanism among these states, and provide insights into neural brain dynamics from the perspective of functional connectivity [4], [5].…”

mentioning

confidence: 99%

Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms

Yang

Qiao

Zhou

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: Multimodal-based methods show great potential for neuroscience studies by integrating complementary information. There has been less multimodal work focussed on brain developmental changes. Methods: We propose an explainable multimodal deep dictionary learning method to uncover both the commonality and specificity of different modalities, which learns the shared dictionary and the modality-specific sparse representations based on the multimodal data and their encodings of a sparse deep autoencoder. Results: By regarding three fMRI paradigms collected during two tasks and resting state as modalities, we apply the proposed method on multimodal data to identify the brain developmental differences. We found that both children and young adults prefer to switch among states during two tasks while staying within a particular state during rest, but the difference is that children possess more diffuse functional connectivity patterns while young adults have more focused functional connectivity patterns. Conclusion and Significance: To uncover the commonality and specificity of three fMRI paradigms to developmental differences, multimodal data and their encodings are used to train the shared dictionary and the modality-specific sparse representations. Identifying brain network differences helps to understand how the neural circuits and brain networks form and develop with age.

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Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study

Cited by 10 publications

References 62 publications

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

Feature Back-Tracking with Sparse Deep Belief Networks

Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms

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