Brain microstructural alterations in the left precuneus mediate the association between KIBRA polymorphism and working memory in healthy adults: a diffusion kurtosis imaging study

Wang, Junxia; Wu, Sichu; Sun, Yi; Lu, Jiaming; Zhang, Jilei; Fang, Yu; Qing, Zhao; Liang, Xue; Zhang, Wen; Chen, Qian; Zhang, Xin; Zhang, B

doi:10.1007/s11682-022-00703-z

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…In recent years, there has been significant progress in the development of multimodal neuroimaging techniques for studying brain diseases, including ESRD-related neurological complications. These techniques include functional magnetic resonance imaging (fMRI) [ 6 ], structural magnetic resonance imaging [ 7 ], magnetic resonance spectroscopy [ 8 ], fluorodeoxyglucose positron emission tomography [ 9 ], single photon emission tomography [ 10 ], arterial spin labeling [ 11 ], diffusion tensor imaging [ 12 ], and diffusion kurtosis imaging (DKI) [ 13 ]. DKI has gained rapid development and has provided valuable insights into the pathophysiological mechanisms of brain diseases from various perspectives such as deposition of pathological markers, brain structural connectivity, and functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

Chen,

Liu,

Shi

2024

PLoS ONE

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The function and structure of brain networks (BN) may undergo changes in patients with end-stage renal disease (ESRD), particularly in those accompanied by mild cognitive impairment (ESRDaMCI). Many existing methods for fusing BN focus on extracting interaction features between pairs of network nodes from each mode and combining them. This approach overlooks the correlation between different modal features during feature extraction and the potentially valuable information that may exist between more than two brain regions. To address this issue, we propose a model using a multi-head self-attention mechanism to fuse brain functional networks, white matter structural networks, and gray matter structural networks, which results in the construction of brain fusion networks (FBN). Initially, three networks are constructed: the brain function network, the white matter structure network, and the individual-based gray matter structure network. The multi-head self-attention mechanism is then applied to fuse the three types of networks, generating attention weights that are transformed into an optimized model. The optimized model introduces hypergraph popular regular term and L1 norm regular term, leading to the formation of FBN. Finally, FBN is employed in the diagnosis and prediction of ESRDaMCI to evaluate its classification performance and investigate the correlation between discriminative brain regions and cognitive dysfunction. Experimental results demonstrate that the optimal classification accuracy achieved is 92.80%, which is at least 3.63% higher than the accuracy attained using other methods. This outcome confirms the effectiveness of our proposed method. Additionally, the identification of brain regions significantly associated with scores on the Montreal cognitive assessment scale may shed light on the underlying pathogenesis of ESRDaMCI.

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Section: Introductionmentioning

confidence: 99%

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

Chen,

Liu,

Shi

2024

PLoS ONE

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HCTMFS: A multi-modal feature selection framework with higher-order correlated topological manifold for ESRDaMCI

Song,

Liu,

Shi

et al. 2024

Computer Methods and Programs in Biomedicine

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Hypergraph representation of multimodal brain networks for patients with end-stage renal disease associated with mild cognitive impairment

Liu

Shi

et al. 2023

MBE

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<abstract><p>The structure and function of brain networks (BN) may be altered in patients with end-stage renal disease (ESRD). However, there are relatively few attentions on ESRD associated with mild cognitive impairment (ESRDaMCI). Most studies focus on the pairwise relationships between brain regions, without taking into account the complementary information of functional connectivity (FC) and structural connectivity (SC). To address the problem, a hypergraph representation method is proposed to construct a multimodal BN for ESRDaMCI. First, the activity of nodes is determined by connection features extracted from functional magnetic resonance imaging (fMRI) (i.e., FC), and the presence of edges is determined by physical connections of nerve fibers extracted from diffusion kurtosis imaging (DKI) (i.e., SC). Then, the connection features are generated through bilinear pooling and transformed into an optimization model. Next, a hypergraph is constructed according to the generated node representation and connection features, and the node degree and edge degree of the hypergraph are calculated to obtain the hypergraph manifold regularization (HMR) term. The HMR and <bold><italic>L</italic></bold><sub>1</sub> norm regularization terms are introduced into the optimization model to achieve the final hypergraph representation of multimodal BN (HRMBN). Experimental results show that the classification performance of HRMBN is significantly better than that of several state-of-the-art multimodal BN construction methods. Its best classification accuracy is 91.0891%, at least 4.3452% higher than that of other methods, verifying the effectiveness of our method. The HRMBN not only achieves better results in ESRDaMCI classification, but also identifies the discriminative brain regions of ESRDaMCI, which provides a reference for the auxiliary diagnosis of ESRD.</p></abstract>

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Brain microstructural alterations in the left precuneus mediate the association between KIBRA polymorphism and working memory in healthy adults: a diffusion kurtosis imaging study

Cited by 4 publications

References 36 publications

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

HCTMFS: A multi-modal feature selection framework with higher-order correlated topological manifold for ESRDaMCI

Hypergraph representation of multimodal brain networks for patients with end-stage renal disease associated with mild cognitive impairment

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