Changes in the connection network of whole-brain fiber tracts in patients with Alzheimer’s disease have a tendency of lateralization

He, Fangmei; Li, Youjun; Li, Chenxi; Zhao, Jie; Liu, Tian; Fan, Liming; Zhang, Xi; Wang, Jue

doi:10.1097/wnr.0000000000001708

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Hemispheric lateralization is a widely accepted organizational principle of the human brain. Brain lateralization was associated with neurodevelopment [ 93 , 94 ], aging [ 95 , 96 ], cognition [ 97 , 98 ], emotion [ 99 , 100 ], and neuropsychiatric disorders, such as schizophrenia [ 101 , 102 ], depression [ 103 , 104 ], dementia [ 105 ], Alzheimer’s disease [ 96 , 106 ] and PD [ 107 , 108 ]. Heldmann et al [ 109 ] found that white matter microstructural abnormalities were only detected in the left hemisphere in the PD risk group.…”

Section: Discussionmentioning

confidence: 99%

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson’s disease

Shi¹,

Ren²,

Zhang³

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson’s disease

Shi¹,

Ren²,

Zhang³

et al. 2023

Heliyon

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“…Due to the influence of AD, some connections of neurons would be damaged, resulting in information transmission barriers and corresponding symptoms [30]. Therefore, the network-based classification research can be categorized into classification by extracting DTI parameters from fiber bundles [31][32] and classification by analyzing brain networks [33][34][35][36].…”

Section: Relevant Researchmentioning

confidence: 99%

Classification Study of Alzheimer’s Disease Based on Self-Attention Mechanism and DTI Imaging Using GCN

Sang,

2024

IEEE Access

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Alzheimer's disease (AD) is a neurodegenerative disorder. Diffusion tensor imaging (DTI) provides information about the integrity of white matter fiber bundles that are related to the neuropathological mechanisms, and it is one of the commonly used techniques in AD research. In this study, we first divided each subject's brain into 90 regions based on the automated anatomical labeling (AAL) brain atlas. The average fractional anisotropy (FA) values between each pair of regions were applied to construct a brain network. We utilized the number of voxels with fibers passing through each brain region as the node features. The brain networks and node features were input into a novel graph convolutional neural network (GCN) structure involving the self-attention pooling mechanism proposed in this study to classify AD and normal controls (NC). The classification performance was compared among different preprocessed brain networks and node features. The final classification result achieved an accuracy of 87.5%. INDEX TERMSAlzheimer's disease; Diffusion tensor imaging; Image classification; Brain networks; Graph convolutional neural networks.

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Lateralized brunt of sleep deprivation on white matter injury in a rat model of Alzheimer’s disease

Mao,

Han,

Guo

et al. 2023

GeroScience

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Changes in the connection network of whole-brain fiber tracts in patients with Alzheimer’s disease have a tendency of lateralization

Abstract: Effect size was obtained by Cramer's Phi.

Cited by 5 publications

References 18 publications

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson’s disease

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson’s disease

Classification Study of Alzheimer’s Disease Based on Self-Attention Mechanism and DTI Imaging Using GCN

Lateralized brunt of sleep deprivation on white matter injury in a rat model of Alzheimer’s disease

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