Identifying brain functional connectivity alterations during different stages of Alzheimer’s disease

Ahmadi, Hessam; Fatemizadeh, Emad; Nasrabadi, Ali Motie

doi:10.1080/00207454.2020.1860037

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…The communication and information transmission efficiency of the PHG.R was decreased. The cingulate region is the first to be affected by AD [ 52 ]. ACG in the limbic system has subregional differences in many previous neuropsychiatric and neurodegenerative diseases brain network studies [ 53 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

Local Brain Network Alterations and Olfactory Impairment in Alzheimer’s Disease: An fMRI and Graph-Based Study

Zhu

Yang

et al. 2023

Brain Sciences

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Alzheimer’s disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.

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Section: Discussionmentioning

confidence: 99%

Local Brain Network Alterations and Olfactory Impairment in Alzheimer’s Disease: An fMRI and Graph-Based Study

Zhu

Yang

et al. 2023

Brain Sciences

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“…Connectivity in brain networks is typically characterized via statistical dependence (such as correlations) between blood oxygen level dependent (BOLD) fMRI signals over spatially-distinct brain regions, called the functional connectivity (FC). Alterations in resting-state FC networks have been associated with neuropsychiatric or neurodevelopmental disorders (Filippi et al, 2019), such as autism spectrum disorder (ASD) (Holiga et al, 2019;Valenti et al, 2020;Wang et al, 2021), schizophrenia (Nyatega et al, 2021;Liu et al, 2019), major depression (Zhu et al, 2020;Wang et al, 2020;Chen et al, 2021), and Alzheimer's disease (Schumacher et al, 2019;Ahmadi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

Loo¹,

Yap²,

Noman³

et al. 2023

Preprint

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Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time. In this paper, a deep probabilistic spatiotemporal framework developed based on variational Bayes (DSVB) is proposed to learn time-varying topological structures in dynamic brain FC networks for autism spectrum disorder (ASD) identification. The proposed framework incorporates a spatial-aware recurrent neural network to capture rich spatiotemporal patterns across dynamic FC networks, followed by a fully-connected neural network to exploit these learned patterns for subject-level classification. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework significantly outperformed state-of-the-art methods in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.

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Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis

Tang,

Zhang,

Liu

et al. 2023

Lecture Notes in Computer Science

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Identifying brain functional connectivity alterations during different stages of Alzheimer’s disease

Cited by 8 publications

References 35 publications

Local Brain Network Alterations and Olfactory Impairment in Alzheimer’s Disease: An fMRI and Graph-Based Study

Local Brain Network Alterations and Olfactory Impairment in Alzheimer’s Disease: An fMRI and Graph-Based Study

A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis

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