Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment

Ji, Yu; Wang, Yuan‐Yuan; Cheng, Qi; Fu, Wen-wen; Huang, Shui-qin; Zhong, Pei-pei; Chen, Xiaolin; Shu, Ben-liang; Wei, Bin; Huang, Qin-yi; Wu, Xiaorong

doi:10.3389/fnins.2023.1227081

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…Similarly, Huang et al 6 found a decrease in the ReHo value of L-MFG in patients with RD, suggesting cognitive impairment in these individuals. Our previous study revealed an increase in the dynamic ALFF values of the bilateral MFG and the right inferior frontal gyrus (IFG) in patients with RD 9 , which aligns with our current research. Patients who experience RD face challenges in adapting to monocular vision due to the sudden loss of vision.…”

Section: Discussionsupporting

confidence: 92%

“…Furthermore, there were increased dEC values from the R-MOG to the R-V1, from the R-CAL to the R-V1, from the L-CUN to the R-V1, from the L-PCUN to the L-V1, as well as a decrease in bidirectional dEC values between the L-V1 and the R-PCUN. Kang et al 29 9 . The occipital lobe plays a primary role in perceiving and processing visual information, making it crucial in complex visual perception processes.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous study, we observed a decrease in dynamic ALFF value in the R-SPG among patients with RD. As the SPG plays a role in the transmission and integration of visual information, this nding may contribute to the decline in vision experienced by RD patients 9 . The parietal lobe, positioned adjacent to the occipital lobe, is crucial in the visual pathway by transmitting visual information to the frontal lobe 36,37 .…”

Section: Discussionmentioning

confidence: 99%

“…In our previous study, we observed an increase in the dynamic ALFF value of the R-MTG in patients with RD. This may potentially indicate a compensatory mechanism for the reduced language comprehension ability 9 . Language and cognition are closely intertwined and play a fundamental role in human communication and thought processes.…”

Section: Discussionmentioning

confidence: 99%

“…However, these methods can only identify local eye lesions and are unable to detect abnormalities in the visual function area of the brain. Previous studies utilizing resting-state functional magnetic resonance imaging (rs-fMRI) have reported abnormal brain activity in regions associated with vision in individuals with RD [6][7][8][9] . Nonetheless, it is still unknown whether there are speci c abnormalities in the dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) related to the primary visual cortex (V1) in individuals with RD.…”

Section: Introductionmentioning

confidence: 99%

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Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning

Ji,

Wang,

Cheng

et al. 2024

Preprint

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Background: Retinal detachment (RD) is a prevalent and severe eye disease that often leads to vision loss. Previous research has indicated abnormal brain activity in individuals with RD. However, these studies solely focused on localized alterations in brain activity among individuals with RD, and it remains unclear if there are any changes in dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) in the primary visual cortex (V1) among individuals with RD. Aim: This study utilizes seed-based functional connectivity (FC) analysis and Granger causality analysis (GCA) to examine alterations in dynamic functional and effective connectivity in the V1 among patients with RD. Methods: The study involved 29 patients with RD and 30 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans.Based on the seed regions in the V1, dynamic FC and GCA were conducted between the RD patients and HCs. To examine particular dFC and dEC states as well as associated temporal characteristics, the k-means clustering method was applied.The altered dFC and dEC values were selected as classification features and Support Vector Machine (SVM) classifiers were utilized to differentiate between patients with RD and HCs. Results: Compared to HCs, patients with RD displayed a significantly increased dFC between the right V1 and the temporal lobe, thalamus, frontal lobe, occipital lobe, angular gyrus, and cerebellum.Additionally, patients with RD exhibited significantly increased dFC between the left V1 and the parietal lobe.On the other hand, patients with RD showed a significantly decreased dFC between the left V1 and the cerebellum, amygdala, temporal lobe, and frontal lobe.Using the dynamic GCA algorithm, patients with RD showed a significant increase in dEC outflow from the right V1 to the frontal lobe, the caudate, the parietal lobule, and the angular gyrus.Patients with RD also exhibited a significant increase in dEC inflow to the right V1 from the temporal lobe, thalamus, the occipital lobe, and the parietal lobe.Additionally, patients with RD had significantly increased dEC outflow from the left V1 to the frontal lobe and the parietal lobe.Furthermore, patients with RD displayed a significant increase in dEC inflow to the left V1 from the occipital lobe.In contrast, patients with RD showed a significant decrease in dEC outflow from the left V1 to the occipital lobe. Lastly, patients with RD had significantly decreased dEC inflow to the left V1 from the occipital lobe and the postcentral gyrus[two-tailed, voxel-level p < 0.05, Gaussian random field (GRF) correction, cluster-level p < 0.05].After performing k-means clustering, it was observed that patients with RD predominantly displayed three dFC states and three or four dEC states.Depending on the region of interest (ROI), there are differences in the number of transitions(NT), frequency(F), and mean dwell time(MDT).The SVM model demonstrated accuracies of 0.712, 0.695, 0.525, 0.542, 0.593, and 0.458, along with corresponding areas under the curve (AUC) of 0.729, 0.786, 0.492, 0.561, 0.572, and 0, respectively, in distinguishing between individuals with RD and HCs based on the dFC/dEC values for the different ROI. Conclusion: Individuals with RD exhibited significant disruption in dFC/dEC between the V1 and multiple brain regions. The variability in dFC proved to distinguish individuals with RD from HCs with a high level of accuracy. These findings can contribute to the identification of potential neurological mechanisms underlying visual impairments in individuals with RD.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning

Ji,

Wang,

Cheng

et al. 2024

Preprint

Self Cite

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Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis

Chen,

Li,

Huang

2024

Brain Topogr

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Primary angle-closure glaucoma (PACG) is an irreversible blinding eye disease. Previous neuroimaging studies have reported abnormal brain function changes in PACG patients, but the potential association of PACG with changes in brain functional networks remains unknown. This study aims to investigate the impact of PACG on brain networks, with the goal of enhancing understanding of its neurobiological mechanisms for improved diagnosis and treatment through the application of graph theory analysis. MethodA cohort comprising 44 PACG patients and 44 healthy controls was recruited for the study. Functional brain networks were constructed based on fMRI data utilizing the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was conducted to assess global metrics, nodal metrics, modular organization, and network-based statistics(NBS), facilitating a comparative analysis between PACG patients and the control group. ResultThe global metrics( including small world and network e ciency) did not reveal signi cant differences between the two groups. However, PACG patients showed increased nodal metrics (including centrality and e ciency) in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, alongside decreased values in the right temporal superior gyrus region compared with health controls. Moreover, Module 5 demonstrated signi cant differences in intra-module connectivity, while signi cant variations in inter-module connectivity were observed between Module 1 and Module 7, as well as Module 1 and Module 8. Notably, the NBS analysis revealed a signi cantly modi ed network when comparing the PACG and HC groups. Conclusionthe study suggests that PACG patients exhibit differences in nodal metrics and modularity of functional brain networks, with changes observed mainly in prefrontal, occipital, and temporal lobes, as well as cerebellar regions. However, global metrics analysis indicates that the global connectivity patterns of the entire brain network remain unchanged in PACG patients. These ndings may serve as potential biomarkers for early diagnosis and differential diagnosis of PACG, and interventions targeting brain regions with high degree centrality and nodal e ciency may help to optimize therapeutic strategies.With the advancement of neuroimaging techniques, functional magnetic resonance imaging (fMRI) has emerged as a non-invasive method for measuring blood oxygen level-dependent (BOLD) signals in diverse brain regions, offering valuable insight into neural activity. ( 14). fMRI plays a pivotal role in investigating the neural mechanism alterations associated with glaucoma. Prior research has revealed varying degrees of changes in brain structure and function in glaucoma patients. For instance, a study by Jiang found differences in the whole brain grey matter volume of glaucoma patients, primarily concentrated in multiple brain regions relevant to visual motion function, the thalamocortical pathway, and emotional function. These regions incl...

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Efficient retinal detachment classification using hybrid machine learning with levy flight-based optimization

Anitha,

Aravindhar D

2024

Expert Systems with Applications

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Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment

Cited by 6 publications

References 51 publications

Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning

Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning

Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis

Efficient retinal detachment classification using hybrid machine learning with levy flight-based optimization

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