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

التفاصيل البيبلوغرافية
العنوان: Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment.
المؤلفون: Ji Y; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Wang YY; Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Cheng Q; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Fu WW; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Huang SQ; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Zhong PP; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Chen XL; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Shu BL; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Wei B; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Huang QY; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Wu XR; Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
المصدر: Frontiers in neuroscience [Front Neurosci] 2023 Jul 20; Vol. 17, pp. 1227081. Date of Electronic Publication: 2023 Jul 20 (Print Publication: 2023).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101478481 Publication Model: eCollection Cited Medium: Print ISSN: 1662-4548 (Print) Linking ISSN: 1662453X NLM ISO Abbreviation: Front Neurosci Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Lausanne : Frontiers Research Foundation
مستخلص: Background: There is increasing evidence that patients with retinal detachment (RD) have aberrant brain activity. However, neuroimaging investigations remain focused on static changes in brain activity among RD patients. There is limited knowledge regarding the characteristics of dynamic brain activity in RD patients.
Aim: This study evaluated changes in dynamic brain activity among RD patients, using a dynamic amplitude of low-frequency fluctuation (dALFF), k-means clustering method and support vector machine (SVM) classification approach.
Methods: We investigated inter-group disparities of dALFF indices under three different time window sizes using resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 RD patients and 24 demographically matched healthy controls (HCs). The k-means clustering method was performed to analyze specific dALFF states and related temporal properties. Additionally, we selected altered dALFF values under three distinct conditions as classification features for distinguishing RD patients from HCs using an SVM classifier.
Results: RD patients exhibited dynamic changes in local intrinsic indicators of brain activity. Compared with HCs, RD patients displayed increased dALFF in the bilateral middle frontal gyrus, left putamen (Putamen_L), left superior occipital gyrus (Occipital_Sup_L), left middle occipital gyrus (Occipital_Mid_L), right calcarine (Calcarine_R), right middle temporal gyrus (Temporal_Mid_R), and right inferior frontal gyrus (Frontal_Inf_Tri_R). Additionally, RD patients showed significantly decreased dALFF values in the right superior parietal gyrus (Parietal_Sup_R) and right paracentral lobule (Paracentral_Lobule_R) [two-tailed, voxel-level p < 0.05, Gaussian random field (GRF) correction, cluster-level p < 0.05]. For dALFF, we derived 3 or 4 states of ALFF that occurred repeatedly. There were differences in state distribution and state properties between RD and HC groups. The number of transitions between the dALFF states was higher in the RD group than in the HC group. Based on dALFF values in various brain regions, the overall accuracies of SVM classification were 97.87, 100, and 93.62% under three different time windows; area under the curve values were 0.99, 1.00, and 0.95, respectively. No correlation was found between hamilton anxiety (HAMA) scores and regional dALFF.
Conclusion: Our findings offer important insights concerning the neuropathology that underlies RD and provide robust evidence that dALFF, a local indicator of brain activity, may be useful for clinical diagnosis.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Ji, Wang, Cheng, Fu, Huang, Zhong, Chen, Shu, Wei, Huang and Wu.)
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فهرسة مساهمة: Keywords: brain region; dynamic amplitude of low-frequency fluctuation; k-means clustering method; resting-state functional magnetic resonance imaging; retinal detachment; sliding window; support vector machine
تواريخ الأحداث: Date Created: 20230807 Latest Revision: 20230808
رمز التحديث: 20230808
مُعرف محوري في PubMed: PMC10398337
DOI: 10.3389/fnins.2023.1227081
PMID: 37547140
قاعدة البيانات: MEDLINE
الوصف
تدمد:1662-4548
DOI:10.3389/fnins.2023.1227081